TensorSharp

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A C# inference engine for running large language models (LLMs) locally using GGUF model files. TensorSharp provides a console application, a web-based chatbot interface, and Ollama/OpenAI-compatible HTTP APIs for programmatic access.

Documentation Map

Start here	Use this when you want to...
Quick build and usage	Build the solution, compile the native GGML bridge, and run the CLI or server
Supported model architectures	Check which GGUF architecture keys, modalities, thinking mode, and tool calling paths are implemented
Compute backends	Choose between pure C# CPU, direct CUDA/cuBLAS, GGML CPU, GGML Metal, and GGML CUDA
HTTP APIs	Use the Ollama-compatible, OpenAI-compatible, or Web UI SSE endpoints
Per-model architecture cards	Read end-to-end documentation of one architecture (origin, forward graph, components, parameters, and how TensorSharp implements / optimizes prefill and decode)
Inference benchmark matrix	Compare TensorSharp against llama.cpp and Ollama across text / multimodal workloads, with KV-cache dtype sweeps
Server API examples	Copy complete curl and Python examples for the server surface
Server integration tests	Exercise the public API contract against a running server

Current Status

Area	Status
Model families	Gemma 3/4, Qwen 3, Qwen 3.5/3.6-family GGUFs (qwen35, qwen35moe, qwen3next), GPT OSS, Nemotron-H, and Mistral 3
Inference hosts	CLI, interactive REPL, ASP.NET Core web UI, Ollama-style API, OpenAI Chat Completions-style API
Backends	Pure C# CPU, direct CUDA/cuBLAS (cuda), GGML CPU, GGML Metal, GGML CUDA
Multimodal	Gemma 4 image/video/audio; Gemma 3, Qwen 3.5-family, Mistral 3, and Nemotron-H Omni image input
Server model scope	One explicitly hosted GGUF via --model; optional explicit projector via --mmproj; no directory scanning
Observability	Structured per-turn logs, queue status, and KV-cache reuse metrics across Web UI, Ollama, and OpenAI response shapes

Features

• Multi-architecture support -- Gemma 4, Gemma 3, Qwen 3, Qwen 3.5/3.6-family, GPT OSS, Nemotron-H, Mistral 3
• Multimodal inference -- image, video, and audio inputs (Gemma 4); images for Gemma 3 / Qwen 3.5-family / Mistral 3 / Nemotron-H Omni
• Thinking / reasoning mode -- structured chain-of-thought output with <think> / <|channel>thought / <|channel>analysis tags (Qwen 3, Qwen 3.5/3.6-family, Gemma 4, GPT OSS, Nemotron-H)
• Tool calling / function calling -- models can invoke user-defined tools; multi-turn tool-call conversations supported across all three API styles
• Quantized model support -- loads GGUF files with Q4_K_M, Q8_0, F16, MXFP4, and other quantization formats; performs native quantized matmul without dequantizing to FP32, including memory-efficient pure C# CPU loading for large GGUFs
• GPU-accelerated -- GGML Metal on macOS, GGML CUDA on Windows/Linux with NVIDIA GPUs, and a direct CUDA/cuBLAS backend with PTX kernels plus CPU fallbacks for unsupported ops
• Optimized pure C# CPU backend -- managed GEMM fast paths plus fused SIMD kernels for RMSNorm, RoPE, softmax, fused activations, and other inference hot paths
• Ollama & OpenAI API compatibility -- drop-in replacement endpoints for existing tooling
• Configurable sampling -- temperature, top-k, top-p, min-p, repetition/presence/frequency penalties, seed, stop sequences
• Chat templates -- auto-loaded from GGUF metadata (Jinja2), with hardcoded fallbacks per architecture
• Request queue -- FIFO inference queue ensures single-request execution for KV cache stability, with real-time position tracking for clients
• Batch processing -- JSONL input support in the console application, plus a built-in inference benchmark for prefill/decode throughput
• Streaming -- token-by-token output via SSE (web) or stdout (console), with abort/stop support for in-flight generations
• Hybrid SSM-Transformer -- Nemotron-H mixes Mamba2 SSM layers, attention-only layers, and MoE FFN layers in a single model
• Hybrid Attention-Recurrent -- Qwen 3.5/3.6-family models mix full-attention layers with GatedDeltaNet recurrent layers
• Mixture of Experts -- Gemma 4 MoE variants (e.g. gemma-4-26B-A4B), GPT OSS MoE (e.g. gpt-oss-20b), Qwen 3.5/3.6-family MoE (qwen35moe / qwen3next variants such as Qwen3.5-35B-A3B), and Nemotron-H MoE FFN layers
• Batched GPU MoE -- a single fused GGML graph dispatch handles all selected experts (plus the optional shared expert and residual add) for Qwen 3.5/3.6-family and Nemotron-H decode, eliminating per-expert round-trips
• Message editing -- edit or delete previous messages in the web chat UI and regenerate from that point
• Text/Image/Audio/Video uploads -- the web UI accepts file uploads up to 500 MB, with automatic token-budget-aware truncation for large text files
• Per-turn observability -- structured logs capture the full user input and the full raw assistant output (both <think> reasoning and the final result) plus the KV cache hit ratio. The same cache-hit stats are surfaced through every API: prompt_cache_hit_tokens / prompt_cache_hit_ratio (Ollama), usage.prompt_tokens_details.cached_tokens (OpenAI), and promptTokens / kvReusedTokens / kvReusePercent in the Web UI SSE done event

Supported Model Architectures

Architecture	GGUF arch keys	Example Models	Multimodal	Thinking	Tool Calling	Card
Gemma 4	gemma4	gemma-4-E4B, gemma-4-31B, gemma-4-26B-A4B (MoE)	Image, Video, Audio	Yes	Yes	gemma4.md
Gemma 3	gemma3	gemma-3-4b	Image	No	No	gemma3.md
Qwen 3	qwen3	Qwen3-4B	Text only	Yes	Yes	qwen3.md
Qwen 3.5 / 3.6 family	qwen35, qwen35moe, qwen3next	Qwen3.5-9B (hybrid Attn+Recurrent), Qwen3.5-35B-A3B / Qwen3.6-35B-A3B (MoE)	Image	Yes	Yes	qwen35.md
GPT OSS	gptoss, gpt-oss	gpt-oss-20b (MoE)	Text only	Yes (always)	No	gptoss.md
Nemotron-H	nemotron_h, nemotron_h_moe	Nemotron-H-8B, Nemotron-H-47B (Hybrid SSM-Transformer, MoE), Nemotron 3 Nano Omni (image)	Image (Omni)	Yes	Yes	nemotron.md
Mistral 3	mistral3	Mistral-Small-3.1-24B-Instruct	Image	No	No	mistral3.md

See the per-model architecture cards for end-to-end documentation of each architecture (origin, forward graph, components, parameters, weight naming, and how TensorSharp implements / optimizes prefill and decode).

Model Downloads (GGUF)

TensorSharp loads models in GGUF format. Below are Hugging Face links where you can download GGUF files for each supported architecture. Pick a quantization that fits your hardware (Q4_K_M for low memory, Q8_0 for higher quality, etc.).

Architecture	Model	GGUF Download
Gemma 4	gemma-4-E4B-it	ggml-org/gemma-4-E4B-it-GGUF
Gemma 4	gemma-4-31B-it	ggml-org/gemma-4-31B-it-GGUF
Gemma 4	gemma-4-26B-A4B-it (MoE)	ggml-org/gemma-4-26B-A4B-it-GGUF
Gemma 4	gemma-4-mmproj (multimodal projector)	Included in the GGUF repos above
Gemma 3	gemma-3-4b-it	google/gemma-3-4b-it-qat-q4_0-gguf
Qwen 3	Qwen3-4B	Qwen/Qwen3-4B-GGUF
Qwen 3.5 / 3.6 family	Qwen3.5-9B	unsloth/Qwen3.5-9B-GGUF
Qwen 3.5 / 3.6 family	Qwen3.5-35B-A3B	ggml-org/Qwen3.5-35B-A3B-GGUF
GPT OSS	gpt-oss-20b (MoE)	ggml-org/gpt-oss-20b-GGUF
Nemotron-H	Nemotron-H-8B-Reasoning-128K	bartowski/nvidia_Nemotron-H-8B-Reasoning-128K-GGUF
Nemotron-H	Nemotron-H-47B-Reasoning-128K	bartowski/nvidia_Nemotron-H-47B-Reasoning-128K-GGUF
Mistral 3	Mistral-Small-3.1-24B-Instruct	bartowski/Mistral-Small-3.1-24B-Instruct-2503-GGUF
Mistral 3	mistral3-mmproj (Pixtral vision projector)	bartowski/Mistral-Small-3.1-24B-Instruct-2503-GGUF

Compute Backends

Backend	Flag	Best fit	Description
Direct CUDA/cuBLAS	--backend cuda	NVIDIA experimentation and backend development	Uses the CUDA Driver API, cuBLAS GEMM, PTX kernels for common float32 ops, and native quantized matmul/get-rows for supported GGUF quant types. Unsupported ops route through CPU fallbacks while preserving tensor semantics.
GGML Metal	--backend ggml_metal	Apple Silicon	GPU-accelerated via Apple Metal. Quantized weights are mapped zero-copy from the GGUF file into Metal command buffers via host-pointer buffers, so the resident set stays close to the on-disk model size.
GGML CUDA	--backend ggml_cuda	NVIDIA inference through ggml	GPU-accelerated via GGML CUDA on Windows or Linux. Quantized weights are uploaded to device memory once at load time and the host copy is released afterwards.
GGML CPU	--backend ggml_cpu	Native CPU kernels	CPU inference using native GGML with optimized kernels. Quantized weights are mapped zero-copy from the GGUF file.
Pure C# CPU	--backend cpu	Portability and debugging	Portable CPU inference with no native dependencies.

Project Structure

TensorSharp/
├── TensorSharp.Core/            # Core tensor library (Tensor, Ops, memory, device abstraction)
├── TensorSharp.Runtime/         # GGUF, tokenizers, templates, sampling, protocol parsing
├── TensorSharp.Models/          # Model architectures and multimodal encoders/injectors
├── TensorSharp.Backends.GGML/   # GGML backend bindings (Metal/CUDA/CPU via native library)
├── TensorSharp.Backends.Cuda/   # Direct CUDA backend using CUDA Driver API, cuBLAS, and PTX kernels
├── TensorSharp.GGML.Native/     # Native C++ bridge to ggml (builds libGgmlOps, split into focused source files)
├── TensorSharp.Server/          # Web chatbot + API server (ASP.NET Core)
│   ├── Program.cs               # Slim bootstrap: DI wiring, middleware, endpoint mapping
│   ├── ModelService.cs          # Facade that keeps the public server inference API stable
│   ├── ModelLifecycleService.cs # Model load/dispose and backend selection
│   ├── SessionKvCacheManager.cs # Active session switching, KV reuse/truncate/reset, prefill chunking
│   ├── ChatGenerationPipeline.cs # Prompt rendering, prefill, decode loop, stop handling
│   ├── InferenceTelemetry.cs    # Prompt/eval timing, TTFT, tokens/sec, full input/output logs
│   ├── ChatHistoryPreparer.cs   # History normalization, raw-token splice helpers, multimodal order helpers
│   ├── ChatSession.cs           # Per-conversation KV cache + tracked history
│   ├── SessionManager.cs        # Thread-safe session registry (default + per-tab sessions)
│   ├── InferenceQueue.cs        # FIFO request queue with position tracking
│   ├── BackendCatalog.cs        # Discovery of available compute backends
│   ├── TextUploadHelper.cs      # Token-budget-aware text-file truncation
│   ├── WebUiChatPolicy.cs       # Web UI chat request validation
│   ├── OpenAIResponseFormatParser.cs  # OpenAI response_format (json_object / json_schema) parsing
│   ├── Hosting/                 # Startup-time concerns: options, backend resolution, logging, web root
│   ├── RequestParsers/          # JSON request parsing (sampling, chat messages, tool functions)
│   ├── ResponseSerializers/     # Per-protocol response shape factories (Ollama, OpenAI, Web UI)
│   ├── StreamingWriters/        # SSE + NDJSON wire-format helpers
│   ├── ProtocolAdapters/        # Per-protocol request handlers (WebUiAdapter, OllamaAdapter, OpenAIChatAdapter)
│   ├── Endpoints/               # ASP.NET Core endpoint mapping (one extension method per protocol)
│   ├── Logging/                 # Request logging middleware + low-noise path support
│   ├── wwwroot/index.html       # Chat UI
│   ├── testdata/                # Integration test suites (bash + Python)
│   └── API_EXAMPLES.md          # Detailed API documentation
├── TensorSharp.Cli/             # CLI application
├── InferenceWeb.Tests/          # xUnit unit tests covering ops, KV cache, web/server helpers
├── AdvUtils/                    # Utility library
├── docs/                        # Developer reference
│   ├── models/                  # Per-model architecture cards (one .md per model, EN + 中文)
│   └── inference_benchmark_matrix.md  # Cross-engine throughput matrix (TensorSharp vs llama.cpp vs Ollama)
├── benchmarks/                  # Reproducible benchmark harnesses
│   └── inference_matrix/        # Driver scripts, modelfiles, prompts, and per-cell raw JSON results
└── ExternalProjects/            # Third-party dependencies (ggml)

NuGet Packages

The repository is now split along package boundaries so consumers can depend on only the layers they actually need.

Project	NuGet package	Public namespace	Responsibility
TensorSharp.Core	TensorSharp.Core	TensorSharp	Tensor primitives, ops, allocators, storage, and device abstraction
TensorSharp.Runtime	TensorSharp.Runtime	TensorSharp.Runtime	GGUF parsing, tokenizers, prompt rendering, sampling, and output protocol parsing
TensorSharp.Models	TensorSharp.Models	TensorSharp.Models	ModelBase, architecture implementations, multimodal encoders, and model-side execution helpers
TensorSharp.Backends.GGML	TensorSharp.Backends.GGML	TensorSharp.GGML	GGML-backed execution and native interop
TensorSharp.Backends.Cuda	TensorSharp.Backends.Cuda	TensorSharp.Cuda	Direct CUDA allocator, storage, cuBLAS GEMM, PTX kernels, and quantized CUDA ops
TensorSharp.Server	TensorSharp.Server	TensorSharp.Server	ASP.NET Core server, OpenAI/Ollama adapters, queueing, and web UI
TensorSharp.Cli	TensorSharp.Cli	TensorSharp.Cli	Console host and debugging / batch tooling

This split keeps engine users off the web stack, keeps API-layer changes from leaking into core/runtime packages, and makes future benchmark or eval-harness projects easier to publish independently.

Validate package metadata and README dependency boundaries before publishing:

pwsh ./eng/verify-packages.ps1

The verifier runs dotnet pack for the seven public packages above and fails if an internal dependency such as AdvUtils leaks into the .nuspec, or if a TensorSharp package depends on a layer outside this table.

Prerequisites

• .NET 10 SDK
• macOS (Metal backend): CMake 3.20+ and Xcode command-line tools for building the native GGML library
• Windows (GGML CPU / CUDA backends): CMake 3.20+ and Visual Studio 2022 C++ build tools; for ggml_cuda or cuda, install an NVIDIA driver plus CUDA Toolkit 12.x or another compatible CUDA toolkit with cuBLAS
• Linux (GGML CPU / CUDA backends): CMake 3.20+; for ggml_cuda or cuda, install an NVIDIA driver plus CUDA Toolkit 12.x or another compatible CUDA toolkit with cuBLAS
• GGUF model files (e.g., from Hugging Face)

Building

Build the entire solution

dotnet build TensorSharp.slnx

Build individual applications

# Console application
dotnet build TensorSharp.Cli/TensorSharp.Cli.csproj

# Web application
dotnet build TensorSharp.Server/TensorSharp.Server.csproj

Build the native GGML library

The native library is built automatically during the first dotnet build if it doesn't exist. To build it manually:

cd TensorSharp.GGML.Native

macOS:

bash build-macos.sh

Linux (CPU-only):

bash build-linux.sh

Linux (GGML_CUDA enabled):

bash build-linux.sh --cuda

Windows (CPU-only):

.\build-windows.ps1 --no-cuda

Windows (GGML_CUDA enabled):

.\build-windows.ps1 --cuda

On Windows and Linux, the native build script auto-detects the visible NVIDIA GPU compute capability and passes a narrow CMAKE_CUDA_ARCHITECTURES value to ggml-cuda (for example 86-real on an RTX 3080), which cuts CUDA build time substantially. The native build also runs in parallel by default with a conservative job cap so nvcc does not overwhelm typical developer machines.

If you want to override the auto-detected architecture list or the default build parallelism, use either environment variables or explicit build flags:

TENSORSHARP_GGML_NATIVE_CUDA_ARCHITECTURES='86-real;89-real' bash build-linux.sh --cuda
bash build-linux.sh --cuda --cuda-arch='86-real;89-real'
TENSORSHARP_GGML_NATIVE_BUILD_PARALLEL_LEVEL=2 bash build-linux.sh --cuda

$env:TENSORSHARP_GGML_NATIVE_CUDA_ARCHITECTURES='86-real;89-real'; .\build-windows.ps1 --cuda
.\build-windows.ps1 --cuda --cuda-arch='86-real;89-real'
$env:TENSORSHARP_GGML_NATIVE_BUILD_PARALLEL_LEVEL=2; .\build-windows.ps1 --cuda

You can also request a CUDA-enabled native build from dotnet build:

TENSORSHARP_GGML_NATIVE_ENABLE_CUDA=ON dotnet build TensorSharp.Cli/TensorSharp.Cli.csproj -c Release

$env:TENSORSHARP_GGML_NATIVE_ENABLE_CUDA='ON'; dotnet build TensorSharp.Cli/TensorSharp.Cli.csproj -c Release

On macOS this compiles libGgmlOps.dylib with Metal GPU support. On Windows and Linux, the native scripts preserve an existing CUDA-enabled build and auto-enable GGML_CUDA when a CUDA toolchain is detected; build-windows.ps1 --cuda, build-linux.sh --cuda, and TENSORSHARP_GGML_NATIVE_ENABLE_CUDA=ON force CUDA explicitly. The build output is automatically copied to the application's output directory.

The direct cuda backend is built as managed C# plus PTX kernels. During dotnet build, TensorSharp.Backends.Cuda compiles native/kernels/*.cu to native/ptx/*.ptx when nvcc is available; if nvcc is missing, the build continues and PTX-backed ops use CPU fallbacks. cuBLAS-backed GEMM still requires the CUDA runtime libraries to be discoverable at run time.

Usage

Console Application

cd TensorSharp.Cli/bin

# Text inference
./TensorSharp.Cli --model <model.gguf> --input prompt.txt --output result.txt \
    --max-tokens 200 --backend ggml_metal

# Text inference on Windows/Linux + NVIDIA GPU
./TensorSharp.Cli --model <model.gguf> --input prompt.txt --output result.txt \
    --max-tokens 200 --backend ggml_cuda

# Interactive turn-by-turn chat (REPL) with KV cache reuse and slash commands
./TensorSharp.Cli --model <model.gguf> --backend ggml_metal --interactive
./TensorSharp.Cli --model <model.gguf> --backend ggml_metal -i \
    --system "You are a terse assistant." --temperature 0.7 --top-p 0.9 --think

# Image inference (Gemma 3/4, Qwen 3.5-family)
./TensorSharp.Cli --model <model.gguf> --image photo.png --backend ggml_metal

# Video inference (Gemma 4)
./TensorSharp.Cli --model <model.gguf> --video clip.mp4 --backend ggml_metal

# Audio inference (Gemma 4)
./TensorSharp.Cli --model <model.gguf> --audio speech.wav --backend ggml_metal

# Thinking / reasoning mode
./TensorSharp.Cli --model <model.gguf> --input prompt.txt --backend ggml_metal --think

# Tool calling
./TensorSharp.Cli --model <model.gguf> --input prompt.txt --backend ggml_metal \
    --tools tools.json

# With sampling parameters
./TensorSharp.Cli --model <model.gguf> --input prompt.txt --backend ggml_metal \
    --temperature 0.7 --top-p 0.9 --top-k 40 --repeat-penalty 1.2 --seed 42

# Batch processing (JSONL)
./TensorSharp.Cli --model <model.gguf> --input-jsonl requests.jsonl \
    --output results.txt --backend ggml_metal

# Multi-turn chat simulation with KV-cache reuse (mirrors the web UI behavior)
./TensorSharp.Cli --model <model.gguf> --multi-turn-jsonl chat.jsonl \
    --backend ggml_metal --max-tokens 200

# Throughput benchmark: best-of-N prefill and decode timing
./TensorSharp.Cli --model <model.gguf> --backend ggml_metal \
    --benchmark --bench-prefill 256 --bench-decode 128 --bench-runs 3

# KV-cache reuse benchmark: measure prefill speedup across multiple chat turns
# (compares with-cache vs forced-reset prefill latency for an 8-turn conversation)
./TensorSharp.Cli --model <model.gguf> --backend ggml_metal \
    --bench-kvcache --bench-kv-turns 4 --max-tokens 64

# Inspect the rendered prompt and tokenization without running inference
./TensorSharp.Cli --model <model.gguf> --input prompt.txt --dump-prompt

# Compare hardcoded fallback templates against GGUF Jinja2 templates for every
# *.gguf file in a directory (useful when adding new architectures)
./TensorSharp.Cli --test-templates ~/models

Command-line options:

Option	Description
--model <path>	Path to a GGUF model file (required)
--input <path>	Text file containing the user prompt
--input-jsonl <path>	JSONL file with batch requests (one JSON per line)
--multi-turn-jsonl <path>	JSONL file for multi-turn chat simulation with KV cache reuse
--output <path>	Write generated text to this file
--image <path>	Image file for vision inference
--video <path>	Video file for video inference
--audio <path>	Audio file (WAV, MP3, OGG) for audio inference
--mmproj <path>	Path to the multimodal projector GGUF file
--max-tokens <N>	Maximum tokens to generate (default: 100)
--backend <type>	Compute backend: cpu, cuda, ggml_cpu, ggml_metal, or ggml_cuda
--kv-cache-dtype <type>	KV cache precision: f32 (default), f16, or q8_0. Quantized / half-precision KV caches reduce memory at the cost of small numerical drift; benchmarks live in docs/inference_benchmark_matrix.md.
--interactive / -i	Start an interactive REPL chat session (turn-by-turn input/output) with KV cache reuse, slash commands, hot-swappable model/backend/projector, file attachments (image, audio, video, text) and live sampling tuning. See the Interactive REPL commands section below for the full list.
--system <text>	System prompt to seed the interactive session (overridden inside the REPL by /system)
--system-file <path>	Read the initial system prompt from a UTF-8 text file (alternative to --system)
--think	Enable thinking/reasoning mode (chain-of-thought)
--tools <path>	JSON file with tool/function definitions
--temperature <f>	Sampling temperature (0 = greedy)
--top-k <N>	Top-K filtering (0 = disabled)
--top-p <f>	Nucleus sampling threshold (1.0 = disabled)
--min-p <f>	Minimum probability filtering (0 = disabled)
--repeat-penalty <f>	Repetition penalty (1.0 = none)
--presence-penalty <f>	Presence penalty (0 = disabled)
--frequency-penalty <f>	Frequency penalty (0 = disabled)
--seed <N>	Random seed (-1 = non-deterministic)
--stop <string>	Stop sequence (can be repeated)
--dump-prompt	Render the prompt + tokenization and exit (no generation)
--benchmark	Run a synthetic prefill/decode throughput benchmark
--bench-prefill <N>	Synthetic prefill length in tokens (default: 32)
--bench-decode <N>	Synthetic decode length in tokens (default: 64)
--bench-runs <N>	Number of benchmark runs; reports best and average (default: 1)
--bench-kvcache	Run a multi-turn KV-cache reuse benchmark (with-cache vs forced-reset prefill)
--bench-kv-turns <N>	Number of conversation turns for --bench-kvcache (default: 4, max: 8)
--bench-chunked	Run a chunked-prefill micro-benchmark (Gemma 4)
--warmup-runs <N>	Number of throw-away forward passes before timing real text / multimodal prompts (default: 0)
--test-chunked-prefill	Run the chunked-prefill correctness check (compares chunked vs non-chunked logits)
--correct-prefill <N>	Prompt length used by --test-chunked-prefill
--correct-decode <N>	Decode length used by --test-chunked-prefill
--test	Run built-in tokenizer + Qwen3 chat-template + ollama-comparison tests
--test-templates <dir>	Validate hardcoded chat templates against GGUF Jinja2 templates for every *.gguf in <dir>
--log-level <lvl>	Console + file logger level: trace, debug, info, warning, error, critical, off
--log-dir <path>	Directory for the JSON-line file logger (default: <binDir>/logs)
--log-file <0|1>	Disable (0) or enable (1) the file logger (default: enabled)
--log-console <0|1>	Disable (0) or enable (1) the console logger (default: enabled)

The multimodal projector file is auto-detected if placed alongside the model file with a recognized name (e.g., gemma-4-mmproj-F16.gguf).

JSONL input format:

Each line is a JSON object with messages, optional prompt, and optional sampling parameters:

{"id": "q1", "messages": [{"role": "user", "content": "What is 2+3?"}], "max_tokens": 50}
{"id": "q2", "messages": [{"role": "user", "content": "Write a haiku."}], "max_tokens": 100, "temperature": 0.8}

Interactive REPL commands:

Once the CLI is launched with --interactive / -i, you can drive the running session with slash commands. Type /help (or /?) inside the REPL for the same list. Anything that does not start with / is treated as a user turn.

The prompt header summarizes the current state on every turn — model, backend, architecture, context length, projector, conversation depth, and any attachments queued for the next turn (e.g. [turn 3 (2 attachments pending)]> ). Press Ctrl+C while generating to interrupt the current reply; press Ctrl+C at the prompt to exit.

Conversation:

Command	Description
/help, /?	Show all interactive commands
/exit, /quit	Leave the session
/reset, /new	Clear conversation history and KV cache
/history	Print the conversation history
/save <file>	Append the current transcript to a UTF-8 file
/system <text>	Set the system prompt (empty argument clears it). Resets KV cache.
/think on|off	Toggle thinking/reasoning mode for supported models
/multiline on|off	Toggle multi-line input (terminate the message with a single . on its own line)

Model and runtime:

Command	Description
/info, /status	Show the loaded model, backend, architecture, context/vocab size, projector, conversation depth, and pending attachments
/model <path>	Load a different .gguf model on the current backend (resets the session)
/backend <name>	Reload the current model on a different backend: cpu, cuda, ggml_cpu, ggml_metal, or ggml_cuda
/mmproj <path>	Load (or replace) the multimodal projector for the current model. Aliases: /projector

Sampling (live, persists across turns):

Command	Description
/sampling, /show	Print the current sampling configuration
/max <N>	Maximum reply length in tokens
/temp <float>	Sampling temperature (0 = greedy)
/topk <int>	Top-K filtering (0 = disabled)
/topp <float>	Top-P / nucleus threshold (1.0 = disabled)
/minp <float>	Min-P filtering (0 = disabled)
/repeat <float>	Repetition penalty (1.0 = none)
/presence <float>	Presence penalty
/frequency <float>	Frequency penalty
/seed <int>	Random seed (-1 = non-deterministic)
/stop <text>	Add a stop sequence
/clearstop	Remove all stop sequences

Uploads (queued for the next user turn, then auto-cleared after the turn):

Command	Description
/image <path>, /img <path>	Attach an image (vision-capable models only)
/audio <path>	Attach an audio file (Gemma 4)
/video <path>, /vid <path>	Attach a video; frames are extracted automatically (Gemma 4)
/text <path>, /file <path>, /txt <path>	Inline a UTF-8 text/markdown/csv/code file into the next prompt (large files are token-budget truncated)
/clearattach	Drop any pending image/audio/video/text attachments without sending a turn

Quoted paths (single or double quotes) are accepted, so drag-and-drop from a file manager works on macOS. Multimodal commands require a multimodal projector to be loaded — pass --mmproj at startup or use /mmproj <path> from the REPL.

Web Application

cd TensorSharp.Server/bin

# Start the server with the exact hosted model
./TensorSharp.Server --model ./models/model.gguf --backend ggml_metal

# Linux + NVIDIA GPU
./TensorSharp.Server --model ./models/model.gguf --backend ggml_cuda

# Multimodal models: host an explicit projector too
./TensorSharp.Server --model ./models/model.gguf --mmproj ./models/mmproj.gguf --backend ggml_cuda

# Configure server-wide default sampling parameters
# (used whenever a request does not override the value itself)
./TensorSharp.Server --model ./models/model.gguf --backend ggml_metal \
    --temperature 0.7 --top-p 0.9 --top-k 40 --repeat-penalty 1.1 \
    --presence-penalty 0.0 --frequency-penalty 0.0 --seed 42 \
    --stop "</s>" --stop "<|endoftext|>"

Open http://localhost:5000 in your browser. The web interface supports:

• Multi-turn chat conversations
• Per-tab chat sessions: each browser tab owns its own KV cache; clicking "New Chat" disposes the current session server-side so its cache is released
• A single hosted GGUF selected explicitly with --model
• An explicit hosted multimodal projector via --mmproj when needed
• Image, video, and audio uploads for multimodal inference (up to 500 MB)
• Thinking/reasoning mode toggle
• Tool calling with function definitions
• Streaming token generation via Server-Sent Events
• Request queue with real-time position feedback
• Message editing and deletion with regeneration from any point in the conversation
• Free scrolling: scroll up to read earlier replies while new tokens stream in; the chat auto-scrolls again as soon as the user scrolls back to the bottom

Use --model to choose the hosted GGUF file and --mmproj to choose the hosted projector. TensorSharp.Server no longer scans a MODEL_DIR.

Server command-line options:

Option	Description
--model <path>	GGUF file to host (required for inference; if omitted, the server starts but /api/models/load will report no hosted model)
--mmproj <path>	Multimodal projector GGUF (resolved relative to the model directory when only a filename is given; pass none to disable). Requires --model.
--backend <type>	Default compute backend: cpu, cuda, ggml_cpu, ggml_metal, or ggml_cuda
--max-tokens <N>	Default maximum tokens to generate when a request omits the limit (default: 20000)
--temperature <f>	Default sampling temperature when a request does not provide one (0 = greedy)
--top-k <N>	Default top-K filtering when a request does not provide one (0 = disabled)
--top-p <f>	Default nucleus sampling threshold when a request does not provide one (1.0 = disabled)
--min-p <f>	Default min-p filtering when a request does not provide one (0 = disabled)
--repeat-penalty <f>	Default repetition penalty when a request does not provide one (1.0 = none)
--presence-penalty <f>	Default presence penalty when a request does not provide one (0 = disabled)
--frequency-penalty <f>	Default frequency penalty when a request does not provide one (0 = disabled)
--seed <N>	Default random seed when a request does not provide one (-1 = non-deterministic)
--stop <string>	Default stop sequence (can be repeated). Per-request stop/stop_sequences fully replace the default list rather than merge with it.

Per-request fields in the chat / generate JSON payloads (e.g. temperature, top_p, top_k, min_p, repeat_penalty, presence_penalty, frequency_penalty, seed, stop/stop_sequences) always win over these server-wide defaults; the defaults only fill in fields the client omits.

Runtime environment variables:

Variable	Description
BACKEND	Default compute backend (cpu, cuda, ggml_cpu, ggml_metal, or ggml_cuda), used when --backend is not passed (default: ggml_metal on macOS, ggml_cpu elsewhere)
MAX_TOKENS	Default maximum generation length when neither --max-tokens nor a request-level limit is set (default: 20000)
MAX_TEXT_FILE_CHARS	Character cap used to truncate plain-text uploads when no tokenizer is available (default: 8000)
VIDEO_MAX_FRAMES	Maximum evenly spaced video frames extracted for video prompts (default: 4)
PORT / ASPNETCORE_URLS	Standard ASP.NET Core listener configuration (default port: 5000)
TENSORSHARP_TEMPERATURE	Default sampling temperature when neither --temperature nor the request body sets one
TENSORSHARP_TOP_K	Default top-K when neither --top-k nor the request body sets one
TENSORSHARP_TOP_P	Default top-P when neither --top-p nor the request body sets one
TENSORSHARP_MIN_P	Default min-P when neither --min-p nor the request body sets one
TENSORSHARP_REPEAT_PENALTY	Default repetition penalty when neither --repeat-penalty nor the request body sets one
TENSORSHARP_PRESENCE_PENALTY	Default presence penalty when neither --presence-penalty nor the request body sets one
TENSORSHARP_FREQUENCY_PENALTY	Default frequency penalty when neither --frequency-penalty nor the request body sets one
TENSORSHARP_SEED	Default random seed when neither --seed nor the request body sets one
TENSORSHARP_LOG_LEVEL	Minimum log level for both console and file loggers: Trace, Debug, Information, Warning, Error, Critical (default: Information). Also honored by TensorSharp.Cli.
TENSORSHARP_LOG_DIR	Directory the JSON-line file logger writes to (default: <binDir>/logs). Also honored by TensorSharp.Cli.
TENSORSHARP_LOG_FILE	Set to 0 to disable the file logger and keep only the console output (default: enabled). Also honored by TensorSharp.Cli.

Sampling parameter precedence (highest wins):

1. Per-request JSON fields in the API call (e.g. temperature, top_p, stop).
2. Server-wide CLI flags (e.g. --temperature, --top-p, --stop).
3. TENSORSHARP_* environment variables listed above.
4. Built-in SamplingConfig defaults (temperature=1.0, top_k=0, top_p=1.0, min_p=0, repeat_penalty=1.0, presence/frequency penalties 0, seed=-1, no stop sequences).

Server Logging

The server emits one structured Information-level entry at the start and end of every chat / generate turn, so a single grep over the log file reproduces the full request-response audit trail without replaying any traffic.

Event id	Emitted on	Carries
ChatStarted (1500)	chat.start, generate.start, plus per-protocol request banners	sampling config, message + attachment counts, userInput= (full latest user message), fullInput= (JSON-encoded array of EVERY message in the request: system prompts + all prior user/assistant turns + the new user message, with attachment counts), or the full prompt for /api/generate
ChatCompleted (1502)	chat.complete, generate.complete	token counts, KV cache reuse (kvReused, kvReusePercent), TTFT, elapsed, throughput, finish reason, full raw assistant output (reasoning + result)
ChatAborted (1503)	client disconnected mid-stream	partial output, KV reuse fraction at the time of abort
KvCacheReusePlan (1510)	per-prefix-reuse decision	Debug-level fine-grained breakdown (exact match / partial / full reset)
HttpRequestStarted/Completed (1100/1101)	every HTTP request	method, path, remote IP, status, duration; /api/queue/status is demoted to Debug so high-frequency UI polling does not drown out the per-turn entries

The raw assistant output captures <think>...</think>, <|channel|>analysis, and any other inline framing the model emits, so the log line for a single turn contains both reasoning and the user-visible result. Combined with the fullInput= field on chat.start, every turn is fully reproducible from the log file alone (request inputs + raw model output). Long uploads or long reasoning traces can produce multi-kilobyte log lines; raise the log level (TENSORSHARP_LOG_LEVEL=Warning) to suppress them while still keeping the start banner and error logs.

Sample fullInput payload (formatted for readability; it is emitted as a single line in the actual log):

[
  {"role":"system","content":"You are a helpful assistant."},
  {"role":"user","content":"What is the tallest mountain?"},
  {"role":"assistant","content":"Mount Everest."},
  {"role":"user","content":"How tall is it?","images":1}
]

The same per-turn KV cache reuse stats are surfaced through every API:

• Web UI SSE (POST /api/chat) - the done event carries promptTokens, kvReusedTokens, and kvReusePercent.
• Ollama NDJSON (POST /api/generate, POST /api/chat/ollama) - the final chunk and the non-streaming response carry prompt_cache_hit_tokens (int) and prompt_cache_hit_ratio (0..1).
• OpenAI (POST /v1/chat/completions) - the usage block carries prompt_tokens_details.cached_tokens, matching the OpenAI extension that existing SDKs already understand.

The Web UI footer line under each assistant message also surfaces the cache hit inline (e.g. 187 tokens · 2.1s · 87.2 tok/s · KV 420/512 (82%)).

HTTP APIs

TensorSharp.Server exposes three API styles. See API_EXAMPLES.md for full documentation with curl and Python examples.

Ollama-compatible API:

# List models
curl http://localhost:5000/api/tags

# Generate text
curl -X POST http://localhost:5000/api/generate \
  -H "Content-Type: application/json" \
  -d '{"model": "Qwen3-4B-Q8_0.gguf", "prompt": "Hello!", "stream": false}'

# Chat
curl -X POST http://localhost:5000/api/chat/ollama \
  -H "Content-Type: application/json" \
  -d '{"model": "Qwen3-4B-Q8_0.gguf", "messages": [{"role": "user", "content": "Hi"}], "stream": false}'

# Chat with thinking mode
curl -X POST http://localhost:5000/api/chat/ollama \
  -H "Content-Type: application/json" \
  -d '{"model": "Qwen3-4B-Q8_0.gguf", "messages": [{"role": "user", "content": "Solve 17*23"}], "think": true, "stream": false}'

# Chat with tool calling
curl -X POST http://localhost:5000/api/chat/ollama \
  -H "Content-Type: application/json" \
  -d '{"model": "Qwen3-4B-Q8_0.gguf", "messages": [{"role": "user", "content": "What is the weather?"}], "tools": [{"function": {"name": "get_weather", "description": "Get current weather", "parameters": {"properties": {"city": {"type": "string"}}, "required": ["city"]}}}], "stream": false}'

OpenAI-compatible API:

# Chat completions
curl -X POST http://localhost:5000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{"model": "Qwen3-4B-Q8_0.gguf", "messages": [{"role": "user", "content": "Hi"}], "max_tokens": 50}'

# Structured outputs (OpenAI response_format)
curl -X POST http://localhost:5000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Qwen3-4B-Q8_0.gguf",
    "messages": [{"role": "user", "content": "Extract the city and country from: Paris, France."}],
    "response_format": {
      "type": "json_schema",
      "json_schema": {
        "name": "location_extraction",
        "strict": true,
        "schema": {
          "type": "object",
          "properties": {
            "city": {"type": "string"},
            "country": {"type": "string"},
            "confidence": {"type": ["string", "null"]}
          },
          "required": ["city", "country", "confidence"],
          "additionalProperties": false
        }
      }
    }
  }'

OpenAI Python SDK:

from openai import OpenAI

client = OpenAI(base_url="http://localhost:5000/v1", api_key="not-needed")
response = client.chat.completions.create(
    model="Qwen3-4B-Q8_0.gguf",
    messages=[{"role": "user", "content": "What is 2+3?"}],
    max_tokens=50
)
print(response.choices[0].message.content)

Queue status:

curl http://localhost:5000/api/queue/status
# {"busy":false,"pending_requests":0,"total_processed":42}

Thinking / Reasoning Mode

Models that support thinking mode (Qwen 3, Qwen 3.5/3.6-family, Gemma 4, GPT OSS, Nemotron-H) can produce structured chain-of-thought reasoning before generating the final answer. The thinking content is separated from the main response and can be displayed or hidden by the client.

• Qwen 3 / Qwen 3.5/3.6-family / Nemotron-H: uses <think>...</think> tags
• Gemma 4: uses <|channel>thought\n...<channel|> tags
• GPT OSS: uses Harmony format with <|channel|>analysis for thinking and <|channel|>final for the response

Enable via --think (console), "think": true (Ollama API), or the thinking toggle in the web UI.

Tool Calling / Function Calling

Models can invoke user-defined tools and participate in multi-turn tool-call conversations. Define tools as JSON and pass them via --tools (console) or the tools parameter in the API.

Each architecture uses its own wire format for tool calls:

• Qwen 3 / Qwen 3.5/3.6-family / Nemotron-H: <tool_call>{"name": "...", "arguments": {...}}</tool_call>
• Gemma 4: <|tool_call>call:function_name{args}<tool_call|>

The output parser (OutputParser.cs) automatically extracts tool calls from the model's raw output regardless of architecture.

Multimodal Support

Gemma 4

Gemma 4 models support image, video, and audio inputs. Place the multimodal projector (gemma-4-mmproj-F16.gguf) in the same directory as the model file for automatic loading.

• Images: PNG, JPEG, HEIC/HEIF
• Video: MP4 (extracts up to 8 frames at 1 fps using OpenCV)
• Audio: WAV (16kHz mono), MP3, OGG Vorbis

Gemma 3

Gemma 3 supports PNG, JPEG, and HEIC/HEIF image inputs. Place its multimodal projector (mmproj-gemma3-4b-f16.gguf) next to the model file for automatic loading.

Qwen 3.5 / 3.6 family

All Qwen 3.5/3.6-family variants (qwen35, qwen35moe, and qwen3next) load through the same Qwen35Model implementation. Image inputs are supported via the dynamic-resolution Qwen35VisionEncoder; place the projector (Qwen3.5-mmproj-F16.gguf) next to the model GGUF for automatic loading. The MoE variants (e.g. Qwen3.5-35B-A3B and Qwen3.6-35B-A3B GGUFs that report the same architecture keys) additionally enable a fused MoEExpertsSwiGLUResidual GGML kernel during decode that runs all selected experts, the optional shared expert, and the residual add in a single GPU graph dispatch.

Mistral 3

Mistral 3 supports image inputs via the Pixtral vision encoder. Place the multimodal projector (mistral3-mmproj.gguf) in the same directory as the model file for automatic loading.

• Images: PNG, JPEG, HEIC/HEIF

Nemotron-H (Omni distribution)

The Nemotron Omni distribution adds a RADIO / v2_vl ViT image encoder. Pass the matching multimodal projector with --mmproj (e.g. nvidia_Nemotron-H-Omni-mmproj.gguf); the language-model GGUF stays the same. Image tokens are inserted at <image> placeholders and expanded into <img> + N tile tokens + </img> automatically by the multimodal injector.

• Images: PNG, JPEG, HEIC/HEIF
• Audio: the chat template emits <so_embedding> per uploaded audio file and the CLI runs the Parakeet-style log-mel preprocessor for verification, but actual audio inference requires a Parakeet audio mmproj that the public GGUFs do not currently ship.

Architecture

TensorSharp is structured as a layered system:

1. TensorSharp.Core provides the core Tensor type, storage abstraction, and the extensible operation registry (Ops). CPU implementations use System.Numerics.Vectors for SIMD acceleration.

2. TensorSharp.Runtime owns runtime-facing contracts and services: GGUF parsing, tokenization (SentencePiece / BPE), chat template rendering, configurable token sampling, output parsing, and reusable contracts such as IModelArchitecture, IPromptRenderer, IOutputProtocolParser, IMultimodalInjector, IKVCachePolicy, and IBackendExecutionPlan.

3. TensorSharp.Models implements ModelBase plus the concrete architectures and multimodal helpers (Gemma 3/4, Qwen 3/3.5, GPT OSS, Nemotron-H, Mistral 3). Models are loaded via ModelBase.Create() which auto-detects the architecture from GGUF metadata.

4. TensorSharp.Backends.GGML registers accelerated implementations of the same operations via a native C++ bridge (libGgmlOps / GgmlOps.dll) that links against ggml. On macOS this provides Metal GPU compute, and on Windows/Linux it can expose GGML CUDA for NVIDIA GPUs. Operations include native quantized matmul (Q4_K_M, Q8_0, etc.) without dequantizing to FP32.

5. TensorSharp.Backends.Cuda is the direct CUDA path. It uses the CUDA Driver API for device/context/storage management, cuBLAS for float32 GEMM, PTX kernels for hot scalar and transformer helper ops, and CPU fallbacks where native kernels are not implemented yet.

6. TensorSharp.Server is the HTTP/application layer. It provides Ollama-compatible and OpenAI-compatible REST APIs, the browser-based chat UI, upload handling, and the FIFO inference queue.

7. TensorSharp.Cli is the console/application layer for local prompts, multimodal experiments, prompt inspection, and JSONL batch workflows.

Performance Optimizations

The list below is the cross-architecture summary; each per-model card under docs/models/ walks through the same kernels in context, with the exact GGML graph dispatched and the conditions under which the fused path engages.

• Fused GPU decode (Gemma 4): all transformer layers are executed in a single GGML compute graph dispatch on Metal, reducing CPU-GPU round-trips from hundreds per token to one. This achieves ~2.6x speedup over per-operation dispatch.
• Fused GPU prefill (Gemma 4): for dense (non-MoE, non-shared, non-PLE/multimodal) layers, Gemma4LayerPrefill runs the entire transformer block (RMSNorm + QKV + QK-norm + RoPE + attention + output projection + post-attn norm + GeGLU FFN + post-FFN norm + residual + layer scalar) as a single GGML graph dispatch per layer during prefill, extending the fused approach from decode to multi-token prefill.
• Chunked prefill (Gemma 4): long prompts are split into bounded chunks (2x sliding window, max 2048 tokens) to avoid O(n^2) attention score tensors for SWA layers. Chunking is applied automatically when text-only (no multimodal embeddings) and keeps each chunk within the SWA window budget.
• Native whole-model decode (Qwen 3): all transformer layers run in one native call (TransformerModelDecode) with pre-resolved per-layer weight pointers cached at load time, removing managed-loop overhead from the decode hot path.
• Fused Qwen 3.5/3.6-family attention layer decode: a single GGML graph performs RMSNorm + fused QKV + Q/gate deinterleave + per-head QK norm + RoPE + KV cache append + flash attention + sigmoid-gated mix + output projection + residual add for each FullAttention layer. Replaces ~2 standalone GGML calls and ~6 small CPU/GPU sync points per attention layer. Engages once the cached sequence length exceeds 4096 tokens (override with FUSED_ATTN_LAYER_MIN_SEQ_LEN=N).
• Fused prefill attention (Qwen 3.5/3.6-family): FusedPrefillAttention combines Q*K^T, causal mask, softmax, and *V into a single GGML graph dispatch during multi-token prefill, eliminating ~5 separate C#-to-GGML round-trips per attention layer. Handles both initial prefill and continuation with existing KV cache entries.
• Fused output-projection + FFN (Qwen 3.5/3.6-family): for both FullAttention and GatedDeltaNet layers with dense FFN, FusedOutProjFFN merges the output projection, residual add, post-attention RMSNorm, and the full SwiGLU FFN (gate_up matmul + SiLU + down matmul + residual) into a single GGML graph dispatch, reducing two GPU round-trips to one per layer.
• Fused output-projection + norm + router (Qwen 3.5/3.6-family MoE): FusedOutProjNormRouter merges the GatedDeltaNet output projection, residual add, post-attention RMSNorm, and MoE router projection into one dispatch. The pre-computed router logits are then consumed directly by the batched MoE kernel, eliminating a separate router dispatch per MoE layer.
• Fused vision encoder (Qwen 3.5/3.6-family): FusedVisionAttention merges LayerNorm + QKV + bias + 2D RoPE + scaled dot-product attention + output projection + bias + residual into one GGML graph dispatch (~8 ops → 1). FusedVisionMLP merges LayerNorm + up + bias + GELU + down + bias + residual into one dispatch (7 ops → 1). Combined, these cut the per-block GPU round-trips from ~15 to 2.
• Fused weight projections: Q/K/V projections are fused into a single QKV matmul; gate and up projections are fused into a single gate_up matmul.
• Native quantized compute: quantized weights (Q4_K_M, Q6_K, Q8_0, IQ2_XXS, MXFP4, etc.) are used directly in matmul without expanding to FP32, saving memory and bandwidth. A batched AddmmQuantBatch kernel handles multiple sub-weight matmuls against a single quantized blob in one dispatch.
• Direct CUDA kernels: the cuda backend accelerates fill/copy, unary ops, activation fusions, RMSNorm, softmax, index select, causal masking, RoPE/RoPEEx, cuBLAS GEMM, and supported quantized matmul/get-rows while safely falling back for incomplete op coverage.
• Batched GPU MoE: MoEExpertsSwiGLUResidual (Qwen 3.5/3.6-family) and MoEExpertsForward (Nemotron-H) collapse all selected experts -- and, for Qwen 3.5/3.6-family, the optional shared expert and the residual add -- into a single GGML graph dispatch per MoE layer.
• GEMM-based vision patch embedding (Qwen 3.5/3.6-family): the patch embedding step is reformulated as parallel im2col + matrix multiplication, replacing a single-threaded scalar quintuple-nested loop with a GPU-accelerated matmul.
• Parallelized Q/gate deinterleave (Qwen 3.5/3.6-family): the Q + sigmoid-gate deinterleave in FullAttention prefill is parallelized across tokens, scaling linearly with CPU core count for long prompts.
• Optimized pure C# CPU path: managed GEMM fast paths and contiguous float32 kernels accelerate decode, softmax, RMSNorm, RoPE, fused activations, and other hot paths while keeping quantized GGUF weights compressed during CPU loading.
• Circular KV cache: sliding-window attention layers use a fixed-size circular buffer, bounding memory usage regardless of sequence length.
• KV-cache prefix reuse: multi-turn conversations reuse the longest matching token prefix across turns. Truncation is automatically backed off by the sliding-window size for SWA models so the suffix can rebuild the SWA context.
• Kernel warmup: both CLI and Server run a tiny forward pass at startup to pre-compile GPU kernels (Metal pipeline states, CUDA JIT) and warm the memory pool, avoiding cold-start latency on the first real inference request.
• Prefill caching (Gemma 4, Qwen 3.5/3.6-family): per-forward-pass SWA mask cache (Gemma 4), NeoX RoPE cos/sin lookup table cache across global layers (Gemma 4), and RoPE position tensor cache across layers (Gemma 4, Qwen 3.5/3.6-family) eliminate redundant recomputation during prefill.
• In-place QK RMSNorm (Qwen 3.5/3.6-family): per-head QK normalization is performed in-place using a View, avoiding one tensor allocation and copy per Q/K per layer.

Memory Optimizations

• Zero-copy file-mapped quantized weights (direct CUDA, GGML CUDA, GGML Metal, GGML CPU): the GGUF model file is memory-mapped and quantized tensors are bound directly into native ops via host-pointer buffers. This removes the per-tensor copy from disk into a freshly-allocated native heap buffer that previously roughly doubled the resident set on Apple Silicon for large quantized models. For example, Qwen3.5-35B-A3B-IQ2_XXS (~10 GB GGUF) now runs with ~7 GB peak working memory under Metal instead of ~17 GB. The OS keeps the mapped file in its page cache and pages it out under memory pressure without any inference penalty on Apple Silicon (unified memory).
• Best-fit memory pool: the GGML host allocator uses a best-fit search across pooled blocks instead of first-fit, which avoids handing out a large scratch block to satisfy a tiny intermediate-tensor request and keeps the working-set tightly bounded across long-running inference.
• Bounded pool retention: the integrated-GPU / CPU memory pool now caps individual retained blocks at 64 MB and the total pool at 32 blocks. Combined with mmap-backed weights, this keeps short-lived intermediate tensors recycled fast while bounding the peak resident set.
• Memory-efficient model loading: large tensors are streamed directly to native memory without intermediate managed allocations. F32 weights and norms still load on demand; quantized weights are mmap-backed when supported by the backend.

Benchmarks

Internal regression baseline

Reference numbers measured on Qwen3.6-35B-A3B-UD-IQ2_XXS.gguf (~10 GB on disk, 256 routed experts of which 8 are active per token, with 12 full attention + 30 GatedDeltaNet recurrent layers) on an Apple M4 Pro with 24 GB unified memory:

Metric	Before (v1 baseline)	After (this branch)	Change
Process peak memory footprint	~17 GB	~8 GB	-52%
TensorSharp.Server resident set after load	~20 GB	~8 GB	-60%
Decode throughput (warm, 256 prefill / 64 decode, M4 Pro)	~3.8 tok/s	~10.8 tok/s	+2.85x
Decode latency (warm, 256 prefill / 64 decode, M4 Pro)	~264 ms/token	~92 ms/token	-65%

Reproduce with:

./TensorSharp.Cli --model Qwen3.6-35B-A3B-UD-IQ2_XXS.gguf --backend ggml_metal \
    --benchmark --bench-prefill 256 --bench-decode 64 --bench-runs 3

The memory reduction comes primarily from no longer copying the GGUF file into a separate native heap buffer (the file is now mmap-bound zero-copy into Metal command buffers). The decode throughput increase is largely a side effect of removing that ~10 GB duplicate working set, which was previously triggering OS-level memory pressure on machines with 24 GB or less of physical RAM.

Cross-engine inference matrix

For an apples-to-apples comparison of TensorSharp, llama.cpp, and Ollama on the same on-disk GGUF files (Gemma 4 E4B Q8_0 today, with text / synthetic prefill / image / audio / video tasks and KV-cache dtype sweeps for f32, f16, and q8_0), see docs/inference_benchmark_matrix.md. The driver scripts are in benchmarks/inference_matrix/scripts/ and the per-cell raw JSON outputs live under benchmarks/inference_matrix/results/.

Testing

Unit tests (xUnit)

InferenceWeb.Tests exercises in-process behavior that doesn't require a running server: managed quantized ops, direct CUDA backend kernels when a CUDA device is available, KV cache policies, KV-cache prompt rendering / multi-turn integration, chat-session and session-manager isolation, model service history and KV cache plumbing, request-logging middleware and file-logger provider, image preprocessing, media helpers, structured-output validation, text-upload helpers, model-service upload logging, web UI chat policy, model context length parsing, and backend catalog resolution.

dotnet test InferenceWeb.Tests/InferenceWeb.Tests.csproj

Server integration tests

Integration tests for TensorSharp.Server are in TensorSharp.Server/testdata/. They cover all three API styles (Web UI SSE, Ollama, OpenAI), multi-turn conversations, thinking mode, tool calling, structured outputs, queue behavior, concurrent requests, and abort support. Architecture-specific features (thinking, tool calling) are auto-detected and skipped when the active model does not support them.

# Start TensorSharp.Server, then run:
python3 TensorSharp.Server/testdata/test_multiturn.py
# or
bash TensorSharp.Server/testdata/test_multiturn.sh

See TensorSharp.Server/testdata/README.md for the full test matrix.

Author

Zhongkai Fu

License

See LICENSE for details.