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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.

Features

  • Multi-architecture support -- Gemma 4, Gemma 3, Qwen 3, Qwen 3.5, GPT OSS, Nemotron-H, Mistral 3
  • Multimodal inference -- image, video, and audio inputs (Gemma 4); images for Gemma 3 / Qwen 3.5 / Mistral 3
  • Thinking / reasoning mode -- structured chain-of-thought output with <think> / <|channel>thought / <|channel>analysis tags (Qwen 3, Qwen 3.5, 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 and GGML CUDA on Linux/NVIDIA, with fused whole-model GPU dispatch for Gemma 4 decode on Metal (~2.6x speedup over per-op dispatch)
  • 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 mixes 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 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 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

Supported Model Architectures

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

See Model Architecture Cards for detailed documentation of each architecture.

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 Qwen3.5-9B unsloth/Qwen3.5-9B-GGUF
Qwen 3.5 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 Description
GGML Metal --backend ggml_metal GPU-accelerated via Apple Metal (macOS). Recommended for Apple Silicon. 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 GPU-accelerated via GGML CUDA on Linux with an NVIDIA GPU. Quantized weights are uploaded to device memory once at load time and the host copy is released afterwards.
GGML CPU --backend ggml_cpu CPU inference using native GGML with optimized kernels. Quantized weights are mapped zero-copy from the GGUF file.
Pure C# CPU --backend cpu 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.GGML.Native/     # Native C++ bridge to ggml (builds libGgmlOps)
├── TensorSharp.Server/          # Web chatbot + API server (ASP.NET Core)
│   ├── ModelService.cs          # Model lifecycle management
│   ├── InferenceQueue.cs        # FIFO request queue with position tracking
│   ├── BackendCatalog.cs        # Discovery of available compute backends
│   ├── TextUploadHelper.cs      # Token-budget-aware text-file truncation
│   ├── 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 (model cards, EN + 中文)
└── 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.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.

Prerequisites

  • .NET 10 SDK
  • macOS (Metal backend): CMake 3.20+ and Xcode command-line tools for building the native GGML library
  • Linux (GGML CPU / CUDA backends): CMake 3.20+; for ggml_cuda, install an NVIDIA driver plus CUDA Toolkit 12.x or another compatible CUDA toolkit
  • 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

On Linux, build-linux.sh now 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

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

On macOS this compiles libGgmlOps.dylib with Metal GPU support. On Linux, build-linux.sh preserves an existing CUDA-enabled build and auto-enables GGML_CUDA when a CUDA toolchain is detected; 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.

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

# Image inference (Gemma 3/4, Qwen 3.5)
./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

# 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, ggml_cpu, ggml_metal, or ggml_cuda
--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)
--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>

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}

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

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

  • Multi-turn chat conversations
  • 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

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, ggml_cpu, ggml_metal, or ggml_cuda
--max-tokens <N> Default maximum tokens to generate when a request omits the limit (default: 20000)

Runtime environment variables:

Variable Description
BACKEND Default compute backend, 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)

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, 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 / 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 / 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
  • 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 image inputs. Place its multimodal projector (mmproj-gemma3-4b-f16.gguf) next to the model file for automatic loading.

Qwen 3.5

All Qwen 3.5 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) 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

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) that links against ggml. On macOS this provides Metal GPU compute, and on 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.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.

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

Performance Optimizations

  • 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.
  • 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 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 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.
  • Batched GPU MoE: MoEExpertsSwiGLUResidual (Qwen 3.5) and MoEExpertsForward (Nemotron-H) collapse all selected experts -- and, for Qwen 3.5, 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): the patch embedding step is reformulated as parallel im2col + matrix multiplication, replacing a single-threaded scalar quintuple-nested loop with a GPU-accelerated matmul.
  • 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.

Memory Optimizations

  • Zero-copy file-mapped quantized weights (CUDA, 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

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.

Testing

Unit tests (xUnit)

InferenceWeb.Tests exercises in-process behavior that doesn't require a running server: managed quantized ops, KV cache policies, image preprocessing, media helpers, structured-output validation, text-upload helpers, web UI chat policy, model service history, 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.

About

A C# inference engine for running large language models (LLMs) locally using GGUF model files. TensorSharp provides both a console application and a web-based chatbot interface for multi-turn conversations with multimodal models.

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  • C++ 37.8%
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