Neroued/ninfer

NInfer

Selected checkpoints. Maximum single-GPU inference performance.

NInfer is a from-scratch C++/CUDA inference engine for Qwen3.5 Dense and MoE architectures on a single NVIDIA GeForce RTX 5090. It runs text, image, and video prompts through a local CLI or OpenAI-/Anthropic-compatible HTTP APIs. The runtime is deliberately specialized: one GPU, one resident model, and a startup-fixed capacity of one to eight active requests.

Five official artifacts are available. The quick-start commands use Qwen3.8-27B NVFP4.

| Model | Weights | Artifact | Download and model card | |---|---|---|---| | Qwen3.6-27B | groupwise-int | qwen3_6_27b.ninfer | Qwen3.6-27B | | Qwen3.6-27B | nvfp4 | qwen3_6_27b_nvfp4.ninfer | Qwen3.6-27B NVFP4 | | Qwen3.8-27B | groupwise-int | qwen3_8_27b.ninfer | Qwen3.8-27B | | Qwen3.8-27B | nvfp4 | qwen3_8_27b_nvfp4.ninfer | Qwen3.8-27B NVFP4 | | Qwen3.6-35B-A3B | groupwise-int | qwen3_6_35b_a3b.ninfer | Qwen3.6-35B-A3B |

Each v3 .ninfer artifact carries model configuration, encoded weights, logical bindings and frontend resources. Runtime execution uses those facts with the implemented model and Op capabilities. You can also convert your own weights, reuse an official recipe or choose another supported mixture of formats.

The current engine requires v3 artifacts. Existing official v2 downloads can be upgraded locally without downloading the weights again.

Quick start

NInfer requires 64-bit Linux, an NVIDIA GeForce RTX 5090, a CUDA toolkit supporting sm_120a, CMake 3.28 or newer, a C++20 host compiler, Ninja, pkg-config, FFmpeg development libraries (libavformat, libavcodec, libavutil, and libswscale), and libcurl >= 7.85. CUDA 13.1 is the validated development toolkit; CMake does not impose a CUDA version floor. The build rejects CUDA architectures other than sm_120a.

Build the product binaries:

git clone https://github.com/Neroued/ninfer.git
cd ninfer
cmake -S . -B build -G Ninja -DCMAKE_BUILD_TYPE=Release
cmake --build build -j

Tests and benchmarks are excluded from the default build. cmake --preset release configures the same product build; cmake --preset dev also enables tests and benchmarks and finds a Python 3 interpreter. Both presets use build/ and explicitly reset the build options. Machine-specific compiler and Python paths belong in the ignored CMakeUserPresets.json. See build organization and configuration for details.

There is no install target or packaged binary distribution; run NInfer from its source build tree. Python tools run independently of CMake; the standalone HBM probe has its own build command.

Download the artifact used by this example with the Hugging Face CLI:

hf download neroued/Qwen3.8-27B-nvfp4-NInfer \
  qwen3_8_27b_nvfp4.ninfer \
  --local-dir models

Start a long-running text/agent server with two active-request lanes and explicit Device/Host checkpoint capacity:

./build/apps/ninfer-serve models/qwen3_8_27b_nvfp4.ninfer \
  --max-context 240000 \
  --kv-capacity 240000 \
  --max-concurrency 2 \
  --kv-dtype fp8 \
  --device-state-slots 2 \
  --host-state-slots 8 \
  --host-kv-mib 8192 \
  --spec mtp --draft-tokens 3 \
  --lm-head-draft \
  --preserve-thinking

Each request has a 240,000-token logical ceiling. A shared 240,000-token Device KV pool serves admitted requests; two requests run concurrently when their combined reservations fit. The cache tiers provide two Device checkpoint slots, eight pinned Host State slots, and 8 GiB of pinned Host KV beyond the two active StateImages.

Send an OpenAI-style request:

curl http://127.0.0.1:8080/v1/chat/completions \
  -H 'Content-Type: application/json' \
  -d '{
    "model": "qwen3.8-27b",
    "messages": [{"role": "user", "content": "Reply with one short sentence."}],
    "max_tokens": 64
  }'

Run a one-shot CLI request with a 32,768-token allocation:

./build/apps/ninfer models/qwen3_8_27b_nvfp4.ninfer \
  --prompt "Explain prefill and decode, then give a concise conclusion." \
  --max-context 32768 \
  --max-new 8192 \
  --kv-dtype fp8 \
  --spec mtp --draft-tokens 3 \
  --lm-head-draft

Answer content is written to stdout. Human-readable startup/runtime diagnostics and the CLI-owned reasoning, timing, throughput, memory, and speculative-decoding report are written to stderr; reasoning and the result report remain unprefixed product output. On a terminal, weight materialization uses one transient progress line followed by a compact Engine-ready summary. Redirected stderr receives persistent readable progress without terminal control sequences. Use --log-level debug for complete startup detail. Option and local input errors remain direct command diagnostics. Use --messages FILE and --vision for structured image/video input; see the CLI guide and committed examples.

Resource-aware long-context reuse

A reusable prefix checkpoint contains KV and the complete continuation state for its exact prompt frontier. A Device-resident checkpoint resumes directly. Under pressure, the planner weighs Device retention, pinned Host State/KV, and eviction by immediate restore work and later reuse cost. Active requests retain their completion reservations.

See Resource scheduling and context cache for the algorithm and Serve TTFT benchmark for public-HTTP coverage of hot reuse, Host resume, eviction, shared prefixes, scheduling boundaries, and multimodal load.

Performance

Published measurements use an RTX 5090. The performance index links to per-model run records and the measurement rules. The tables below are excerpts from those detailed results.

Concurrent MTP3 decode

Saturated decode used INT8 group-64 KV, CUDA Graphs, MTP3, and one 8,192-token generation per active request. Throughput uses aggregate committed decode tokens from complete intervals whose actual decode batch equaled the configured concurrency. Acceptance covers the complete request wave; these rates are steady decode (tok/s).

| Model profile | C=1 tok/s / accept | C=2 tok/s / accept | C=4 tok/s / accept | C=8 tok/s / accept | C8 / C1 | |---|---:|---:|---:|---:|---:| | Qwen3.6-27B groupwise-int | 185.8 / 68.2% | 247.0 / 69.0% | 309.5 / 68.4% | 535.0 / 68.3% | 2.88× | | Qwen3.6-27B nvfp4 | 202.4 / 69.3% | 399.7 / 71.4% | 699.7 / 69.3% | 1,146.9 / 68.6% | 5.67× | | Qwen3.6-35B-A3B groupwise-int | 642.5 / 68.6% | 907.2 / 66.3% | 1,213.5 / 69.6% | 1,380.7 / 68.0% | 2.15× | | Qwen3.8-27B nvfp4 | 143.8 / 48.9% | 267.6 / 48.1% | 461.1 / 45.8% | 766.6 / 46.0% | 5.33× |

Single-request serving

The serial serving corpus used INT8 group-64 KV, CUDA Graphs, a 1,024-token prefill chunk, and five fixed seeds after warm-up. The table keeps one short-prefill, one extreme-prefill, and one structured-output MTP3 point for each published profile; the full context and scenario matrices are linked from each model below.

| Model profile | 7,680-token prefill | 260,096-token prefill | Structured MTP3 decode | |---|---:|---:|---:| | Qwen3.6-35B-A3B groupwise-int | 17,705.4 tok/s | 5,247.0 tok/s | 779.6 tok/s | | Qwen3.6-27B groupwise-int | 3,218.1 tok/s | 1,614.8 tok/s | 193.0 tok/s | | Qwen3.6-27B nvfp4 | 11,191.5 tok/s | 2,510.6 tok/s | 252.2 tok/s | | Qwen3.8-27B groupwise-int | 3,274.7 tok/s | 1,609.7 tok/s | 224.4 tok/s | | Qwen3.8-27B nvfp4 | 8,340.4 tok/s | 2,203.1 tok/s | 219.8 tok/s |

Evaluation

Capability scores were measured through NInfer's OpenAI-compatible serving route with thinking enabled, MTP3, and EvalScope 1.9.0 (0-shot, rule scoring, one sample per problem):

| Model profile | AIME 2025 | AIME 2026 | GPQA-Diamond | ERQA | RealWorldQA | |---|---:|---:|---:|---:|---:| | Qwen3.6-27B groupwise-int | 86.67% | 93.33% | 86.87% | — | — | | Qwen3.6-27B NVFP4 | 93.33% | 93.33% | 84.34% | — | — | | Qwen3.6-35B-A3B groupwise-int | 90.00% | 90.00% | 85.35% | — | — | | Qwen3.8-27B groupwise-int | 96.67% | 96.67% | 87.37% | 66.25% | 82.22% | | Qwen3.8-27B NVFP4 | 96.67% | 96.67% | 90.40% | 66.25% | 83.53% |

The Qwen3.6 rows used temperature 0.6 and presence penalty 1.0; the Qwen3.8 rows used temperature 1.0 and presence penalty 0.0. Multimodal evaluation used --vision and an 81,920-token context limit. Text evaluation used 262,144 tokens except Qwen3.8-27B NVFP4, which used 252,928 tokens to fit the RTX 5090 after weights. Each score is one sample per problem; model cards contain the correct/total counts and evaluation notes.

Startup notes

GPU residency is fixed at process startup. --spec selects speculative decoding residency, and --vision independently selects Vision residency. Qwen3.6-35B-A3B DFlash can be combined with Vision; it accelerates generated-text decode after multimodal prefill, not Vision encode itself.

Docker

Build the runtime image on a host with the NVIDIA Container Toolkit:

docker build --tag ninfer:local .

Mount the downloaded model and run the same example server profile:

docker run --rm \
  --gpus '"device=0"' \
  --publish 8080:8080 \
  --volume "$PWD/models:/models:ro" \
  ninfer:local \
  ninfer-serve /models/qwen3_8_27b_nvfp4.ninfer \
  --host 0.0.0.0 \
  --max-context 240000 \
  --kv-capacity 240000 \
  --max-concurrency 2 \
  --kv-dtype fp8 \
  --device-state-slots 2 \
  --host-state-slots 8 \
  --host-kv-mib 8192 \
  --spec mtp --draft-tokens 3 \
  --lm-head-draft \
  --preserve-thinking

Capabilities and limits

The official artifacts provide the following capabilities, with optional components enabled at startup:

• text generation with thinking and non-thinking prompt modes;
• image, multi-image, video, and mixed multimodal messages;
• chunked prefill, exact-batch CUDA Graph decode, and startup-bounded batched decode;
• MTP speculative decoding with draft windows from one to five;
• BF16, INT8, FP8, NVFP4, and K8V4 KV storage;
• offline causal-perplexity scoring;
• private and shared exact-prefix reuse with Device/Host State and KV retention;
• model-aware sampling defaults and explicit sampler overrides;
• OpenAI Responses Core, OpenAI Chat Completions, and Anthropic Messages, including streaming,

tools, local response state, token counting, and usage accounting.

The 35B-A3B target additionally supports DFlash with draft windows from one to fifteen for Text and image/video Vision prompts. Qwen3.8-27B artifacts with the DFlash2 companion weights support --spec dflash2 --draft-tokens 7 for the same Text/Vision Engine path, with draft counts 1..15 and either full or optimized proposal heads.

The product boundary remains intentionally small:

• one RTX 5090 and one resident model per Engine;
• a startup-fixed capacity of one to eight active requests with bounded FIFO ingress;
• no request preemption, priority/QoS, active-request swapping, weight offload, multi-GPU, or

distributed serving;

• one shared startup-fixed KV pool across active requests and retained prefixes;
• model architectures and format/shape combinations use explicitly implemented native paths;
• parsed tool calls are returned to the client; NInfer does not execute tools;
• the in-tree C++ headers are not distributed as an installed SDK.

--max-context is each sequence's logical limit. --kv-capacity sizes the shared Main Text KV pool used by active requests and retained prefixes; auto resolves the largest legal capacity at startup from the memory remaining after weights while keeping 1 GiB of sizing headroom. Explicit capacities remain fixed for the process lifetime.

Documentation

• Documentation index
• CLI
• HTTP serving
• Performance
• Perplexity evaluation
• Weight conversion and custom recipes
• Resource scheduling and context cache
• Serve TTFT benchmark
• CLI examples
• Contributing

Run the relevant --help for the exact current option contract.

Support

NInfer is a personal project that I develop out of interest. If you find it useful and would like to support its continued development, you can support the project on Ko-fi.

Support is entirely voluntary. It is not a purchase or investment and does not come with financial returns, promised services or features, or a role in project decisions. The project's direction, priorities, technical choices, and release schedule remain independently determined by the maintainer.

License

NInfer is licensed under the Apache License 2.0.

The published artifacts are derived from Qwen/Qwen3.6-27B, Qwen/Qwen3.8-27B, and Qwen/Qwen3.6-35B-A3B. The Qwen3.6-27B NVFP4 artifact also uses the fixed packed weights from rdtand/Qwen3.6-27B-PrismaSCOUT-Blackwell-NVFP4-BF16-vllm. The Qwen3.8-27B NVFP4 artifact also uses the fixed mixed FP8/NVFP4 weights from unsloth/Qwen3.8-27B-NVFP4. These source repositories are distributed under Apache-2.0. Vendored dependencies retain their own license files under third_party/.