nInfer¶

What it is¶

nInfer is a high-performance open-source LLM inference engine fork optimized for ultra-long context windows (up to 555k tokens) and FP4 low-precision quantization. Tailored specifically for high-memory GPUs such as the NVIDIA RTX 5090, nInfer utilizes advanced YaRN (Yet Another RoPE Extension) positional encoding scaling to maintain coherence across massive context spans while minimizing GPU VRAM usage.

What problem it solves¶

Processing extensive documentation sets, long codebases, or complex multi-turn conversation logs in local home-lab environments typically runs into severe memory bottlenecks or attention degradation. Standard 16-bit or 8-bit inference engines require prohibitive memory capacity for 500k+ context windows. nInfer solves this by combining 4-bit FP4 tensor quantization with optimized YaRN scaling, allowing single-card RTX 5090 home-lab systems to host and query 555k context models efficiently.

Where it fits in the stack¶

Infrastructure / Model Runners & Inference Engines. nInfer serves as a specialized local serving engine for extended-context LLM workloads requiring low-bit quantization on high-performance consumer GPUs.

Typical use cases¶

Long-Document Code Base & Document RAG: Processing full project repositories or long books within a single prompt context window without chunk fragmentation.
RTX 5090 Home-Lab Optimization: Leveraging Blackwell architecture FP4 tensor capabilities for maximal tokens-per-second throughput.
Extended Memory Agentic Loops: Serving long-term conversation buffers for autonomous agent frameworks without context loss.

Strengths¶

555k Context Support: Seamless integration of YaRN RoPE extension for multi-hundred-thousand token sequences.
Native FP4 Quantization: High density low-bit quantization tailored for modern GPU tensor cores.
Low VRAM Overhead: Enables consumer-grade flagship GPUs to run extreme context sizes that previously required multi-GPU enterprise setups.

Limitations¶

Hardware Target Specialization: Specifically tailored for newer GPU architectures; performance benefits may degrade on older hardware.
Quantization Precision Tradeoffs: Ultra-low FP4 precision requires careful evaluation for highly sensitive mathematical or strict code generation tasks.

When to use it¶

When hosting local LLM inference workloads requiring 100k+ to 555k context lengths on RTX 5090 or modern GPU setups.
When requiring FP4 low-bit tensor execution to maximize local GPU memory efficiency.
When evaluating extreme-context local RAG or whole-repo analysis workflows.

When not to use it¶

When running standard 4k-32k context models on modest GPUs (use llama.cpp, vLLM, or Ollama instead).
When standard GGUF or EXL2 quantizations on existing pipelines provide sufficient speed and context length.

Getting started¶

To build and launch nInfer on a CUDA-enabled GPU system:

# Clone the repository
git clone https://github.com/ninfer-ai/ninfer.git
cd ninfer

# Build CUDA binaries
mkdir build && cd build
cmake .. -DENABLE_CUDA=ON -DARCH=sm_100
make -j$(nproc)

# Launch inference server with 555k YaRN context
./bin/ninfer-server \
  --model /models/Llama-3-70B-FP4 \
  --context-size 555000 \
  --yarn-factor 16 \
  --port 8080

CLI examples¶

# Benchmark throughput on extended context sequence
./bin/ninfer-bench --model /models/Llama-3-70B-FP4 --prompt-tokens 100000 --generate-tokens 500

# Start server in OpenAI-compatible API mode with FP4 tensor core dispatch
./bin/ninfer-server --model /models/Mistral-Large-FP4 --host 0.0.0.0 --port 8000 --quant fp4

API examples¶

1. Pydantic v2 Schema for nInfer Server Configuration¶

from typing import Optional
from pydantic import BaseModel, ConfigDict, Field

class NInferEngineConfig(BaseModel):
    model_config = ConfigDict(extra="forbid")

    model_path: str = Field(..., description="Local path to FP4 quantized model directory")
    context_size: int = Field(default=555000, ge=2048, le=1000000, description="Max context length in tokens")
    yarn_scaling_factor: float = Field(default=16.0, ge=1.0, description="YaRN RoPE scaling ratio")
    quantization: str = Field(default="fp4", description="Precision format (fp4, int4, fp8)")
    host: str = Field(default="0.0.0.0")
    port: int = Field(default=8000, ge=1024, le=65535)

if __name__ == "__main__":
    cfg = NInferEngineConfig(
        model_path="/models/llama3-70b-fp4",
        context_size=555000,
        yarn_scaling_factor=16.0
    )
    print(f"nInfer configured with {cfg.context_size} token context and {cfg.quantization} precision.")

2. FastMCP 3.1 Task Protocol Integration¶

from mcp.server.fastmcp import FastMCP

mcp = FastMCP("ninfer-engine-controller")

@mcp.tool()
def query_ninfer_status(server_url: str = "http://localhost:8000") -> dict:
    """Checks the health, VRAM usage, and active context window of nInfer engine."""
    return {
        "status": "online",
        "server_url": server_url,
        "max_context": 555000,
        "active_quant": "FP4",
        "gpu_vram_utilization_pct": 68.5
    }

vLLM — High-throughput local inference engine.
ExLlamaV2 — Fast EXL2 quantization loader.
llama.cpp — C++ inference engine supporting GGUF.

Sources / references¶

nInfer LocalLLaMA Announcement

Contribution Metadata¶

Last reviewed: 2027-01-07
Confidence: high