Air.rs

Memory-Fluid LLM Inference Engine
Run models larger than your VRAM — at full GPU speed.

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The Problem

Large language models don't fit in VRAM. A 70B-parameter model at FP16 needs 140 GB of GPU memory. Even quantized to Q4, that's still 35 GB — more than a consumer RTX 4090's 24 GB.

Current solutions:

• CPU offloading → 10–50× slower inference
• Model parallelism → requires multiple expensive GPUs
• Aggressive quantization → degrades output quality

The Air.rs Solution

Air.rs treats VRAM as a streaming cache, not a storage device. Instead of loading the entire model into GPU memory, it streams layers from NVMe → RAM → VRAM in a triple-buffered pipeline that hides PCIe transfer latency behind kernel execution.

 ┌──────────────────────────────────────────────────────────────┐
 │                    Air.rs Pipeline                           │
 │                                                              │
 │  NVMe SSD ──mmap──→ System RAM ──PCIe DMA──→ VRAM           │
 │     (model.gguf)       (page cache)       (ping-pong buf)   │
 │                                                              │
 │  While GPU executes layer N,                                 │
 │  PCIe is already uploading layer N+1,                        │
 │  and NVMe is prefetching layer N+2.                          │
 └──────────────────────────────────────────────────────────────┘

Result: Run 70B+ models on a single consumer GPU at near-native speed.

Features

• 🚀 Layer-Streamed Inference — only one transformer block is in VRAM at a time
• 🔁 Triple-Buffer Pipeline — overlaps NVMe reads, PCIe transfers, and GPU kernels
• 📄 Native GGUF Support — directly memory-maps quantized model files with zero parsing overhead
• 🗺️ 4KB Page-Aligned DMA — transfers are snapped to OS page boundaries for optimal throughput
• 💾 KV-Cache Shuttle — swaps attention caches between RAM and VRAM per-layer
• 🔌 OpenAI-Compatible API — drop-in /v1/chat/completions endpoint via Axum
• 🐍 Python Bindings — optional PyO3 module for Python integration
• ⚡ Fused Kernels — candle-core CUDA backend with cudarc 0.13

Architecture

src/
├── main.rs           # Entry point
├── lib.rs            # Module declarations, constants
├── loader.rs         # GGUF parser — extracts tensor offsets from file metadata
├── manifest.rs       # Execution planner — groups tensors into page-aligned chunks
├── uploader.rs       # Transfer engine — async triple-buffered NVMe→VRAM pipeline
├── orchestrator.rs   # Tensor hydrator — maps VRAM pointers into Candle tensors
├── generator.rs      # Inference loop — layer-streamed token generation
├── kv_cache.rs       # KV-cache manager — shuttles attention state RAM↔VRAM
├── api.rs            # OpenAI-compatible HTTP API (Axum)
└── python.rs         # Optional PyO3 bindings

Prerequisites

Requirement	Version
Rust	1.75+ (2021 edition)
CUDA Toolkit	12.x
NVIDIA GPU	Compute capability 7.0+ (Turing/Ampere/Ada/Hopper)
MSVC (Windows)	Visual Studio 2022 Build Tools
OS	Windows 10/11, Linux (Ubuntu 22.04+)

Quick Start

Building on Windows

Use the provided build script that auto-configures the MSVC and CUDA environment:

.\build_air.ps1

Building manually

# Ensure CUDA Toolkit is installed and nvcc is on PATH
cargo build --release --features cuda

Running

cargo run --release --features cuda

Usage

Air.rs exposes an OpenAI-compatible API. Once running, send requests like:

curl -X POST http://localhost:3000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "llama-70b-q4",
    "messages": [{"role": "user", "content": "Hello!"}],
    "max_tokens": 128
  }'

How It Works

1. Load — loader.rs parses the GGUF file header to extract exact byte offsets of every tensor
2. Plan — manifest.rs groups tensors into layer chunks with 4KB-aligned DMA boundaries
3. Stream — uploader.rs runs an async pipeline: madvise() prefetches the next chunk into the OS page cache while the current chunk is being DMA'd to VRAM via htod_sync_copy
4. Execute — orchestrator.rs wraps the raw VRAM buffer into Candle tensors using pointer arithmetic (the "magic trick" of offset calculation)
5. Cache — kv_cache.rs downloads the attention KV-cache back to RAM after each layer, then re-uploads it when that layer is needed again
6. Repeat — the pipeline runs layer-by-layer, token-by-token, never exceeding one layer's worth of VRAM

Project Status

⚠️ Alpha — Core pipeline architecture is implemented and compiles. Kernel fusion, full inference loop, and benchmarks are in active development.

Roadmap

• GGUF loader with exact byte-offset tensor mapping
• Page-aligned DMA manifest builder
• Triple-buffered async transfer engine
• VRAM pointer → Candle tensor hydration
• KV-cache RAM↔VRAM shuttle
• OpenAI-compatible API scaffolding
• Full transformer block kernel execution
• Token sampling with temperature/top-p
• GBNF grammar-constrained generation
• Multi-GPU support (NVLink/PCIe)
• Benchmarks vs llama.cpp, vLLM, exllama

Acknowledgments

• candle — Rust ML framework with CUDA support
• llama.cpp — GGUF format and quantization reference
• AirLLM — original layer-streaming concept in Python

License

MIT © Sunay Hegde