Aria Compute

huggingface.co/ariacompute · ariacompute.com · github.com/ariacompute · Shanghai, China

On-device AI, cloud-cooperative. Aria Compute builds an end-to-end quantized model and inference engine stack that brings production-grade LLMs, VLMs, and VLA models to phones, robots, wearables, and embedded devices — all running purely on-device CPU, with no network dependency.

Who We Are

Aria Compute is an AI infrastructure company founded in June 2026, focused on on-device multimodal foundation models and hybrid inference infrastructure. Our core offering is the Aria Compute platform: a unified toolchain for voice, vision, text, and embodied intelligence (VLA) models, built from the ground up for mobile and edge hardware.

What We Ship

Aria Engine

A from-scratch, pure-Rust inference runtime optimized for our quantized model bundles. Features include:

• CPU-only execution — No GPU or cloud required
• Blocked Hadamard domain multiplication (HDM) — Applies the same rotation tiles used during quantization to activations at runtime, enabling exact dequantization-free matrix multiplication
• mmap-based weight loading — Direct memory-mapped IO with zero-copy weight access, minimizing startup latency and RAM overhead
• Quantized KV cache — 4-bit per-entry cache reduces context memory ~4× vs FP16, keeping multi-turn conversation viable on low-RAM devices
• Streaming token generation — Low-latency output suitable for real-time chat and tool-calling loops

Aria Quant Bundles

We quantize, package, and distribute production-ready bundles of popular open-source models. Every bundle is produced by our Python quantization toolkit using a common recipe stack:

All recipes share the same pipeline: Hadamard rotation preprocessing followed by Lloyd-Max codebook quantization. The Hadamard transform rotates weight matrices to decorrelate columns, making them amenable to low-bit codebook compression with minimal reconstruction error. No calibration data is required — the method is data-free and deterministic given a random seed.

Each bundle ships as a lightweight directory: weight.bin (codebook-indices + per-group/per-channel codebooks) + config.json + tokenizer files. No GGUF, no safetensors — just Aria-native format.

Model Portfolio

We provide quantized bundles across 9 model families from the open-source community, each with q4 / q8 / q326_channel variants:

Bench Evaluation

We maintain an automated cross-engine benchmark suite at bench/ that compares Aria-quantized models head-to-head against the same base models running in their officially recommended engines (llama.cpp GGUF, HuggingFace Transformers). Metrics span:

• Capability: MMLU, GSM8K, C-Eval, HumanEval, Needle-in-Haystack, IFEval
• Performance: TTFT, tokens/sec, peak memory, model size, power draw
• Output quality: Token overlap, exact prefix fraction, logprob delta vs FP16

Design Philosophy

• Local-first. All inference happens on-device. No data leaves the device. No network calls during inference.
• Calibration-free quantization. Hadamard + Lloyd-Max requires no calibration dataset, no fine-tuning, no distillation — just the weight matrix.
• One bundle, any CPU. ARM (Android, iOS, Linux) and x86_64 are first-class targets. Rust portability ensures the same bundle runs everywhere.
• Progressive quality. Choose the recipe that fits your device: q4 for smallest footprint, q8 for near-lossless, q326_channel for the sweet spot in between.

Getting Started

• HuggingFace: huggingface.co/ariacompute — Browse and download quantized model bundles
• Website: ariacompute.com — Dashboard and documentation
• GitHub: github.com/ariacompute — Engine, model quantization toolkit, and benchmark suite
• Dashboard: ariacompute.com/dashboard/models — Authenticated model download

License

All Aria Compute quantized bundles inherit the license of their base model. See individual model cards for details. The Aria Engine and quantization toolkit are separately licensed.