GraphCanon updated 4w · GitHub synced 4w
Decision brief
Focuses on efficient and scalable model deployment with Rust, supporting various GPU technologies and inference frameworks.
Good fit when
- When aiming for high-performance Rust-based deployment that leverages hardware accelerators like NVIDIA DGX systems and Cuda technology.
- If your project requires integration with the OpenAI API or supports Speculative Decoding techniques.
Avoid when
- Avoid if you prefer tools in languages other than Rust for inference engines, since this is purely designed in Rust.
- Not ideal if your deployment environment does not support NVIDIA GPU technologies such as DGX, which are key to maximize performance with Atlas.
Observed Jul 14, 2026 · Source: enrich:decision_facts
Verify the decision
Maintenance and security
Full trust report- Maintenance
- Very active (0d since push)
- As of 4w
- Provenance
- Not a fork · Organization account
- As of 4w
- Security (OSV)
- No lockfile
- As of 1mo
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
cargo add atlasSimilar tools
Same-category neighbours. No typed graph edges are catalogued for this tool yet.
Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
Avarok-Cybersecurity/atlas is a inference engine written in Rust, focused on providing efficient and scalable model deployment.
Capability facts
- Languages
- rust
Source: github.language · Jul 25, 2026
Categories
Tags
README
🚀 Quick Start
The whole supported model matrix lives in one Docker image. Pull it, mount your HuggingFace cache, point Atlas at any model ID from the model table.
Defaults below are tuned for maximum accuracy under agentic-coding workloads — 64K context window, BF16 MTP draft head (highest acceptance rate ⇒ highest end-to-end throughput), prefix caching for multi-turn tool loops, and FP8 KV cache with
auto-promoted boundary layers. These are the recipes we use to drive opencode / Claude Code / Cline through Atlas on a single Spark.
🔌 Adding a New Hardware Target
The full recipe is in docs/HARDWARE.md. The short version: implement two traits (ComputeTarget for the build-time compiler, GpuBackend for the runtime), drop kernel sources into kernels/<your-hw>/, add one match arm in the registry. There is a MockGpuBackend in spark-runtime that lets you write and test the entire scaffold without owning the hardware — every layer above the GPU trait is hardware-agnostic, so unit tests can run on a laptop. We bolted the project from "single CUDA target" to "trait-pluggable across vendors" specifically so that the AMD, Apple, and Intel ports stop being our problem and start being yours.
⚖️ License and Enterprise Edition
Atlas operates under a dual-license model. Both are real, both are intentional, and neither is a teaser for the other.
- Community Edition — AGPLv3. Free, open, copyleft. Use it for yourself to run inference on your own hardware, research, hobby projects, side-projects, and/or hosted demos, as examples. If you want to make money from Atlas, purchase a commercial license.
- Enterprise Edition — commercial license. If you need to ship Atlas inside a closed-source product, run it as a SaaS backend without inheriting the AGPLv3 source-disclosure obligation, or simply want a support relationship with the people who wrote the kernels, contact sales. Enterprise customers also receive prioritized model and hardware ports.
This split exists for a single reason: a permissive license keeps us building Atlas full-time, and the AGPL community license keeps the project honest. What is in this repository is what we run.
For agents
This page has a .md twin and JSON over the API.