Neutrino-1
Neutrino-1 is an open-weight language-model family released by Fermion Research on July 27, 2026. Its flagship `FermionResearch/Neutrino-8B` is an 8.19B-parameter decoder-only model shipped as a 2.56 GB download and 3.88 GB runtime container, with a 40,960-token context window, tool-calling evaluation, and official CPU, CUDA, MLX, and OpenAI-compatible serving paths.
Neutrino-1 targets a practical local-inference constraint: moving fewer weight bytes per generated token. The 8B model, 0.6B draft model, and shared runtime are designed so one model artifact can serve across an H100, an Apple-silicon laptop, or a desktop CPU. The tradeoff is ecosystem compatibility: its proprietary ternary-family format needs Fermion's engine or forks and cannot be assumed to work in stock llama.cpp, Ollama, or LM Studio.
Fermion Research's launch article, model page, Hugging Face repository, runtime documentation, and PyPI package establish the released checkpoints, architecture, byte sizes, license, context, installation, serving, and compatibility limits. Benchmark, throughput, memory, and speculative-decoding results are vendor-reported unless a cited row is explicitly external. Hacker News and Reddit are used only for demand and deployment questions.
- Local text generation on an Apple-silicon laptop or desktop CPU.
- OpenAI-compatible local serving with streaming and tool calling.
- Compact GPU deployment where model weight bandwidth is the main bottleneck.
- Testing speculative decoding with a matched 0.6B draft and 8B verifier.
Neutrino-1 8B ships as a 2.56 GB coded download that expands to a 3.88 GB container. Fermion publishes a Python engine, a CUDA/CPU llama.cpp fork, and an MLX path. The official command can start a local OpenAI-compatible server with streaming and tool calling, but stock llama.cpp, Ollama, and LM Studio do not load the custom weight type.
Neutrino-1 0.6B shares the tokenizer and container system with the 8B model. Fermion uses it for speculative decoding: the small model proposes tokens and the 8B model verifies them, preserving the 8B greedy output when the implementation works as described. The 0.6B and 0.6B-Chat variants belong on the family page until distinct reader demand justifies separate model records.
Fermion publishes protocol details for MMLU, IFEval, BFCL, GSM8K, throughput, memory, and drafted decoding. Some comparison cells use external or vendor numbers while others are Fermion runs, so they are not one independent leaderboard. Reproduce quality and speed on the actual runtime, hardware, prompt format, context length, and tool schemas you plan to use.
Source confidence
Fermion Research
Fermion Research
Fermion Research on Hugging Face
Fermion Research on PyPI
Hacker News
Reddit r/LocalLLaMA
Neutrino-1 FAQ
Common questions about Neutrino-1.
Is Neutrino-1 8B open weight?+
Yes. Fermion Research publishes the weights under Apache 2.0 with no gate or waitlist. The model is derived from Qwen3-8B, which is also Apache-2.0.
Can Neutrino-1 8B run on a 16 GB Mac?+
Fermion publishes an MLX path and measurements from a 16 GB Apple M5, but actual memory and speed depend on context length and runtime settings. The 40,960-token maximum context adds substantial KV-cache memory beyond the 3.88 GB weight container.
Does Neutrino-1 work with Ollama or stock llama.cpp?+
Not from the official release path. Fermion says the custom weight type requires its engine or its llama.cpp forks; stock llama.cpp, Ollama, and LM Studio cannot load the shipped format.
What does the Neutrino-1 0.6B model do?+
It can run as a small model on its own and also serves as the matched draft model for Neutrino-1 8B speculative decoding. A separate 0.6B-Chat checkpoint is tuned for conversation.