Janas 开源 LLM 推理引擎：SSD 流式加载专家突破 RAM 限制，32GB 笔记本跑 48GB MoE 模型提速近 9 倍Hacker News AIT2开发与工程

Hacker News AI · 2026/10/13 06:05:43

Janas 开源 LLM 推理引擎：SSD 流式加载专家突破 RAM 限制，32GB 笔记本跑 48GB MoE 模型提速近 9 倍

原标题：Janas: Run LLMs larger than RAM by streaming experts from SSD

78AI 研判分

核心综述

Janas 发布开源 LLM 推理引擎，通过从 NVMe SSD 直接流式读取混合专家（MoE）权重，成功在 32GB 内存的笔记本上运行 48GB 的大模型。实测显示其生成速度达 24 tok/s，较 llama.cpp 的 2.8 tok/s 提升近 9 倍，显著降低了大模型本地部署的硬件门槛。

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Janas

37 tok/s from Qwen3-Next-80B (a 48 GB model) on a laptop with 32 GB of memory, 24 without drafts, where llama.cpp manages 2.8; reading a prompt, 62 tok/s against 16. Janas-LLM was faster than llama.cpp on every model compared on the same laptop (Debian 13 Linux, Intel Core Ultra 9 185H, its integrated Arc GPU, 32 GB, an NVMe SSD; 9-11 October 2026; the whole machine below):

Model	reading 1,024 tokens: Janas-LLM	llama.cpp	writing: Janas-LLM	llama.cpp
Qwen3-Next-80B-A3B (48.4 GB, more than the memory)	62 tok/s	16 tok/s	24 tok/s	2.8 tok/s
Qwen3-Coder-Next (48.4 GB, more than the memory)	66 tok/s	14 tok/s	22 tok/s	2.2 tok/s
Qwen3.6-35B-A3B	104 tok/s	79 tok/s	19 tok/s	12 tok/s
Qwen3-30B-A3B	135 tok/s	122 tok/s	32 tok/s	15 tok/s
Gemma-4-26B-A4B-it	84 tok/s	76 tok/s	18 tok/s	12 tok/s
Gemma-4-12B-it	65 tok/s	46 tok/s	9.9 tok/s	7.1 tok/s
Qwen3.5-9B	111 tok/s	78 tok/s	14 tok/s	8.7 tok/s
Gemma-4-E4B-it	122 tok/s	109 tok/s	20 tok/s	15 tok/s
Qwen3-4B	193 tok/s	164 tok/s	27 tok/s	18 tok/s
Gemma-4-E2B-it	191 tok/s	150 tok/s	38 tok/s	19 tok/s
Qwen3.5-2B	441 tok/s	323 tok/s	52 tok/s	28 tok/s
Qwen3.5-0.8B	799 tok/s	519 tok/s	109 tok/s	32 tok/s
Qwen3-0.6B	939 tok/s	670 tok/s	130 tok/s	45 tok/s

The same models from other hands, in the catalog beside them:

Model	reading 1,024 tokens: Janas-LLM	llama.cpp	writing: Janas-LLM	llama.cpp
Qwen3-30B-A3B, bartowski's Q4_K_M (importance matrix)	131 tok/s	122 tok/s	32 tok/s	16 tok/s
Qwen3-30B-A3B, unsloth's UD-Q3_K_XL (3 bits)	100 tok/s	112 tok/s	36 tok/s	11 tok/s
Qwen3-4B, bartowski's Q4_K_M (importance matrix)	191 tok/s	160 tok/s	26 tok/s	18 tok/s

One exception: the 3-bit Qwen3-30B reads a prompt slower than llama.cpp, since its Q3_K weights do not go to the GPU's fast prompt yet; it writes three times as fast.

How they were measured, and the speeds with the drafts: What works now. On another machine the figures will be other ones: janas-bench measures yours.

Try it on Linux x86-64 (Ubuntu 22.04, Debian 12 and later):

curl -LO https://github.com/prabanta-dev/janas/releases/latest/download/janas-linux-x86_64.tar.gz
tar xf janas-linux-x86_64.tar.gz && cd janas
./janas-chat                     # the models it knows: pick one, it is downloaded and checked

Or on Windows 10 (1903 or later) and 11, x86-64: download janas-windows-x86_64.zip, unpack it, and in a terminal in its janas folder run .\janas-chat. On the same laptop under Windows 11 (12 October 2026) Janas-LLM wrote as fast as on Linux (Qwen3-4B 25.6 tok/s, Qwen3-30B-A3B 34.2) and read a prompt as fast with the mixture (Qwen3-30B-A3B 132 tok/s) and 1.8 times as fast with the dense model (Qwen3-4B 346 tok/s against 193), whose whole blocks go to the GPU, through Intel's own Windows driver there.

The models come already converted from Hugging Face; building from the sources: Getting started.

文本

  ▄███████████▄    
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███ ▄ █████ ▄ ███ ▄   J A N A S
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▀█████▄▀▀▀▄█████▀ ▀   a family of tools for artificial intelligence, in C
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▀ ▀ ▀ ▀ ▀ ▀ ▀ ▀

Janas is a family of tools for artificial intelligence, written in C with no dependencies beyond the C library. Its first component, Janas-LLM, runs large mixture-of-experts language models on ordinary computers, with or without a GPU: the weights stay on disk, the experts each token needs are streamed from an NVMe SSD, and the ones that keep coming back stay in memory. An 80-billion parameter model runs, and answers, on a laptop with 32 GB of RAM.

Designed and written by Maurizio "camauri" Cammalleri.

⚠️ Read this first

This is an experiment, not a product.


Stage	Early development. The public API and the command line change without notice, and have changed several times a week. The models' files are the exception: the `.jns` files published on Hugging Face (format version 2) will be read by every later version of Janas-LLM.
Stability	Not verified. The engine is deliberately hard on the machine: it fills the free memory with its expert cache and reads the disk at full speed with `O_DIRECT`. On a machine with less headroom than it reckons, the desktop can be pushed into swap and become slow or unresponsive. Using a GPU has, once, caused a driver reset that took the desktop with it.
Data	It writes only in `~/.cache/janas` (what it learns about your machine, and the system messages it has read, so as not to read them again) and where you tell it to put a converted model. It does not touch the model files it reads.
Hardware	Developed and run on one machine (below). On anything else it is untested: it may be slower, it may refuse the model, it may misbehave.

If something goes wrong, Ctrl-C stops a reply and Ctrl-D leaves the chat; --cache <GiB> puts a hard limit on the memory it takes, and JANAS_GPU=0 keeps it off the GPU entirely.

The machine it grew on

Everything in this repository was written and measured on:

Linux Debian 13 (trixie), x86-64, kernel 6.12.107; GPU driver Mesa 26.1.6 (Vulkan); built with GCC 14.2
Intel Core Ultra 9 185H (6 performance cores, 8 efficiency, 2 low-power; AVX2, FMA, F16C, AVX-VNNI; integrated Arc GPU)
32 GB of DDR5 RAM
1 TB NVMe SSD (about 5.7 GB/s reading)

The Windows build was run on the same laptop under Windows 11 (build 26300), with Intel's own graphics driver: its tests pass there, and how fast it was is under Or on Windows, above.

Everything the engine does is chosen from what it measures, so another machine will make other choices — that part is meant to travel. What has never been tried is the rest: other CPUs (especially without AVX-VNNI, or AMD), discrete GPUs, slower disks, more or less memory. If you run it somewhere else, the report from janas-bench is the single most useful thing you can send back (see Helping it run on more hardware).

（注：更多技术实现细节与完整 API 文档请查阅原项目 README）