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prabanta-dev/janas

37 tok/s from Qwen3-Next-80B (48 GB) on a 32 GB laptop, 24 without drafts vs 2.8 for llama.cpp; reading 62 vs 16 tok/s. Faster than llama.cpp on all 13 models compared in Q4_K_M. LLMs bigger than your RAM, pure C, CPU or GPU.

#LLM inference
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GNU General Public License v3.0
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Momentum

How fast prabanta-dev/janas is gaining stars, and where it lands on Trendshift and GitHub Trending

Activity and trending

Stars, forks and merged pull requests each day or month, lined up with the repository's rank on Trendshift Trending

Oct 11 – Nov 5, 2026

Stars +583 in 26 days

0100200300
287

Forks +48 in 26 days

051015
14

Merged PRs 0 in 26 days

01

Trendshift Trendingwhere the repository ranked each day it made the top 25

Top 25 in C

481811
Oct 11Oct 15Oct 19Oct 23Oct 27Oct 31Nov 4
The left and right arrow keys step from day to day; Home and End go to the first and the last

Stars, forks, merged pull requests and Trendshift Trending rank, by day
DayStars gainedForks gainedMerged PRsC
Sun, Oct 11, 2026+0+00Not trending
Mon, Oct 12, 2026+0+00Not trending
Tue, Oct 13, 2026+287+140#4
Wed, Oct 14, 2026+73+70#8
Thu, Oct 15, 2026+19+20Not trending
Fri, Oct 16, 2026+14+30Not trending
Sat, Oct 17, 2026+5+10Not trending
Sun, Oct 18, 2026+8+10Not trending
Mon, Oct 19, 2026+9+00Not trending
Tue, Oct 20, 2026+4+00Not trending
Wed, Oct 21, 2026+5+20Not trending
Thu, Oct 22, 2026+2+00Not trending
Fri, Oct 23, 2026+4+00Not trending
Sat, Oct 24, 2026+3+00Not trending
Sun, Oct 25, 2026+1+00Not trending
Mon, Oct 26, 2026+1+00Not trending
Tue, Oct 27, 2026+1+10Not trending
Wed, Oct 28, 2026+1+00Not trending
Thu, Oct 29, 2026+3+00Not trending
Fri, Oct 30, 2026+38+50#18
Sat, Oct 31, 2026+40+20#11
Sun, Nov 1, 2026+14+50Not trending
Mon, Nov 2, 2026+15+20Not trending
Tue, Nov 3, 2026+12+20Not trending
Wed, Nov 4, 2026+11+10Not trending
Thu, Nov 5, 2026+13+00Not trending
Total+583+480
  • 1#1 on the list
  • 7Rank that day
  • Not trending

Mentions and achievements

Where people are talking about prabanta-dev/janas, and every badge it has earned
    1. Oct 161 post
      • HN 上 Janas 57 分,24 条评论。标题就一件事:模型比内存大也能跑,专家层扔 SSD,用到再读。 整模塞进 RAM 的跑法,内存不够直接起不来。Janas 内存里只留当前这几个专家,其余的放在 SSD 上。 流进来的是 experts,这摆明是冲着 MoE 模型设计的。一个 token 用不到全部专家,剩下的不用占着内存。 内存那关过了,下一关是 SSD…

        Read full on X, posted by @iluciddreaming

        @iluciddreaming · x.com

    2. Oct 142 posts
      • 显存装不下的模型从 SSD 读 Janas 让 MoE 专家按需从 SSD 载入,48 GB 的模型在 32 GB 内存的笔记本上跑到 37 token/s,llama.cpp 只有 2.8。

        Read full on X, posted by @aigeeknews

        @aigeeknews · x.com

      • 只有轻薄本也想跑 800 亿参数大模型?开源实验项目 Janas:权重放硬盘,推理时从固态硬盘"流式"加载,不用买几万块显卡。 作者 32GB 本实测:读提示 62 token/秒、生成 24 token/秒,比 llama.cpp 快几倍。 纯 C 零依赖。低成本玩大模型可围观,项目还很早期。项目放评论。

        Read full on X, posted by @TheHappyGe

        @TheHappyGe · x.com

    3. Oct 132 posts6 posts
      • Intersting things happening in MoE expert streaming. This weekend Strata was shuffling experts between GPU and RAM. Now, Janas, a pure-C, GPL engine that streams experts straight from an NVMe drive,…

        Read full on X, posted by @MattWolfgang

        @MattWolfgang · x.com

      • Janas:从 NVMe SSD 流式加载 MoE 专家,32GB 内存笔记本跑 80B 模型,读 62 tok/s,比 llama.cpp 快不少。思路不算新,瓶颈大概率在 SSD 带宽和缓存命中,长上下文 prefill 估计吃力,但想本地跑大模型的可以关注。#AI

        Read full on X, posted by @ZxqMain

        @ZxqMain · x.com

      • Intel D270 GPU AI Accelerator–Up to 480 GB LPDDR5X (

        Read full on X, posted by @betterhn50

        @betterhn50 · x.com

      • Intel D270 GPU AI Accelerator–Up to 480 GB LPDDR5X (

        Read full on X, posted by @betterhn20

        @betterhn20 · x.com

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

        Read full on Reddit

        r/hackernews

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

        Read full on Reddit

        r/hypeurls

    4. Oct 121 post
      • Janas: Run LLMs larger than RAM by streaming experts from SSD

        Read full on Hacker News

        Hacker News

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