# Run a 48GB model on a 32GB laptop: an Italian's pure-C inference engine hit HN on a single comparison number, and still only has 645 stars · GitHub open-source project

> Source: https://stringers.io/en/github/gh_prabanta-dev__janas.html (Stringers / 什么值得做, generated 2026-11-08)
> Stringers answers one question every day: what small product can one or two people build in a few months and actually make money from. Numbers come from 15 databases we collect daily, each with source and date.
> For agents: these verdicts and the underlying databases (verified revenue, App Store / Google Play charts, ad libraries, GitHub, Product Hunt…) are queryable live over MCP — tools today / hot / products / directions / direction / data. Setup: https://github.com/komako-workshop/stringers (the user logs in at stringers.io → "配置 agent" to get a personal URL). Site index: https://stringers.io/llms.txt

> The first line of the description reads "Run 48GB models on a 32GB laptop, 24 vs llama.cpp's 2.8 tok/s". Someone posted it to HN and it scored 221 points, and twenty-plus AI news sites reposted it. The author had earlier recruited testers on the HF forum with a one-click test script, building up a group of hardware enthusiasts who actually ran it.

[GitHub open-source project](https://stringers.io/en/github/index.md) · written 11-6

prabanta-dev/janas · prabanta-dev

# Run a 48GB model on a 32GB laptop: an Italian's pure-C inference engine hit HN on a single comparison number, and still only has 645 stars

**What it is** A local LLM inference engine written in pure C. MoE models stay on the SSD, and each step reads only the parts it needs, so a 32GB-RAM laptop can run a 48GB model.

**Why it caught on** The first line of the README reads "Run a 48GB model on a 32GB laptop, 24 vs llama.cpp's 2.8 tok/s." Someone else posted it to HN and it scored 221 points, and more than twenty AI news sites reprinted it. Earlier, the author had recruited testers on the HF forum with a one-click test script, which built up a group of hardware enthusiasts who actually ran it.

**C** **How you could follow** Don't build another engine that reads experts from SSD. What's missing is what sits between these engines: an open benchmark that compares llama.cpp, Strata, ds4, and Janas on the same machine, collects user-submitted results, and copies the cold-start playbook of "recruit testers + one-click script."

C-grade: the niche already has a dozen-plus similar projects in 2026 years. Strata leads with 40k stars, ds4 rides its author's name, and Janas, though solid, sits mid-pack. What's worth copying is the title style and the cold-start tactics, not building yet another engine. Whispers in small circles

Data (updated daily) · Repo created 2026-09-20 · 663 stars · 7 days +114

GitHub · stars

Data 2026-10-09 → 2026-11-08 (31 days, 30 observations). Points before 10-28 come from deeply / archive backfill, not every day has one

Janas is a local LLM inference engine written solo by Italian indie developer Maurizio Cammalleri. Models larger than RAM can run, with unused parts left on the SSD and read on demand. Repo created Sep 20, at 645 stars by Nov 6 (GitHub). What's worth studying isn't the star count but how cleanly it executed a few steps: one comparison number that gets reprinted verbatim, and a script that turned early users into testers. Its problem is also typical: same month, same idea, a neighboring project got 40k stars.

### Sep 25, a "call for testers" letter

In Sep 25 2026, the author posted on the Hugging Face forum under the title "Call for testers." The opening line: "I'm looking for people willing to spend half an hour, or a quiet evening, running Janas on their own Linux machine."

The reasoning was candid: the engine tunes its parameters based on what it measures, "but it has only been measured on one machine," his own Core Ultra 9 laptop with 32GB RAM. He named what he most wanted: AMD CPUs, machines with under 16GB RAM, SATA SSDs, and distros other than Debian.

Testing takes two commands: clone, then run `tools/janas-try.sh`. The script compiles, runs tests, downloads the model, verifies the SHA-256, benchmarks speed, and writes a report that **you review first** . Only if you say yes does it open a GitHub issue for you. There are three model tiers, the smallest 2.5GB, finishing in about fifteen minutes.

Two days later someone ran it on Colab and replied. By early November, the issue tracker had gathered more than 30 hardware reports and PRs from AMD, Intel, and NVIDIA users.

### What it is

Many open LLMs today use a MoE architecture: the model is huge, but each token only activates a small set of "experts." Janas exploits this. The whole model lives on an NVMe SSD, the experts needed for each step are read on the fly, and the frequently used ones stay in RAM.

Its GitHub description is just a line of numbers: "Qwen3-Next-80B (48GB) runs at 37 tok/s on a 32GB laptop, no draft 24, llama.cpp is 2.8." Pure C, depending on nothing beyond the C library, GPL licensed, with a terminal chat and an OpenAI-compatible API. In Nov 3 it added Ollama and Anthropic interfaces, so coding tools like Claude Code and Cline can connect directly.

### How the stars came

| Date | What happened | star |
|---|---|---|
| 09-20 | Repo created | — |
| 09-25 | The author recruits testers on the HF forum | — |
| 10-11 | The author posts "37 vs 2.8 tok/s" in the HF Show and Tell forum | — |
| 10-12 | Someone else (not the author) posts it to Hacker News: 221 points, 95 comments | dozens |
| 10-12 to 10-16 | More than twenty AI news and aggregator sites repost it almost simultaneously | 10-14 about 326 (screenshot from an iMasters report) |
| 10-16 to 10-30 | No new activity, gaining 0 to 10 per day | 412 → 471 |
| 10-29 | A release, adding support for the then-hot Qwen3.8-Flash-Next | 471 |
| From ¥10-31 | Starts climbing again: 10-31 +27, 11-01 +51, then a dozen or two a day after | 11-06 645 |

The one real jump came from a single HN front-page appearance. The poster had no connection to the author, and the title was "Janas: Run LLMs larger than RAM by streaming experts from SSD."

Over the next few days, aiweekly, dev.to, daily.dev, Brazil's iMasters, Italy's ilsoftware (headline emphasizing "an Italian's idea"), and Japanese and Korean tech sites all ran nearly identical pieces, all opening with the same set of numbers. An AI news account on Instagram made a short video, and the author's name was even rendered as "Prabanta Dey." Most of these sites were likely auto-monitoring the HN front page, not contacted by the author.

The second wave came in late October, right after a release that added support for a popular new model, so the timing lines up. I couldn't find the specific post that drove it (inference). No big accounts on X mentioned it. On Nov 3, someone on Reddit r/LocalLLaMA wrote a roundup of GPU + RAM + SSD inference engines, and it didn't list it either.

### Why it took off

**A number that can be copied as is.** "Run a 48GB model on a 32GB laptop" is understood by everyone, and "24 vs 2.8" makes the gap obvious. Aggregator sites don't need to understand the tech. Translating the README is enough to write a story.

**That HN front-page moment.** It's the only trigger that matches the star curve.

**Unusually serious testing.** Results were bit-identical across machines and thread counts, the model ships with checksums, and the README spells out measurement conditions and limitations in detail. Someone in the HN comments defended it, saying llama.cpp couldn't read SSDs this way in the short term. The people who stayed are the hardware enthusiasts who actually ran the tests and submitted reports.

**Near-daily releases.** From October 11 to November 4, 20 versions in total.

### Why only 645 stars

One reply in the HN comments was a single line: "Qwen 3? What year is it now? ... Strata already does this with Qwen3.8-Flash-Next."

Strata's repo was created 4 days after Janas, and it does the same thing, but bets on the hottest model of the time, targets discrete GPUs, and offers one-click install. It hit 4 on HN with 940 points, reached 40,493 stars by Nov 6, and gained 4,588 in the last 7 days. Janas initially only supported previous-generation models like Qwen3, was only tested on one Intel laptop, and its author doesn't merge outside code. The contributing guide says PRs will be merged only after a non-profit foundation is set up to hold the copyright, and the ARM and macOS PRs have been sitting open.

The niche was already crowded. The same comment thread said it plainly: "Every few weeks, someone stumbles on what llama.cpp has had for years again," and "There are many of these stream-experts-from-SSD projects now."

| Project | Scale (GitHub, 11-06) | Compared with Janas |
|---|---|---|
| Strata | 40.5k stars, 7 days +4.6k | Created the same week, bet on the hottest model + discrete GPUs + one-click install |
| antirez/ds4 | 24k stars | A DeepSeek 4 engine by the Redis author, also reads experts from SSD, rides the author's name |
| jundot/omlx | 23k stars | SSD-cached inference on Apple Silicon |
| ktransformers | 19.7k stars | CPU/GPU hybrid offloading, aimed at workstations |
| Edge0 | 9.1k stars | Prefetches using a trained router, featured on ModelScope's official site |
| flash-moe | 4.5k stars | Runs 397B on a laptop, Mac only |
| inferssd, BigMoeOnEdge, pulsar, and a dozen-plus others | 100 to 700 stars | The same idea surfaced 7 through October all at once |
| llama.cpp | 133k stars | The general-purpose engine everyone benchmarks against |

Its distinctive points are real: pure C with zero dependencies, it can use an Intel laptop's NPU to read prompts, and a custom format lets each expert be read in a single pass. But these decisions earn it serious attention, not star counts. An independent test ran it at 9.5 to 16.2 tok/s on a machine with 31GB RAM, so it works. The "nearly tenfold faster" comparison in the README has not been reproduced by anyone.

### If it were you

**C. Its cold-start tactics and title style are worth copying, but "yet another engine that reads experts from SSD" isn't worth following.** This niche already has a dozen-plus identical projects in 2026 years. The leaders are Strata at 40k stars and ds4, which rides its author's name. Janas, solid as it is, sits mid-pack.

**Who it's for** : developers who already know C/C++, memory and IO optimization, and want a hardcore track record in the local LLM community; or people with a small inference project who want to learn how to cold-start one.

**Who it's not for** : anyone hoping to pick a hot niche and collect thousands of stars in a few weeks. Stars in this niche track whichever model is hottest that week. You can't keep up with the leaders' release pace, and you don't have antirez's name.

**How to do it** : don't build the Nth engine; build what's missing between these engines. A commenter on the HN thread put it bluntly: "We need a guide that compares all these projects." An open benchmark fills that gap: one script, run on the same machine across llama.cpp, Strata, ds4, Janas, Edge0, broken down by RAM size, GPU presence, and SSD type, with user-submitted results. Janas's `janas-try.sh` is a single-project version of the same idea. Another route is covering platforms nobody handles, such as one-click setups for Windows or ARM laptops (inference: Janas only supports x86 Linux, and Strata targets discrete GPUs).

**How to get users** : follow Janas's sequence. Open the README with a "my X vs their Y" number plus an everyday scenario. Recruit testers on the HF forum and r/LocalLLaMA, offer a script that finishes in one command and generates a report automatically, and let early users cover your hardware gaps. Once the numbers hold up, then pitch HN. Support each new hot model the same week and spell it out in the release notes.

**When to move** : for a benchmark, the earlier the better. Now is when these projects have just appeared and nobody has organized them yet. Once llama.cpp or Ollama ships SSD expert reading upstream, or Strata pulls ahead alone, the need for a comparison fades (inference).

**Signs you've misread it** : ① someone has already published a comparison table that r/LocalLLaMA pins or cites often, and the gap is gone; ② llama.cpp's main branch adds similar streaming reads at near-equal speed, and the whole niche cools off together; ③ mid-pack projects like Janas drop back to single-digit daily gains by mid-November, which would mean this wave of stars followed one hot model, and building add-ons for them wouldn't get attention.

---

Data & sources: GitHub repo, release history and issues (11-06), daily star counts (10-16 to 11-06); Hacker News post 50063717 (10-12) and comments; Hugging Face forum posts 180740 (09-25) and 190340 (10-11); coverage and reviews on iMasters (10-14), aiweekly, ilsoftware, ReviewRamble; Strata's HN post and competitor star counts (GitHub, 11-06). Places marked "inferred" are estimates.

[GitHub repo ↗](https://github.com/prabanta-dev/janas)

**Research notes: competitor table, acquisition evidence, full research report**

Competitors

| Name | Where | Launched | Scale | How it differs from this one |
|---|---|---|---|---|
| Niko1221/Strata | Open source | 2026-09-24 | 40.5k stars, +4.6k in 7 days; HN 940 points | Rode the hottest model at the time, Qwen3.8-Flash-Next, targeting discrete GPUs with one-click install; Janas targets CPU-only laptops and initially supported only older models |
| ggml-org/llama.cpp | Open source | 2023-03 | 133k stars | General-purpose engine; when a model exceeds memory it relies on mmap and runs slowly. Janas used it as the speed benchmark |
| Edge0-AI/Edge0 | Open source | 2026-09-08 | 9.1k stars | Uses a trained router to prefetch ahead, so a 35B model needs only 2.5GB of RAM; backed by the ModelScope official account |
| antirez/ds4 | Open source | 2026-05 | 24k stars | A DeepSeek 4 engine built by the Redis creator, supporting Metal / CUDA / ROCm, riding the author's reputation |
| danveloper/flash-moe | Open source | 2026-03 | 4.5k stars; HN 398 points | Mac only; runs 397B on a laptop |
| kvcache-ai/ktransformers | Open source | 2024-07 | 19.7k stars | CPU/GPU heterogeneous MoE offloading, aimed at workstations and servers |
| Ollama / LM Studio | Other | 2023 | Ollama 186k stars | An easy entry point for trying it out; doesn't specifically optimize MoE models that exceed memory |

Where users come from

- **Verified** **Hacker News** : posted by third-party account khalidkhair on 2026-10-12 (hackernews library id 50063717), 221 points / 95 comments; stars later climbed past 300+

- **Verified** **AI news aggregators / auto-generated content sites reposting** : between 10-12 and 10-16, more than twenty sites including aiweekly, ai-tldr, byteiota, dev.to, daily.dev, iMasters, ilsoftware, oflight, braindetox reposted it simultaneously, plus Instagram @gradientai.news and YouTube Shorts videos

- **Verified** **Author cold-starts on the HF forum** : 09-25 posted a Call for testers (with a one-click test script), and 10-11 posted in Show and Tell comparing 37 vs 2.8 tok/s

- Inferred **Reddit r/LocalLLM** : there is a post 1xivl25, but its date and engagement couldn't be retrieved

- Inferred **Following hot model releases** : 10-29 release added support for Qwen3.8-Flash-Next / Nemotron, 11-03 added Ollama and Anthropic API support; 10-31 and 11-01 star counts rose by +27 and +51 respectively. The timing lines up, but I couldn't identify which specific post drove it

## Why prabanta-dev/janas (Janas-LLM) actually runs

> Channels: GitHub | Research day 2026-11-06 | Data: our github / hackernews libraries, GitHub API, Jina/Exa/Brave web search

### One line

A local inference engine written in plain C (depending only on the C library) by Italian indie developer Maurizio "camauri" Cammalleri: MoE model weights stay on NVMe, and the experts each token needs are read on demand with O_DIRECT + io_uring, with hot ones kept in memory. The selling point is a set of highly shareable comparison numbers: "a 32GB laptop running a 48GB Qwen3-Next-80B at 24 tok/s (with an MTP draft model 37), where llama.cpp manages only 2.8."

**Takeaway** : it's a minor player in the "stream experts from SSD" wave, not a leader. The growth came from 2026-10-12 third-party post to HN (221 points / 95 comments), after which a large number of AI news aggregators and auto-generated content sites reworked the README's headline numbers. Growth accelerated again after 10-31, timed right after the 10-29 release (which added support for the hottest model at the time, Qwen3.8-Flash-Next). In the same month, the competing project Strata reached 40k stars, while Janas had 645. So the answer to "why did it take off" is: a headline with strong numbers, an HN front-page placement, aggregator reposts, and rare documentation and testing rigor that attracted hardware enthusiasts who actually ran the tests. It hasn't broken out beyond that niche.

### 1. Data

- Stars: 2026-09-20 repo created; 10-14 iMasters screenshot shows 326 stars / 20 forks; 10-16 to 412 (our github repo_day); 10-16 to 10-30 gained only 0–10 per day, reaching 471; 10-31 +27, 11-01 +51, then +11~26 per day afterward; the 11-06 card shows 630, and the GitHub API live shows 645 stars / 62 forks. gain_7d 159, the prior week 14 (github library).

- The card's max_day_gain of 258 is recorded on 10-14, corresponding to the HN wave (our daily records 10-28 only began then, so this figure is the difference between two observations).

- Author account camauri: registered in 2015, 24 followers, plus two small retro BASIC / SID audio projects (documented on Substack). No existing audience to speak of.

- All 96 commits are by the author alone; on HF there are 14 pre-converted JNS model repos (huggingface.co/prabanta-dev).

### 2. Competitors (same goal: running MoE models larger than memory/VRAM on consumer hardware)

| Product | Form | Launched | Scale (github repos, 2026-11-06) | How it differs from Janas |
|---|---|---|---|---|
| llama.cpp | Open source | 2023-03 | 133k stars | General-purpose engine; mmap can run models larger than memory but slowly. Janas uses it as a baseline, claiming 2.8→24 tok/s |
| Ollama / LM Studio | Open source / app | 2023 | Ollama 186k stars | An easy entry point for trying it out; doesn't specifically optimize MoE models that exceed memory |
| **Niko1221/Strata** | Open-source C++ | 2026-09-24 (4 days after Janas) | **40,493 stars, +7 in 4,588 days** | Focused on the hottest model of the time, Qwen3.8-Flash-Next (125B), running at 100 T/s on an RTX 4090, one-click install, OpenAI/Anthropic API. 10-04 points / 940 comments on HN 420. On Reddit it was called the "current belle of the ball." |
| Edge0-AI/Edge0 | Open-source Python | 2026-09-08 | 9,088 stars | "35B MoE needs only 2.5GB of RAM," using a trained router to prefetch; the ModelScope official account promoted it on X |
| antirez/ds4 | Open-source C | 2026-05 | 24k stars | A DeepSeek 4 local engine built by the Redis creator, also using SSD-streamed expert caching, riding the author's reputation |
| jundot/omlx | Open source | 2026-02 | 23k stars | An SSD-caching inference service on Apple Silicon |
| danveloper/flash-moe | Open source | 2026-03 | 4.5k stars | Runs 397B on a laptop, Mac only; 398 points on HN |
| ktransformers | Open source | 2024-07 | 19.7k stars | CPU/GPU heterogeneous MoE offloading, aimed at workstations and servers |
| Helldez/BigMoeOnEdge, wici-ai/inferssd, giannisanni/pulsar, Xolotl, moe-l2, hummingbird, and others | Open source | 2026-07~10 | 100~700 stars | Different implementations of the same idea, mostly built on llama.cpp or targeting a single model |

Original HN comments: "There are many of these stream-experts-from-SSD projects now" and "Every few weeks, someone stumbles on what llama.cpp has had for years again." This niche has been crowded for 2026 years, and Janas sits in the middle-to-lower tier.

### 3. Where users came from (growth tactics)

1. **The author cold-started on the HF forum** : 09-25 posted a "Call for testers" with a one-click test script `tools/janas-try.sh` that generates a report automatically and lets users open a GitHub issue directly; 10-11 then posted "37 vs 2.8 tok/s" in HF Show and Tell. (discuss.huggingface.co posts 180740, 190340)

2. **HN front page** : 10-12 22:05 UTC, third-party account khalidkhair posted "Janas: Run LLMs larger than RAM by streaming experts from SSD"; 221 points / 95 comments (hackernews library, id 50063717). This was the only major spike; stars jumped from a few dozen to 300+.

3. **Aggregator / AI-generated news reposts** : between 10-12 and 10-16, at least 20 sites wrote about it at nearly the same time, including aiweekly, ai-tldr, byteiota, dev.to (2 posts), daily.dev, iMasters (Brazil), ilsoftware.it (Italy, headline emphasizing "an Italian's idea"), oflight (Japan), braindetox (Korea), 9to5ai, top10.dev, and others; Instagram @gradientai.news and YouTube Shorts made short videos, and the author's name was misspelled as "Prabanta Dey." These are most likely automated HN-tracking content sites, not paid placements.

4. **Reddit r/LocalLLM** : One post is "Janas: a C inference engine that runs MoE models bigger than your RAM" (post 1xivl25). The exact date and engagement couldn't be pulled (Reddit 403). But in the r/LocalLLaMA roundup "GPU+RAM+SSD inference engines" (11-03), the focus is Strata, and Janas isn't mentioned.

5. **Acceleration again from 10-31** : the 10-29 release added Qwen3.8-Flash-Next, NVIDIA Nemotron 3.5 Bolt, and image and Whisper support; 11-03 added Ollama and Anthropic API, so it can plug into coding agents like Claude Code and Cline; 11-04 added AVX-512. 10-31 and 11-01 rose by +27 and +51 respectively, matching the release timing, but I couldn't find which specific post drove it (**unverified** ). ReviewRamble 10-31 published a multilingual review page, and sdad.pro 11-02 wrote up real-world SATA drive tests.

6. On X: the x-radar corpus found no discussion of Janas in nearly 60 days. No major accounts have reposted it.

### 4. Why it took off

- **The headline is a set of comparison numbers** : the GitHub description reads "37 tok/s… vs 2.8 for llama.cpp," and it picks the everyday scenario "a 32GB laptop running a 48GB model," which makes it easy for aggregators to copy as-is.

- **HN front page at 221 points** : this is the only event that lines up with the big jump in stars (10-12 to 10-14; iMasters screenshot shows 326 stars).

- **Rare testing rigor attracted users who submitted reports** : it ships a one-click test script, results are bit-identical across different machines, the HF repo has pre-converted models with SHA-256 checksums, and the README thoroughly documents measurement conditions and limitations. After 10-13, 30 or more AMD / Intel / NVIDIA user hardware reports and PRs followed (see the issues list). These are the people who stuck around.

- **Sustained rapid releases** : 10-11 to 11-04 saw 20 releases in total, nearly one a day; 10-29 kept up with the hottest model, Qwen3.8-Flash-Next, after which growth picked up again.

### 5. Differentiation, and whether that's why it took off

- Pure C, zero dependencies, GPL; positioned as "CPU on an ordinary laptop," with iGPU / NPU optional, using the Intel Core Ultra NPU to read the prompt, which is rare in this category; a custom JNS format lets each expert be read in one aligned pass; cache-conditional routing skips lightweight experts that aren't cached; uses an MTP draft model for speculative decoding; testing and documentation are very rigorous.

- These gaps are why it got approval on HN (one commenter defended it: "llama.cpp can't read from SSD at this speed anytime soon"), but **not enough to beat its peers** . Strata caught the "hottest model + discrete GPU + one-click install" combo and hit 40k stars; Janas initially only supported "old" models like Qwen3 / Qwen3-Next, got mocked on HN with "Qwen 3? What year is it", was only tested on a single Intel laptop, and the author wasn't accepting outside code (CONTRIBUTING states PRs will be merged only after a non-profit foundation holds the copyright). ARM64 / macOS / AVX-512 PRs have been sitting open, and ReviewRamble flagged this too.

- An independent third-party test: a 31GiB-RAM machine ran a 80B model at 9.5–16.2 tok/s, so it works; but nobody has reproduced the 26.4 vs 3.0 comparison (ReviewRamble).

### 6. Visibility

It's circulating in small circles: it hit the HN front page once and was reposted by a batch of aggregator sites. No big accounts on X mentioned it, and the Reddit roundup threads didn't list it. Compared with Strata, almost nobody knows about it. There was a one-off media burst, but it didn't turn into word of mouth.

### 7. Copycats

Not a copycat. Over the same period there were a dozen-plus projects with the same idea (inferssd 10-22, Betelgeuse 10-31, Infernix 10-08, etc.). They surfaced together, not by copying Janas.

### 8. Takeaways

- The GitHub description leads with hard numbers in the form "me X, rival Y", plus an everyday scenario (a 32GB laptop), which aggregators copy verbatim.

- "Call for testers + one-click test script + auto-generated issue": this turned cold-start users into hardware-coverage testers and brought activity to the issues tab.

- Catching the hottest model that week mattered: Strata and Janas were built the same week, one bet on the hottest model, the other ran an older one, and the star count differed by 60x.

### 9. Uncertain

- The specific source of the 10-31 / 11-01 acceleration (a Reddit post? a release announcement?) hasn't been found.

- The date and popularity of the Reddit r/LocalLLM post.

- Daily star counts before 10-12 (the GitHub stargazers API returns 404 for this repo).

### Sources

- GitHub: [github.com/prabanta-dev/janas](https://github.com/prabanta-dev/janas) (README, releases, commits, issues, scraped 2026-11-06)

- HN: [news.ycombinator.com/item?id=50063717](https://news.ycombinator.com/item?id=50063717); Strata's HN post id 49953495; flash-moe's HN post id 47476422

- HF forum: [discuss.huggingface.co/t/180740](https://discuss.huggingface.co/t/180740), [discuss.huggingface.co/t/190340](https://discuss.huggingface.co/t/190340)

- iMasters: [imasters.com/news/janas-runs-ai-models-larger-than-ram-strai…](https://imasters.com/news/janas-runs-ai-models-larger-than-ram-straight-from-ssd) (10-14, screenshot 326 stars)

- aiweekly:[aiweekly.co/alerts/janas-pure-c-engine-streams-qwen3-80b-fro…](https://aiweekly.co/alerts/janas-pure-c-engine-streams-qwen3-80b-from-nvme-at-37-toks)

- ilsoftware:[www.ilsoftware.it/janas-llm-ai-80-miliardi-parametri-pc-32-g…](https://www.ilsoftware.it/janas-llm-ai-80-miliardi-parametri-pc-32-gb-ram/)

- ReviewRamble:[www.reviewramble.com/github-repos/janas](https://www.reviewramble.com/github-repos/janas)

- r/LocalLLaMA roundup (paraphrased): [pulse.ecneproject.com/articles/your-3090-ti-just-got-a-lot-m…](https://pulse.ecneproject.com/articles/your-3090-ti-just-got-a-lot-more-useful-a-roundup-of-gpu-ram-ssd-inference-engin-342f96)

- Our github repo_profile / repo_day; hackernews repo story

This article was written by AI from public data and the data we collect daily. Numbers are sourced, and inferences are labeled.