# Running a 8-billion-parameter model on 27.8 GB of RAM: how a C project that "runs but is useless" got 10k stars · GitHub open source

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> After Kimi K3 released its weights, everyone said "nobody can run this." 5 days later it posted the most extreme number yet, "runs on a 8 GB laptop," and a batch of AI newsletter accounts on X reposted it as-is. The November K3.1 Pro release then put it back on GitHub's trending chart for another wave.

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

FareedKhan-dev/kimi-k3-in-c · FareedKhan-dev

# Running a 8-billion-parameter model on 27.8 GB of RAM: how a C project that "runs but is useless" got 10k stars

**What it is** A small program written in pure C that streams the 1.56 TB Kimi K3 model from disk and runs it on an ordinary computer with 8 GB of RAM. It produces about one token every half minute.

**Why it blew up** After Kimi K3's weights were released, everyone said "nobody can run this." Within 5 days this project offered the most extreme answer, "runs on a 8 GB laptop," which a batch of AI news accounts on X reposted verbatim. When Kimi K3.1 Pro came out in November, the repo climbed GitHub trending again.

**B** **How you could ride this** Don't copy K3 specifically. Instead, prepare general-purpose streaming inference code ahead of time, and within 5 days of the next huge open-weight model's release, ship a "squeeze it into a phone / Raspberry Pi / browser" demo, with a long write-up and real benchmark data, giving news accounts a ready-made contrast line.

Grade B: the playbook is clear and reusable for every large model release, and the star payoff is good. But the K3 slot is already taken by this project, warp, and deltafin; the window lasts only about a week; and you have to produce an engine that actually runs. The author's existing followers and long-form channel helped a lot. Whispers in niche circles

Data (updated daily) · Repo created 2026-08-01 · 11,194 stars · 7 days +1,931

GitHub · stars

Data 2026-08-02 → 2026-11-08 (99 days, 32 observations). Points before 10-28 come from deeply / archive backfill, not every day has one

A 176 KB C program, on a machine with 8 GB of RAM, took 261 seconds to say "Paris." It is barely usable for real work, yet it picked up 1,700 more stars than Kimi K3's official repo. Below: what it hit, where the stars came from, who did the same thing first, and whether you can repeat it next time.

### 4 min 21 s, 8 tokens

The README includes a terminal log: author Fareed Khan fed the program "The capital of France is" and asked it to continue for 8 tokens. The program read the model layer by layer from an NVMe drive, computing each layer as it arrived, and printed:

> Paris.", 8 tokens in 261.5 s, 32.69 s/token average PEAK RSS for the whole run: 8.24 GB

The one being run is Kimi K3, the 27.8B-parameter model Moonshot AI released in Jul 27, 1.56 TB on disk. That week, the thing people were arguing about on Hacker News (the K3 weights post, 1,382 points) was the same issue: the biggest open model, and almost nobody can run it. The author made this repo on Aug 1, and the first line of the README reads: "27.8B parameters. One CPU. 8 GB of RAM." The next line: "No BLAS, no framework, no GPU."

By Nov 6, the repo had 10,719 stars and 1,672 forks (GitHub, 11-06). Moonshot's official Kimi-K3 repo had 9,043.

### What it actually is

An inference engine written in pure C99, the whole program 176 KB. K3 is a mixture-of-experts model; the 1.45 TB of "experts" never sit in memory all at once. They are read and computed on each step. More RAM means more stays cached, and things get faster. The README's numbers:

| Machine | RAM | Per token |
|---|---|---|
| Ordinary laptop | 8 GB | 26.5 s |
| High-end laptop | 32 GB | 24.2 s |
| Desktop | 64 GB | 19.8 s |
| Heavy workstation | 128 GB and up | 5.6 s |

A line that gets quoted repeatedly: going from 8 GB to 224 GB, the output is byte-identical; only the time changes.

Put simply, it proves something is possible, but nobody will use it to get work done: you have to download 1.56 TB of weights, leave roughly 1.7 TB of disk free, and then wait half a minute per word. People who really want to use K3 go to hosted APIs like Together AI or Modal. Most of these 10k stars are spectators, not users (my inference).

### Why this project got the stars

**It's not the first.** Back in Jul 28, sqliteai/warp and deltafin were already doing "running K3 on consumer hardware": warp's HN title was "Running Kimi K3,0.5 at 29 GB RAM, token/s" (341 points), and deltafin later hit "1 token/s on a MacBook Pro, reading from four SSDs" (279 points). Both came before it and both are faster. warp now has 2,537 stars, deltafin 857.

**Its numbers are the most extreme.** Everyone else talks about how fast it runs. This one squeezes memory down to 8 GB, a typical laptop's worth of RAM, then pairs it with 176 KB and 0 GPU. Those four numbers together are already a tweet that can be reposted as-is. Between Aug 3 and 8, a batch of AI news accounts on X posted almost the same line about it: "A 27.8B-parameter model, one CPU, 8 GB of RAM, no GPU, 100% open source" — Shubham Saboo and Hasan Toor 8-03, DataChaz 8-04, RoundtableSpace and the Chinese account MaxForAI 8-07, Kirill 8-08. No sign of paid promotion; it looks more like news accounts passing it around among themselves. In August alone this wave pushed it to roughly 9,000 stars.

**The packaging was ready-made, and the author already had an audience.** The repo homepage links to a Medium article the author marked as a 94-minute read. The README includes a SpongeBob-style architecture diagram, and every number matches the measured files in `docs/data/`. Fareed Khan has 3,126 GitHub followers and 52 repos with 38k stars in total; in his earlier "from scratch" series, train-llm-from-scratch has 13k stars. Midway through the README sits a line: "I'm looking for AI research roles and PhD opportunities," with a CV attached. For him, this project is a portfolio.

Hacker News didn't really help: three submissions, 4, 2 and 2 points.

### Why it jumped again in November

In mid-October it was gaining only ten or so stars a day. In Nov, +3 71,4 day, +452,5 day, +383,6 day, 538: about 1,488 in one week, compared with 74 the week before (GitHub, 11-06).

The code hasn't been pushed since Oct 2, so this wave wasn't caused by any change the author made. The timing lines up with Kimi K3.1 Pro: it was announced as open-sourced in Oct 23, and in Nov 4 the weights landed on Hugging Face (HN 343 points), and "how to run Kimi locally" became a hot search. Once it started rising, by Nov 5 it reached #11 on GitHub's all-language daily trending and #1 on C daily trending. Then 20 more reposts and explainers appeared in various languages, mostly from sites that auto-generate content from trending lists. Trending brought reposts; reposts brought stars. I couldn't pin down which single post set it off. Nor has anyone verified whether it actually supports the K3.1 Pro weights.

A side effect: starting in Nov 4, a batch of external PRs came in. Some people submitted a dozen bug fixes in a day, and someone added a Termux/Android route. The README lists 20 contributors, and the macOS, Windows, and ARM ports were all done by others.

### Who else is doing the same thing

| Project | star | How it differs |
|---|---|---|
| MoonshotAI/Kimi-K3 official | 9,043 | Needs a GPU cluster |
| sqliteai/warp | 2,537 | Earlier and faster (29 GB, 0.5 token/s), supports multiple models |
| deltafin | 857 | Aimed at Mac, 1 token/s, with an OpenAI-compatible API |
| antirez/ds4, flash-moe, slotstream | 24.2k / 4.5k / 652 | Same idea of streaming a large MoE from SSD, for other models |
| llama.cpp | 133k | General-purpose, can also mmap models larger than RAM, but not built specifically for K3 |

The "stream giant MoE from SSD" approach already had several projects 2026, and antirez's ds4 has 24k stars. On the K3 model specifically, the niche is already taken.

### If it were you

**My take:** Building "K3 running on X" again adds nothing. What's worth learning is the playbook: within a week of a big model's weights dropping, ship a "stuffed into impossible hardware" demo, and title it with one contrast number. The playbook works once per big model release, and each time you only get a week.

**Who it suits:** People who can write low-level inference code (C/C++/Rust, quantization formats, memory mapping, SIMD), ideally with some habit of writing long-form pieces and a few followers already.

**Who it doesn't suit:** People who can't produce an engine that actually runs. Stars on projects like this depend entirely on "it really ran." The terminal output in the README, with timings and peak memory, is the whole credibility. If the numbers are fudged, the first person who reproduces them will expose you.

**How to do it:**

- Pick your next target now: a huge open model about to release weights. Write the general parts ahead of time (streamed weight reading, 4-bit expert matmul, tokenizer), so when the weights land you only have to adapt.

- Headline on even more extreme hardware: phone, Raspberry Pi, browser (WebGPU), a used ThinkPad. The number has to compress into one line, like "2.78T / 8 GB / 0 GPU."

- Or fill the gap this one left: it's slow, only supports K3, and hasn't been updated since October. The first repo that clearly shows "K3.1 Pro runs on 8 GB" can catch the current search wave directly.

**How to get seen:** Ship three things together: the repo, a long write-up (explaining where every byte of memory goes), and a directory of raw benchmark output. The README opens with four big numbers and a dated terminal screenshot. Then make it easy for AI news accounts on X to repost: hand them a ready-made line and an image. HN may not help; this project never topped 4 points there.

**When to be early:** Within 5 days of the weights being released. This repo was created 5 days after K3's release, 4 days behind warp, and made up for it with more extreme numbers. Any later and the topic has moved on.

**How to tell you misread it:** A week after the next big model drops, two or three repos are already running it on X hardware with even bigger numbers than yours. Or you ship it, 3 days go by, nobody reposts the news, and stars stall at a few hundred. Either way, this time the contrast wasn't strong enough, or people are already tired of this kind of demo.

[GitHub repo ↗](https://github.com/FareedKhan-dev/kimi-k3-in-c)

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

Competitors

| Name | Where | Launched | Scale | How it differs from this one |
|---|---|---|---|---|
| MoonshotAI/Kimi-K3 official + vLLM/SGLang | Open source | 2026-07-27 | 9,043 star | Official route needs a GPU cluster; this one targets a single CPU with 8 GB |
| sqliteai/warp (formerly waste) | Open source | 2026-07-28 | 2,537 stars; HN 341 points | Earlier and faster (29 GB, 0.5 tok/s), multi-model and embeddable, but the numbers aren't extreme enough |
| deltafin(gavamedia / argonautlabsai) | Open source | 2026-07-28 | 857 stars; HN 138 points + 279 points | Aimed at Mac, 1 tok/s on four SSDs, with an OpenAI-compatible API, practical in focus |
| llama.cpp | Open source | 2023-03 | 133k star | General-purpose ecosystem, can mmap models larger than RAM, not built specifically for K3 extreme memory |
| ktransformers | Open source | 2024-07 | 19.7k star | Hybrid CPU/GPU runs large MoE, needs a GPU and lots of RAM |
| antirez/ds4, flash-moe, slotstream | Open source | 2026 | 24.2k / 4.5k / 652 star | Same SSD-streaming MoE approach, but for other models |
| Together AI / Modal / Telnyx and other hosted K3 APIs | Other | 2026-07 | Big | Anyone who actually wants to use K3 goes through a cloud API; this is just a demo |

Where users come from

- **Verified** **Reposted by big X AI news accounts** : Shubham Saboo, Hasan Toor 8-03, DataChaz 8-04, RoundtableSpace and MaxForAI 8-07, Kirill 8-08, with nearly identical copy

- **Verified** **Audience: a Medium long-form post plus fans of the author's build-from-scratch series** : levelup.gitconnected published a 94-minute long read 8-03, and the repo homepage points to it. The author's other repos total about 28k stars.

- **Verified** **Platform events** : K3 weights released 7-27, repo created 8-01; K3.1 Pro weights released 11-04, second wave starting 11-03

- **Verified** **GitHub Trending + auto-generated content sites** : 11-05 hit #11 on the all-language daily chart and #1 on the C chart; from 11-01 to 11-06 there were 20+ reposts (readmex, buildtube, tokenstrom, explainx, etc.)

- Inferred **HN / Reddit** : three HN submissions peaked at 4/2/2 points; there's a post on r/LocalLLaMA, but its traction is unverified

## FareedKhan-dev/kimi-k3-in-c: why it works

In one line: a pure-C176 inference engine in 99 KB that streams Kimi K27.8 (a 3-billion-parameter model, 1.56 TB on disk) from NVMe and runs inference on a single CPU with 8.24 GB peak memory (about 8 seconds per token on a 26.5 GB machine).

### Verdict

It's an **extreme-engineering demo riding the biggest platform news** , not a tool people use day to day. The two waves of traction each had their own trigger:

1. **First wave (early 2026-08, 0 → about 9,000 stars)** : Kimi K3 weights went up on Hugging Face 7-27 (HN 1,382 points), and everyone was talking about 'the biggest open model, but nobody can run it'. The author created the repo 8-1 and on 8-3 published an 94-minute Medium post. From 8-3 to 8-8 a batch of AI news accounts on X (Hasan Toor, Shubham Saboo, DataChaz, 0xMarioNawfal/RoundtableSpace, Kirill, the Chinese-language MaxForAI, etc.) reposted it with almost the same line: '27.8 billion-parameter model, one CPU, 8 GB of RAM, no GPU, 100% open source'. HN itself did little (three submissions with 4, 2, and 2 points).

2. **Second wave (from 2026-11-03, daily growth jumped from about 10 to 287/452/383/538)** : it lines up with the open weights release of Kimi K3.1 Pro (announced 10-23, posted on HF 11-04, HN 343 points, peaking at #6 on the front page). After the surge it hit #11 on GitHub's all-language daily chart and #1 on the C daily chart on 11-05, which drew a large batch of automated or semi-automated content sites (readmex, buildtube, tokenstrom, freshweights, earlysignal, explainx, and Chinese, Korean, Italian and Spanish reposts) plus an Arabic YouTube video, forming a self-amplifying loop of trending chart → content sites → trending chart. The code hasn't been pushed since 10-02, so this wave wasn't driven by product updates.

Its differentiation is **extreme in how it's framed** . Similar projects (sqliteai/warp at 29 GB, deltafin running on an M1 Max with four SSDs) talk about how fast it runs. This one squeezes the number down to '8 GB on an ordinary laptop' and stresses 'zero dependencies, 176 KB, and 8 GB and 224 GB outputs that are byte-for-byte identical'. That contrast of 'smallest number, biggest model' is itself a line people can repost, and it's the main reason it has the most stars in its category (10,719, more than the 3 repo official to Moonshot's Kimi-K9,043). Practical value is weak: 26.5 s/token, and you need 1.7 TB of free disk.

### Data

| Metric | Value | Source |
|---|---|---|
| star / fork | 10,719 / 1,672(2026-11-06) | Our GitHub repo_day |
| Repo created | 2026-08-01; release v0.1.0 8-02, v1.0.0 8-07, v1.1.0 10-02 | GitHub API |
| Already had by 10-17 | 9,070 stars (first wave already completed in August) | Data card daily |
| past 7 days | +1,488 (+7 over the first 74 days) | Data card |
| Daily adds | 11-03 +71→11-04 +452, 11-05 +383, 11-06 +538 | repo_day |
| trending | 11-05 all-language daily chart #11, C daily chart #1, C weekly chart #2; 11-06 C daily chart #2 | GitHub repo trending |
| Latest push | 2026-10-02; from 11-04 a batch of external PRs appeared (rudycelekli submitted more than a dozen fix PRs in one day, plus a Termux/Android guide) | GitHub API |
| Author | Fareed Khan, a GitHub user with 3,126 followers, 52 repos with 38,576 stars in total; also the from-scratch series train-llm-from-scratch (13,144) and all-agentic-architectures (4,677); a long-time Medium writer (levelup.gitconnected) | GitHub API / our repo tracking |

### Competitors

| Name | Form | Launched | Scale | How it differs |
|---|---|---|---|---|
| Official MoonshotAI/Kimi-K3 repo + vLLM/SGLang deployment | Open source / big companies | 2026-07-27 | 9,043 star | The official route needs a GPU cluster; this one's pitch is a single CPU with 8 GB |
| sqliteai/warp (formerly waste) | Open-source C engine | 2026-07-28 | 2,537 stars; HN 7-31 'Run Kimi K3 using 29 GB of RAM at 0.50 tok/s' with 341 points | Earlier, faster (0.5 tok/s), supports multiple models, embeddable; but its headline number is 29 GB, which isn't as striking a contrast |
| gavamedia/deltafin(argonautlabsai/deltafin) | Open source | 2026-07-28 | 857 star; HN 7-28 138 points, 9-08 "1 token/s on a MacBook Pro, streamed from four SSDs" 279 points | Aimed at Mac, with an OpenAI-compatible API, and pitches practical speed |
| llama.cpp(GGUF + mmap) | Open source | 2023 | 133k star | General-purpose, the largest ecosystem; can mmap models larger than RAM, but not built specifically for K3 |
| ktransformers | Open source | 2024 | 19.7k star | Hybrid CPU/GPU runs large MoE, needs a GPU and lots of RAM |
| antirez/ds4, danveloper/flash-moe, carloslfu/slotstream | Open source | 2026 | 24.2k / 4.5k / 652 star | Same 'stream large MoE from SSD' approach, aimed at other models |
| 'From-scratch PyTorch' implementations of K3 such as TimRots/kimi3 | Open source | 2026-08 | Small | Educational, not an inference engine |
| Hosted APIs from Together AI / Modal / Telnyx and others | Cloud services | 2026-07 | Big | Anyone who really wants to use K3 goes here |

Bottom line: the same goal (running K3 on consumer hardware) already had warp and deltafin ahead by late July, and they were faster. This one entered 4 days later and took the most attention with the more extreme '8 GB' number and the long-form narrative.

### Growth tactics (evidence)

1. **Synced reposts from AI news accounts on X (verified, the main driver of the first wave)** : Shubham Saboo 8-03, Hasan Toor 8-03, DataChaz 8-04, 0xMarioNawfal (RoundtableSpace) 8-07, MaxForAI (Chinese) 8-07, Kirill 8-08, MIKE 8-17, the_osps 9-09. The copy is highly consistent (Brave search of x.com results). No evidence of paid promotion; it looks more like news accounts passing it along to each other.

2. **Author's own content channel (verified)** : a 94-minute long read on Medium/levelup.gitconnected (8-03), with the repo homepage linking straight to it; the author has a longstanding following from a series of from-scratch 'viral' repos. The README includes 'seeking AI research roles / PhD' and a CV, which shows the motive is personal branding.

3. **Platform events (verified timing match)** : K3 weights released 7-27 → repo created 8-01; K3.1 Pro weights released 11-04 → second wave starts 11-03.

4. **GitHub Trending + auto content site amplification (verified)** : 11-05 hit the all-language daily chart; from 11-01 to 11-06 there were 20+ reposts and explainers in various languages (Exa search), mostly from auto-generated sites driven by trending charts.

5. **HN / Reddit (small effect)** : the three HN submissions peaked at 4 points; there's a post on r/LocalLLaMA (the content was blocked by 403, traction unverified).

6. **YouTube** : an English 'KIMI K3 in C' video and an Arabic video from early November; view counts unverified.

### Why it took off

- Timing: it rode the collective mood of 'the largest open-weight model ever, but nobody can run it' (the HN K3 weights thread at 1,382 points, and The New Stack's 'few can run it').

- The one-line contrast copy: 2.78T parameters / 8 GB / 176 KB / 0 GPU, which is naturally suited to news-account reposts.

- The author is a long-time writer of 'from-scratch' content with a Medium long read, diagrams (a SpongeBob architecture chart) and a directory of measured data, so the credibility packaging is complete.

- The second wave rode search demand from the Kimi K3.1 Pro launch and compounding from GitHub trending.

### Differentiation, and whether it was the cause

The differentiation is 'the most extreme number + zero dependencies, portable, with a reproducible byte-identical narrative', not speed or ease of use. That differentiation (not practicality) is why it got the most stars despite entering after warp and deltafin, so it **is the cause** ; but it also means stars don't equal use: people who actually run it need 1.56 TB of downloads and 26 seconds per token.

### Visibility

It was circulating in small circles early on, and the second wave was widely reposted: in August, a batch of AI news accounts on X with hundreds of thousands of followers (follower counts not checked individually) posted it; in November there were 20+ multilingual articles. There's no revenue (open source, Apache-2.0).

### Copycats

Existing competitors: sqliteai/warp and deltafin came earlier; later there were a Termux/Android port PR, an 'arm64 handset layer' PR, and same-route repos like carloslfu/slotstream. No library directly copying its name was found.

### Takeaways

- Within 1 weeks of a big model or platform release, make an extreme demo of 'fitting it into impossible hardware', with one contrast number as the title.

- Long-form article + repo + data catalog, a trio that makes it easy for newsletters to repost.

- The next release of a model of this kind (new weights) brings a second wave to the old repo; adapting to the new version can catch that wave.

### Uncertain

- 11-03 The specific post that ignited the second wave was not found (x-radar's local corpus doesn't index it; possibly the K3.1 Pro hype plus some X/YouTube post we didn't retrieve).

- Whether the engine supports K3.1 Pro weights is unverified (no push after the code 10-02).

- 8 GB working for real: the README has a measurement file, and third parties (Thaki Cloud 11-02 blog post) claim to have reproduced it, but most reposts didn't rerun it; actual user count unknown.

### Sources

- Repo README / GitHub API: [github.com/FareedKhan-dev/kimi-k3-in-c](https://github.com/FareedKhan-dev/kimi-k3-in-c)

- Medium long-form: [levelup.gitconnected.com/building-kimi-k3-2-8t-model-in-c-to…](https://levelup.gitconnected.com/building-kimi-k3-2-8t-model-in-c-to-run-on-8gb-ram-a5792cbf3b59)

- X: [x.com/hasantoxr/status/2084228085331931537](https://x.com/hasantoxr/status/2084228085331931537), [x.com/Saboo_Shubham_/status/2084103716664487978](https://x.com/Saboo_Shubham_/status/2084103716664487978), [x.com/DataChaz/status/2084620676917604836](https://x.com/DataChaz/status/2084620676917604836), [x.com/RoundtableSpace/status/2085844683066880206](https://x.com/RoundtableSpace/status/2085844683066880206), [x.com/MaxForAI/status/2085806455626842291](https://x.com/MaxForAI/status/2085806455626842291), [x.com/kirillk_web3/status/2086051043050479660](https://x.com/kirillk_web3/status/2086051043050479660), [x.com/mikenevermiss/status/2089224310426644938](https://x.com/mikenevermiss/status/2089224310426644938)

- HN: [news.ycombinator.com/item?id=49181439](https://news.ycombinator.com/item?id=49181439) (4 points); K3 weights thread, warp, deltafin, K3.1 Pro weights thread are in our hackernews library

- Reposts: [explainx.ai/blog/kimi-k3-in-c-2-78-trillion-parameters-one-c…](https://explainx.ai/blog/kimi-k3-in-c-2-78-trillion-parameters-one-cpu-8-gb-ram-26-seconds-per-token-github-trending-2026), [earlysignal.news/s/fareedkhan-dev-kimi-k3-in-c](https://earlysignal.news/s/fareedkhan-dev-kimi-k3-in-c), [thakicloud.com/tech-blog/en/llmops/kimi-k3-cpu-inference/](https://thakicloud.com/tech-blog/en/llmops/kimi-k3-cpu-inference/)

- Our GitHub repo data (repo_day / repo_profile / trending, 2026-11-06)

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