FareedKhan-dev/kimi-k3-in-c
A 2.78-trillion-parameter Kimi K3 running inference on a single CPU in 8.24 GB of RAM. Portable C99: no BLAS, no framework, no GPU.
2.78T-parameter Kimi K3 inference in 176 KB of C, no GPU needed — This project runs the 2.78-trillion-parameter Kimi K3 model on a single CPU with only 8.24 GB peak RAM, using a 176 KB portable C99 engine with no BLAS or framework dependency. It streams the 1.56 TB checkpoint from disk, so more memory just speeds up inference rather than being required to run it at all. Useful for anyone curious about running huge MoE/transformer models on ordinary hardware.
Metrics
Retention can be computed 30 days after a repository first trends (it isn't shown until then). See About for how each metric is defined.
Star growth and Trending appearances (last 30 days)
Bands mark days on the trending list (darker = higher rank)
Daily trending ranks as a table (1 days)
| Date | Daily |
|---|---|
| Nov 5, 2026 | #11 +351 |
Created Aug 1, 2026 · First on trending Nov 5, 2026
Where it's being discussed
Posts that are about this repository. Japan means Japanese-language tech media and videos; it says nothing about how widely the project is used in Japan.
Other notable repositories in the same category (LLMs & ML)
- snap-stanford/memorilla
- kerlenton/bootgpt
- scienthoon/jev-ood-calibration
- alejandrohdez00/alien-space-of-science
- microsoft/VibeVoice
See all LLMs & ML repositories →
Rank within deep-learning
| # | Repository | ★ |
|---|---|---|
| 1 | tensorflow/tensorflow | 201,446 |
| 2 | huggingface/transformers | 168,755 |
| 3 | AUTOMATIC1111/stable-diffusion-webui | 165,293 |
| 4 | pytorch/pytorch | 105,332 |
| 5 | rohitg00/ai-engineering-from-scratch | 78,323 |
| 6 | labmlai/annotated_deep_learning_paper_implementations | 67,746 |
| 7 | roboflow/supervision | 51,588 |
| 8 | coqui-ai/TTS | 46,210 |
| 9 | ray-project/ray | 44,251 |
| 10 | huggingface/diffusers | 34,902 |
| 21 | FareedKhan-dev/kimi-k3-in-c | 11,661 |