kimi-k3-in-c
Pilot it★ 6,623 · 1,079 forks
C99 inference engine for 2.78 T‑parameter Kimi K3, runs on a single CPU with ~8 GB RAM
Worth a timeboxed spike before you bet on it.
Who it's for
tiny teams needing on‑device LLM inference without GPU, e.g., embedded or low‑cost SaaS prototypes
What it replaces
cloud LLM API calls or heavyweight GPU inference stacks
The catch
Huge model size causes high latency and memory pressure; no GPU support; limited community; single maintainer; may fail on non‑AVX2 CPUs
Your first hour
Clone the repo, compile the C code on a Linux machine, and run the supplied benchmark to confirm 8 GB RAM usage
The numbers
Stars6,623
Forks1,079
Stars added (7d)measuring…
Open issues5
LanguageC
LicenceApache-2.0
Last pushUpdated 3 days ago
Project age1 months old
Maintainers describe it as: “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.”
avx2c99cpu-inferencedeep-learningfrom-scratchinference-enginekimi-k3linear-attentionllmllm-inference
kimi-k3-in-c, in short
- Should a small team use kimi-k3-in-c?
- Pilot it. Worth a timeboxed spike before you bet on it. tiny teams needing on‑device LLM inference without GPU, e.g., embedded or low‑cost SaaS prototypes
- What does kimi-k3-in-c actually do?
- C99 inference engine for 2.78 T‑parameter Kimi K3, runs on a single CPU with ~8 GB RAM
- What does kimi-k3-in-c replace?
- cloud LLM API calls or heavyweight GPU inference stacks
- What is the downside of kimi-k3-in-c?
- Huge model size causes high latency and memory pressure; no GPU support; limited community; single maintainer; may fail on non‑AVX2 CPUs
- Can kimi-k3-in-c be used in a commercial product?
- Its licence is Apache-2.0, which is permissive and generally fine for commercial use. Confirm against the LICENSE file in the repository.