What it does
Core capabilities at a glance
- Avx2
- C99
- CPU Inference
- From Scratch
- Inference Engine
- Kimi K3
- Linear Attention
- LLM Inference
Deep dive
The full breakdown - performance, comparisons, and setup
kimi-k3-in-cLink to this section
kimi-k3-in-c is a local inference server - 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.
OverviewLink to this section
Kimi K3 inference in portable C99. No BLAS. No framework. No GPU.
2.78T parameters 1.56 TB checkpoint on disk 8.24 GB peak RSS, measured 176 KB the whole engine 0 GPUs
The same 2.78-trillion-parameter model, the same answer, on whatever machine you own. More memory only buys speed:
an ordinary laptop 8 GB 26.5 s the whole model streams off the disk on every step
a high-end laptop 32 GB 24.2 s some of the model now sits in memory
a heavy workstation 128 GB+ 5.6 s the model fits entirely in memory, the disk wait is gone
kimi-k3-in-c is open-source, written primarily in C, with 11,844 GitHub stars under the Apache 2.0 license. The latest release is v1.0.0 (2026-08-07).
Key capabilitiesLink to this section
From the project's documentation:
- Quick start: clone, build and verify in about a minute, with no model
- Full setup: the whole path to generated text
- Reading the run report
- The problem: a model that does not fit
- The machine, and what it assumes
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- Reading a 1.56 TB checkpoint from its headers
How it fits a local-AI stackLink to this section
kimi-k3-in-c runs on your own hardware, so pair it with a model and a GPU sized to your needs. Use the VRAM calculator to pick a model that fits your card, and see what you can run for hardware guidance. Related local inference servers in the directory:
SourcesLink to this section
- Source code & docs: FareedKhan-dev/kimi-k3-in-c
- Official website: https://medium.com/@fareedkhandev/building-kimi-k3-in-c-to-run-a-2-8t-model-on-consumer-hardware-a5792cbf3b59
Stats from GitHub, 2026-08-15.
Frequently asked
Quick answers to common questions
What is kimi-k3-in-c?
kimi-k3-in-c is a inference-server tool for local AI workloads. 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.
Is kimi-k3-in-c free and open source?
Yes, kimi-k3-in-c has 11,844 GitHub stars and is licensed under Apache 2.0. You can self-host it for free on linux.
What platforms does kimi-k3-in-c support?
kimi-k3-in-c runs on linux.
What hardware do I need for kimi-k3-in-c?
The hardware requirements depend on which models you run. Check our hardware directory for compatible GPUs and systems. kimi-k3-in-c has 11,844 GitHub stars and an active community.
Does kimi-k3-in-c support GPU acceleration?
kimi-k3-in-c supports GPU acceleration via CUDA, Metal, or Vulkan depending on your platform. For the best performance, pair it with an NVIDIA RTX 4090 or 5090.
What are the best alternatives to kimi-k3-in-c?
Popular alternatives include other inference-server tools in our directory. Browse our full collection at /tool for comparisons, community reviews, and benchmark data to find the right fit for your workflow.
How much does kimi-k3-in-c cost?
kimi-k3-in-c is free-open-source. It is completely free and open source to self-host.
Pairs well with
Complementary tools, models, and hardware