moonshotai

Kimi-K3

2,779.9B parameters · mixture-of-experts · Instruct · Kimi family

Mixture-of-experts — what it means for memory & speed → Long context — how it uses memory →
kimi-k3 hash verified source matched revision pinned upstream repo changed since import
Good for: Long context

Which version should I download?

Default pick: safetensors · BF16

Set your rig to confirm it fits and to see if a higher-quality quant runs fully on your hardware.

Set your rig

Quantizations

Why these quants? Which quant should I pick? What do these mean?

safetensors · BF16

1.42 TB

Set your rig
Est. speed
—
Swarm
0S / 1L webseed OK

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Magnet .torrent

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Context

Advertised 1M · no independent evidence yet

No independent long-context evidence has been graded for this model yet — the advertised window above is the maintainer's number, not a usable-context claim.

Run it

Runtime completeness (BF16 torrent): llama.cpp – Ollama – vision sidecar –

  • – llama.cpp — no GGUF artifact for this torrent
  • – Ollama — not an Ollama-native artifact
  • – vision sidecar — non-GGUF vision artifact

Context / KV 8,192 tokens · FP16 set above the quant table

Generic commands (no rig set). GPU-offload values assume the model fits on your GPU — set your rig for values tuned to your hardware.

llama.cpp loads GGUF files only; this quant is a safetensors build.

Safetensors weights run under vLLM or transformers; a GGUF conversion, if one exists, is a separate quant row.

Ollama runs GGUF builds only; this quant is a safetensors build.

Safetensors weights run under vLLM or transformers; a GGUF conversion, if one exists, is a separate quant row.

Serve an OpenAI-compatible endpoint 
vllm serve moonshotai/Kimi-K3 --max-model-len 8192

Serves on http://localhost:8000/v1 by default.

Install 
pip install transformers accelerate torch
Load and run (run.py) 
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "moonshotai/Kimi-K3"
tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    dtype="auto",
    device_map="auto",
    trust_remote_code=True,
)

Loads the weights straight from the repo; point model_id at a local directory to use your downloaded copy. This repo ships its own modelling code, so trust_remote_code=True is required — read that code before running it.

LM Studio loads GGUF (and MLX on Apple Silicon) only; this quant is a safetensors build.

Safetensors weights run under vLLM or transformers; a GGUF conversion, if one exists, is a separate quant row.

MLX runs MLX-format weights only (Apple Silicon). This quant is a safetensors build.

Safetensors weights run under vLLM or transformers; a GGUF conversion, if one exists, is a separate quant row.

Technical details

No template or sampling metadata recorded.

Evidence & provenance

Source

Provenance
https://huggingface.co/moonshotai/Kimi-K3
Revision pin
9f62e4e9fffbd0a83ddd60e1c209d828994b3569
Manifest
Present

License

Name
kimi-k3
Commercial use
unclear
Access
Open

How verification works →

File hashes (SHA-256)

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  • moonshotai-kimi-k3-bf16/LICENSE 20c797ce19af0c17de52c6afb144644768a591c521655f5ebf5712c9850f2887
  • moonshotai-kimi-k3-bf16/README.md 57de265b5842dfa465c6e73b368b0e15a89b8793b5450528dad577da202cc6fe
  • moonshotai-kimi-k3-bf16/assets/kimi-logo.png 4a80f64242bf907940765adc7bcf340c28dd83334b07c5503792a26495d1933b
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