LocalFit

Kimi-K2-Instruct-0905

moonshotai/Kimi-K2-Instruct-0905

ReleasedRelease date unknownSize1.03T tensors · MoERoleAssistantLicensemodified-mitContext256K config max

Where it runs 8K context · ≥ 5 t/s floor

SetupBest planMemoryGenerate
8 GB GPU + 32 GB RAMNon-expert weights + KV cache (11.7 GiB) exceed the GPU memory left after reserves (6.5 GiB)
12 GB GPU + 32 GB RAMNon-expert weights + KV cache (11.7 GiB) exceed the GPU memory left after reserves (10.5 GiB)
16 GB GPU + 64 GB RAMDoes not fit
24 GB GPU + 64 GB RAMDoes not fit
32 GB GPU + 128 GB RAMDoes not fit
2× 24 GB GPUs + 128 GB RAMDoes not fit
96 GB workstation GPU + 256 GB RAMDoes not fit
Mac, 24 GBDoes not fit
Mac, 48 GBDoes not fit
Mac, 128 GBDoes not fit
Mac, 512 GBDoes not fit
DGX Spark 128 GBDoes not fit
Ryzen AI Max+ 395, 128 GBDoes not fit
CPU only, 64 GB DDR5-5600 dual channelDoes not fit

Same plan engine as the planner, with its default reserves and RAM. How plans are chosen.

Files exact Hugging Face bytes

ArtifactSizeFilesRevisionSource
Q8_0
bartowski/moonshotai_Kimi-K2-Instruct-0905-GGUF
1,016 GiB308580767137HF files
Q5_K_M
bartowski/moonshotai_Kimi-K2-Instruct-0905-GGUF
680 GiB208580767137HF files
Q4_K_M
bartowski/moonshotai_Kimi-K2-Instruct-0905-GGUF
581 GiB168580767137HF files
MLX 8-bit 8-bit
richardyoung/Kimi-K2-Instruct-0905-MLX-8bit
1,016 GiB36139004dfaf0HF files
MLX 4-bit 4-bit
richardyoung/Kimi-K2-Instruct-0905-MLX-4bit
538 GiB18040998cb188HF files
Native safetensors mixed/custom native; quant_method=fp8; stored tensor dtypes=
moonshotai/Kimi-K2-Instruct-0905
959 GiB62ac6c49f048HF files

Not found in the representative quant search (not proven unavailable): GGUF FP16, EXL3, AWQ, GPTQ. Complete selected file sets; projectors, MTP/draft and lookup files are listed separately and budgeted only by plans that use them. File bytes are not allocated memory.

Quality

No Artificial Analysis evaluation mapped to this checkpoint.

Arena text rating

Unknown

Publisher-reported benchmarks

None extracted from the model card.

AA and Arena describe the hosted model in the stated mode, not each quantized file; scores are never inherited by base, pruned, abliterated or quantized variants and never averaged together.

How to run it

No model-specific recipe in the research. The planner binds the backend’s documented command template (llama.cpp, Ollama, LM Studio, MLX, vLLM) to your plan: open a plan’s details.

Recipes are source examples (settings are the publisher’s or author’s, not measured optima) and were not executed by LocalFit. A downloadable file does not prove your backend build supports the architecture.

Reported speeds 0

No reported measurement yet. Submit one.