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Kimi-K2 VRAM Requirements

Moonshot AI, China · Released July 11, 2025

Kimi-K2 has 1026.4B parameters (33B active per token). In BF16 its weights alone take 1912 GiB; quantized to 4-bit, about 577 GiB. Each 32K-token request adds 2.14 GiB of KV cache. Cheapest way to run it today: 4 × AMD MI300X (192 GB) at $9.56 per hour.

Size your own setup in the calculator →

VRAM by precision and context length

Weights plus the KV cache of one request (BF16 cache), in GiB. Add 1 to 3 GiB for the inference engine itself.

Weights formatWeights only+ 8K context+ 32K context+ 128K context
FP8 (native)959959961967
BF16 / FP161912191219141920
FP8 / INT8958959960967
4-bit (GGUF Q4_K_M)577578580586

Kimi-K2 GGUF files

Exact size of each quantization in unsloth/Kimi-K2-Instruct-GGUF (opens in a new tab), and the smallest GPU that runs it with llama.cpp, Ollama or LM Studio and an 8K-token context.

QuantizationFile sizeSmallest GPU
BF161912 GiBMore than 96 GB: see the calculator
UD-Q8_K_XL1108 GiBMore than 96 GB: see the calculator
Q8_01016 GiBMore than 96 GB: see the calculator
UD-Q6_K_XL819 GiBMore than 96 GB: see the calculator
Q6_K785 GiBMore than 96 GB: see the calculator
UD-Q5_K_XL680 GiBMore than 96 GB: see the calculator
Q5_K_M678 GiBMore than 96 GB: see the calculator
Q5_K_S658 GiBMore than 96 GB: see the calculator
Q4_1598 GiBMore than 96 GB: see the calculator
Q4_K_M578 GiBMore than 96 GB: see the calculator
UD-Q4_K_XL547 GiBMore than 96 GB: see the calculator
Q4_K_S543 GiBMore than 96 GB: see the calculator
Q4_0541 GiBMore than 96 GB: see the calculator
IQ4_NL539 GiBMore than 96 GB: see the calculator
IQ4_XS509 GiBMore than 96 GB: see the calculator
Q3_K_M456 GiBMore than 96 GB: see the calculator
UD-Q3_K_XL421 GiBMore than 96 GB: see the calculator
Q3_K_S412 GiBMore than 96 GB: see the calculator
UD-IQ3_XXS388 GiBMore than 96 GB: see the calculator
UD-Q2_K_XL356 GiBMore than 96 GB: see the calculator
Q2_K_L348 GiBMore than 96 GB: see the calculator
Q2_K348 GiBMore than 96 GB: see the calculator
UD-IQ2_M323 GiBMore than 96 GB: see the calculator
UD-IQ2_XXS306 GiBMore than 96 GB: see the calculator
UD-IQ1_M283 GiBMore than 96 GB: see the calculator
UD-IQ1_S261 GiBMore than 96 GB: see the calculator
UD-TQ1_0227 GiBMore than 96 GB: see the calculator

Read every day from Hugging Face. Split files are added together; vision projectors are left out.

How many GPUs to run Kimi-K2

Fewest GPUs that fit the weights plus one 8K-token request. Data-center GPUs use vLLM (90% of memory usable), the others llama.cpp. Several GPUs split the model with tensor or pipeline parallelism.

GPUFP8 (native)BF16 / FP168-bit4-bit
GeForce RTX 4090 24 GB— (not supported on this GPU)128 (several servers)64 (several servers)32 (several servers)
GeForce RTX 5090 32 GB— (not supported on this GPU)96 (several servers)48 (several servers)24 (several servers)
RTX PRO 6000 Blackwell 96 GB— (not supported on this GPU)24 (several servers)16 (several servers)7
Mac M5 Max 128 GB 96 GB usable— (not supported on this GPU)24 (several servers)16 (several servers)7
NVIDIA H100 80 GB16 (several servers)32 (several servers)16 (several servers)16 (several servers)
NVIDIA H200 141 GB816 (several servers)85
NVIDIA B200 (HGX) 180 GB716 (several servers)74

8-bit: FP8 with vLLM (INT8 on GPUs without FP8), Q8_0 with llama.cpp. 4-bit: AWQ / GPTQ with vLLM, Q4_K_M with llama.cpp, both counted at the Q4_K_M size. —: the official FP8 weights do not run there: llama.cpp needs a GGUF version, and vLLM needs a GPU with FP8 support.

Cheapest way to run Kimi-K2 today

For each GPU: the fewest cards that fit Kimi-K2 with one 8K-token request, the most faithful weight format at that count, and the cheapest on-demand price (2026-10-11).

On-demand prices from RunPod, Vast.ai, Verda and Azure, checked every hour. Serving many users needs more KV cache, so more memory: size it in the calculator. Some links are affiliate links: StudioTV may earn a commission, at no extra cost to you.

KV cache: how memory grows with context

Architecture: 61 layers · MLA (512 + 64 latent) on 61. Its cache stays compact because MLA stores one compressed latent per token and layer instead of full keys and values. Only 33B parameters are active per token, but all 1026B must sit in memory: VRAM depends on the total, speed on the active part.

Context per requestBF16 cacheFP8 cache
8K tokens0.54 GiB0.27 GiB
32K tokens2.14 GiB1.07 GiB
128K tokens8.58 GiB4.29 GiB

Kimi-K2 VRAM FAQ

How much VRAM does Kimi-K2 need?

In BF16 the weights alone take 1912 GiB (2053 GB). In FP8 that is 958 GiB, and about 577 GiB with 4-bit quantization (Q4_K_M); the official FP8 checkpoint is 959 GiB. Each request then adds KV cache: 0.54 GiB at 8K tokens and 2.14 GiB at 32K (BF16 cache).

Can Kimi-K2 run on a single RTX 4090 (24 GB)?

No. Even in 4-bit it needs 32 (several servers) RTX 4090s. In 4-bit it needs 16 (several servers) H100 GPUs.

What is the cheapest way to run Kimi-K2?

On 2026-10-11, the cheapest on-demand setup is 4 × AMD MI300X (192 GB) with 4-bit weights on RunPod, at $9.56 per hour (about $6,978.80 per month). Next: 7 × RTX PRO 6000 Blackwell (96 GB) with Q4_K_M weights on Vast.ai, at $9.80 per hour. Sized for one 8K-token request; prices are checked every hour.

How many H100 GPUs does Kimi-K2 need?

With one request and an 8K-token context: 32 (several servers) in BF16, 16 (several servers) in FP8 and 16 (several servers) in 4-bit (vLLM, 90% of memory usable). Serving many users at once needs more memory for their KV caches: the calculator sizes that for you.

Kimi-K2 VRAM badge

For a model card or a README: the 4-bit size and the smallest GPU it fits on, linked to this page. Data as JSON: /api/models/kimi-k2.json.

Kimi-K2 VRAM badge

[![Kimi-K2 VRAM](https://studiotvai.com/badge/kimi-k2.svg)](https://studiotvai.com/vram-requirements/kimi-k2)

Other models

Same family (large moe): MiniMax M2.7 · DeepSeek R1 · Kimi K2.6 · GLM-5.3 · GLM-5.3-Flash · DeepSeek V4 Flash · DeepSeek V4 Pro · DeepSeek V4.1 Flash · MiMo-V2.6 Pro · MiMo-V2.6 Flash · Kimi K3 · Mistral Large 4 · DeepSeek V4 Flash Vision Exp · Qwen3.8 2.4T-A95B · Ornith 1.5 397B · Atria Dawn Preview · Intern S2 397B · DeepSeek-V3 · DeepSeek-V3.1 · Kimi-K2.7-Code · Kimi-K2.5 · GLM-5.2 · GLM-5.1 · GLM-5 · MiniMax-M2.5 · MiniMax-M2 · MiMo-V2.5 · Ornith-1.0 397B

All models: Llama 3.1 8B · Mistral Small 3.2 24B · Qwen3 32B · Llama 3.3 70B · Llama 3.1 405B · Qwen3.5 4B · Qwen3.5 9B · Qwen3.8 27B · Qwen3.6 35B-A3B · Qwen3.5 122B-A10B · Qwen3.8-Flash-Next · LFM2.5 8B-A1B · LFM2 24B-A2B · Gemma 4 12B · Gemma 4 31B · Gemma 4 26B-A4B · gpt-oss 20B · gpt-oss 120B · Llama 4 Scout 17B-16E · Llama 4 Maverick 17B-128E · Kolibri-1 · Nemotron 3 Nano 30B-A3B · Nemotron 3 Super 120B-A12B · MiniMax M2.7 · DeepSeek R1 · Kimi K2.6 · GLM-5.3 · GLM-5.3-Flash · DeepSeek V4 Flash · DeepSeek V4 Pro · DeepSeek V4.1 Flash · MiMo-V2.6 Pro · MiMo-V2.6 Flash · Kimi K3 · Mistral Large 4 · Qwen3-Coder-Next 80B-A3B · Qwen3-Coder 30B-A3B · Qwen3-Coder 480B-A35B · DeepSeek V4 Flash Vision Exp · Qwen3.8 2.4T-A95B · Ornith 1.5 397B · Ornith 1.5 35B-A3B · Ornith 1.5 9B · Atria Dawn Preview · Intern S2 397B · Clef · Clef Flash · D1 3B · Humanizer · JEV 27B VL · Mellum2.1 12B-A2.5B · LightOnOCR-3 4B · Spark-X2.5 4B · Agnes-3.0-Qwen · Youtu-Parsing-Omni · Agnes-3.0-Flash · Mistral 7B-v0.3 · Mistral-Small-3.1 24B · Mistral-Nemo · Ministral 8B · DeepSeek-V3 · DeepSeek-R1-Qwen3 8B · DeepSeek-R1-Distill-Qwen 32B · DeepSeek-V3.1 · DeepSeek-R1-Distill-Qwen 14B · DeepSeek-R1-Distill-Qwen 7B · DeepSeek-R1-Distill-Llama 8B · Qwen3 4B · Qwen3 8B · Qwen2.5 7B · Qwen3 14B · Qwen2.5 3B · Qwen3.6 27B · Qwen3.5 27B · Qwen2.5-Coder 7B · Qwen2.5 32B · Qwen2.5-Coder 14B · Qwen3.5 35B-A3B · Qwen3 30B-A3B · Qwen2.5 14B · Qwen2.5-Coder 32B · Kimi-K2.7-Code · Kimi-K2.5 · GLM-5.2 · GLM-5.1 · GLM-5 · MiniMax-M2.5 · MiniMax-M2 · MiMo-V2.5 · Ornith-1.0 397B

Estimates, not guarantees: computed from the official config.json with the same engine as the LLM VRAM Calculator. Real usage depends on your engine version and settings.