Can NVIDIA GeForce RTX 5090 run Kimi K2 Instruct?

No — Kimi K2 Instruct requires at least 286.2 GB at IQ2_XXS, which exceeds the NVIDIA GeForce RTX 5090's 32 GB of VRAM.

What's the best quantization for Kimi K2 Instruct?

For Kimi K2 Instruct, Q4_K_M (619.8 GB) offers the best balance of quality and VRAM usage. Q5_K_S (709.6 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 286.2 GB.

Can I run Kimi K2 Instruct on a Mac?

Kimi K2 Instruct requires at least 286.2 GB at IQ2_XXS, which exceeds the unified memory of most consumer Macs. You would need a Mac Studio or Mac Pro with a high-memory configuration.

Can I run Kimi K2 Instruct locally?

Yes — Kimi K2 Instruct can run locally on consumer hardware. At Q4_K_M quantization it needs 619.8 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.

What's the download size of Kimi K2 Instruct?

At Q4_K_M, the download is about 615.88 GB. The full-precision Q8_0 version is 1026.47 GB. The smallest option (IQ2_XXS) is 282.28 GB.

Moonshot AI·Kimi K2·DeepseekV3ForCausalLM

Kimi K2 Instruct — Hardware Requirements & GPU Compatibility

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Kimi K2 Instruct is Moonshot AI's massive Mixture-of-Experts model, weighing in at over one trillion total parameters. It represents one of the largest open-weight models available, delivering frontier-class performance across reasoning, coding, and multilingual tasks through its sparse MoE architecture that activates only a fraction of its full parameter count per token. Running Kimi K2 locally is an extreme undertaking, requiring professional multi-GPU setups with hundreds of gigabytes of combined VRAM even at aggressive quantization. This model is best suited for research labs, enterprise deployments, or enthusiasts with access to server-grade hardware who want to explore trillion-parameter-scale inference.

80.6K downloads 2.3K likesJan 2026131K context

Specifications

Publisher: Moonshot AI
Family: Kimi K2
Parameters: 1026.5B
Architecture: DeepseekV3ForCausalLM
Context Length: 131,072 tokens
Vocabulary Size: 163,840
Release Date: 2026-01-30
License: Other

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HuggingFace

moonshotai/Kimi-K2-Instruct

How Much VRAM Does Kimi K2 Instruct Need?

Select a quantization to see compatible GPUs below.

Quantization	Bits	VRAM	+ Context	File Size	Quality
IQ2_XXS	2.20	286.2 GB	511.8 GB	282.28 GB	Importance-weighted 2-bit, extreme compression — significant quality loss
IQ2_M	2.70	350.3 GB	576.0 GB	346.43 GB	Importance-weighted 2-bit, medium
IQ3_XXS	3.10	401.6 GB	627.3 GB	397.76 GB	Importance-weighted 3-bit
Q2_K	3.40	440.1 GB	665.8 GB	436.25 GB	2-bit quantization with K-quant improvements
Q3_K_S	3.50	453.0 GB	678.6 GB	449.08 GB	3-bit small quantization
Q3_K_M	3.90	504.3 GB	730.0 GB	500.40 GB	3-bit medium quantization
Q4_0	4.00	517.1 GB	742.8 GB	513.24 GB	4-bit legacy quantization
IQ4_XS	4.30	555.6 GB	781.3 GB	551.73 GB	Importance-weighted 4-bit, compact
Q4_1	4.50	581.3 GB	806.9 GB	577.39 GB	4-bit legacy quantization with offset
Q4_K_S	4.50	581.3 GB	806.9 GB	577.39 GB	4-bit small quantization
IQ4_NL	4.50	581.3 GB	806.9 GB	577.39 GB	Importance-weighted 4-bit, non-linear
Q4_K_M	4.80	619.8 GB	845.4 GB	615.88 GB	4-bit medium quantization — most popular sweet spot
Q5_K_S	5.50	709.6 GB	935.2 GB	705.70 GB	5-bit small quantization
Q5_K_M	5.70	735.2 GB	960.9 GB	731.36 GB	5-bit medium quantization — good quality/size tradeoff
Q6_K	6.60	850.7 GB	1076.4 GB	846.84 GB	6-bit quantization, very good quality
Q8_0	8.00	1030.3 GB	1256.0 GB	1026.47 GB	8-bit quantization, near-lossless

Which GPUs Can Run Kimi K2 Instruct?

Q4_K_M · 619.8 GB

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Kimi K2 Instruct (Q4_K_M) requires 619.8 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 806+ GB is recommended. Using the full 131K context window can add up to 225.7 GB, bringing total usage to 845.4 GB. No single GPU has enough memory — multi-GPU or cluster setups are needed.

Which Devices Can Run Kimi K2 Instruct?

Q4_K_M · 619.8 GB

2 devices with unified memory can run Kimi K2 Instruct, including NVIDIA DGX H100.

Decent

— Enough memory, may be tight

NVIDIA DGX H100~28 tok/s NVIDIA DGX A100 640GB~17 tok/s

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Frequently Asked Questions

How much VRAM does Kimi K2 Instruct need?: Kimi K2 Instruct requires 619.8 GB of VRAM at Q4_K_M, or 1030.3 GB at Q8_0. Full 131K context adds up to 225.7 GB (845.4 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 1026.5B × 4.8 bits ÷ 8 = 615.9 GB
KV Cache + Overhead ≈ 3.9 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 229.5 GB (at full 131K context)
VRAM usage by quantization
Q4_K_M
619.8 GB
Q4_K_M + full context
845.4 GB
Learn more about VRAM estimation →
Can NVIDIA GeForce RTX 5090 run Kimi K2 Instruct?: No — Kimi K2 Instruct requires at least 286.2 GB at IQ2_XXS, which exceeds the NVIDIA GeForce RTX 5090's 32 GB of VRAM.
What's the best quantization for Kimi K2 Instruct?: For Kimi K2 Instruct, Q4_K_M (619.8 GB) offers the best balance of quality and VRAM usage. Q5_K_S (709.6 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 286.2 GB.
VRAM requirement by quantization
IQ2_XXS
286.2 GB~53%
Q3_K_S
453.0 GB~77%
Q4_1
581.3 GB~88%
Q4_K_M ★
619.8 GB~89%
Q5_K_S
709.6 GB~92%
Q8_0
1030.3 GB~99%
★ Recommended — best balance of quality and VRAM usage.
Learn more about quantization →
Can I run Kimi K2 Instruct on a Mac?: Kimi K2 Instruct requires at least 286.2 GB at IQ2_XXS, which exceeds the unified memory of most consumer Macs. You would need a Mac Studio or Mac Pro with a high-memory configuration.
Can I run Kimi K2 Instruct locally?: Yes — Kimi K2 Instruct can run locally on consumer hardware. At Q4_K_M quantization it needs 619.8 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
What's the download size of Kimi K2 Instruct?: At Q4_K_M, the download is about 615.88 GB. The full-precision Q8_0 version is 1026.47 GB. The smallest option (IQ2_XXS) is 282.28 GB.