Kimi K2 Base — Hardware Requirements & GPU Compatibility
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Kimi K2 Base is a 1026.5B-parameter open language model from Moonshot AI in the Kimi K2 family. It supports a context window of up to 131,072 tokens. At Q4_K_M it needs about 619.76 GB of VRAM — see which GPUs and Macs can run it below.
11.0K downloads
304 likesSpecifications
- Publisher
- Moonshot AI
- Family
- Kimi K2
- Parameters
- 1026.5B
- Architecture
- DeepseekV3ForCausalLM
- Context Length
- 131,072 tokens
- Vocabulary Size
- 163,840
- Release Date
- 2025-07-03
- License
- Other
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HuggingFace
How Much VRAM Does Kimi K2 Base Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_Kest. | 3.40 | 440.1 GB | 665.8 GB | 436.25 GB | 2-bit quantization with K-quant improvements |
| Q3_K_Mest. | 3.90 | 504.3 GB | 730.0 GB | 500.40 GB | 3-bit medium quantization |
| Q4_K_Mest. | 4.80 | 619.8 GB | 845.4 GB | 615.88 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_Mest. | 5.70 | 735.2 GB | 960.9 GB | 731.36 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_Kest. | 6.60 | 850.7 GB | 1076.4 GB | 846.84 GB | 6-bit quantization, very good quality |
| Q8_0est. | 8.00 | 1030.3 GB | 1256.0 GB | 1026.47 GB | 8-bit quantization, near-lossless |
| BF16est. | 16.00 | 2056.8 GB | 2282.5 GB | 2052.94 GB | Brain floating point 16 — preferred for training |
est.= calculated VRAM estimate; no published GGUF file found for that quantization yet. Other rows are verified against real community uploads.
Which GPUs Can Run Kimi K2 Base?
Q4_K_M · 619.8 GBKimi K2 Base (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 Base?
Q4_K_M · 619.8 GB2 devices with unified memory can run Kimi K2 Base, including NVIDIA DGX H100.
Decent
— Enough memory, may be tightRelated Models
Frequently Asked Questions
- How much VRAM does Kimi K2 Base need?
Kimi K2 Base requires 619.8 GB of VRAM at Q4_K_M, or 2056.8 GB at BF16. 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_M619.8 GBQ4_K_M + full context845.4 GB- Can NVIDIA GeForce RTX 5090 run Kimi K2 Base?
No — Kimi K2 Base requires at least 440.1 GB at Q2_K, which exceeds the NVIDIA GeForce RTX 5090's 32 GB of VRAM.
- What's the best quantization for Kimi K2 Base?
For Kimi K2 Base, Q4_K_M (619.8 GB) offers the best balance of quality and VRAM usage. Q5_K_M (735.2 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 440.1 GB.
VRAM requirement by quantization
Q2_K440.1 GBQ4_K_M ★619.8 GBQ5_K_M735.2 GBQ6_K850.7 GBQ8_01030.3 GBBF162056.8 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Kimi K2 Base on a Mac?
Kimi K2 Base requires at least 440.1 GB at Q2_K, 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 Base locally?
Yes — Kimi K2 Base 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 Base?
At Q4_K_M, the download is about 615.88 GB. The full-precision BF16 version is 2052.94 GB. The smallest option (Q2_K) is 436.25 GB.
- Which GPUs can run Kimi K2 Base?
No single consumer GPU has enough VRAM to run Kimi K2 Base at Q4_K_M (619.8 GB). Multi-GPU or professional hardware is required.
- Which devices can run Kimi K2 Base?
2 devices with unified memory can run Kimi K2 Base at Q4_K_M (619.8 GB), including NVIDIA DGX A100 640GB, NVIDIA DGX H100. Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.