Kimi K2 Instruct 0905 — Hardware Requirements & GPU Compatibility
Chat
29.5K downloads
690 likesSpecifications
- Publisher
- Moonshot AI
- Family
- Kimi K2
- Parameters
- 1026.5B
- Architecture
- DeepseekV3ForCausalLM
- Context Length
- 262,144 tokens
- Vocabulary Size
- 163,840
- Release Date
- 2026-01-30
- License
- Other
Get Started
HuggingFace
How Much VRAM Does Kimi K2 Instruct 0905 Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 440.1 GB | 895.0 GB | 436.25 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 453.0 GB | 907.9 GB | 449.08 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 504.3 GB | 959.2 GB | 500.40 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 517.1 GB | 972.0 GB | 513.24 GB | 4-bit legacy quantization |
| Q4_K_M | 4.80 | 619.8 GB | 1074.7 GB | 615.88 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 735.2 GB | 1190.2 GB | 731.36 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 850.7 GB | 1305.6 GB | 846.84 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 1030.3 GB | 1485.3 GB | 1026.47 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run Kimi K2 Instruct 0905?
Q4_K_M · 619.8 GBKimi K2 Instruct 0905 (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 262K context window can add up to 454.9 GB, bringing total usage to 1074.7 GB. No single GPU has enough memory — multi-GPU or cluster setups are needed.
Which Devices Can Run Kimi K2 Instruct 0905?
Q4_K_M · 619.8 GB2 devices with unified memory can run Kimi K2 Instruct 0905, including NVIDIA DGX H100.
Decent
— Enough memory, may be tightRelated Models
Similar
Frequently Asked Questions
- How much VRAM does Kimi K2 Instruct 0905 need?
Kimi K2 Instruct 0905 requires 619.8 GB of VRAM at Q4_K_M, or 1030.3 GB at Q8_0. Full 262K context adds up to 454.9 GB (1074.7 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 ≈ 458.8 GB (at full 262K context)
VRAM usage by quantization
Q4_K_M619.8 GBQ4_K_M + full context1074.7 GB- Can NVIDIA GeForce RTX 5090 run Kimi K2 Instruct 0905?
No — Kimi K2 Instruct 0905 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 0905?
For Kimi K2 Instruct 0905, 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_XXS286.2 GB~53%Q3_K_S453.0 GB~77%Q4_1581.3 GB~88%Q4_K_M ★619.8 GB~89%Q5_K_S709.6 GB~92%Q8_01030.3 GB~99%★ Recommended — best balance of quality and VRAM usage.
- Can I run Kimi K2 Instruct 0905 on a Mac?
Kimi K2 Instruct 0905 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 0905 locally?
Yes — Kimi K2 Instruct 0905 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 0905?
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.
- Which GPUs can run Kimi K2 Instruct 0905?
No single consumer GPU has enough VRAM to run Kimi K2 Instruct 0905 at Q4_K_M (619.8 GB). Multi-GPU or professional hardware is required.
- Which devices can run Kimi K2 Instruct 0905?
2 devices with unified memory can run Kimi K2 Instruct 0905 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.