Can You Run Kimi K2 (1T MoE)? VRAM Requirements
Kimi K2 (1T MoE) has 1.0T total parameters (MoE, 32B active per token). Running it at Q4_K_M (GGUF) with 16K context takes roughly 631 GB of memory (583 GB weights + 1.1 GB KV cache + runtime overhead). The minimum viable hardware is about 4× B200 192GB. All numbers are planning estimates, verified 2026-07-27.
VRAM by quantization (16K context)
| Quantization | Weights | Total needed | Minimum hardware |
|---|---|---|---|
| FP16 / BF16 | 1.87 TB | 2.02 TB | 12× B200 192GB |
| FP8 | 956 GB | 1.01 TB | 6× B200 192GB |
| Q8_0 (GGUF) | 1013 GB | 1.07 TB | 7× B200 192GB |
| Q6_K (GGUF) | 784 GB | 847 GB | 5× B200 192GB |
| Q5_K_M (GGUF) | 678 GB | 734 GB | 5× B200 192GB |
| MXFP4 | 506 GB | 548 GB | 8× H100 80GB |
| Q4_K_M (GGUF)recommended | 583 GB | 631 GB | 4× B200 192GB |
| Q3_K_M (GGUF) | 449 GB | 486 GB | 7× H100 80GB |
| Q2_K (GGUF) | 334 GB | 362 GB | Mac M3 Ultra 512GB |
How context length changes memory (Q4_K_M (GGUF))
| Context | KV cache | Total needed |
|---|---|---|
| 4K | 0.3 GB | 630 GB |
| 16K | 1.1 GB | 631 GB |
| 64K | 4.3 GB | 634 GB |
| 128K | 8.5 GB | 638 GB |
KV cache grows linearly with context; FP8 KV cache halves it again (try it in the calculator below).
Customize the estimate
LLM VRAM Calculator — Can I Run It?
Estimate how much GPU memory (VRAM) you need to run open-weight LLMs like Kimi K3, DeepSeek R1, Qwen3, or Llama locally. Pick a model, quantization, and context length — the calculator adds up weights, KV cache, and runtime overhead, then shows which GPUs or Macs can fit it. Runs entirely in your browser: no upload, no signup.
1.0T total params · 32B active (MoE) · max 128K context
Recommended balance · ~0.61 bytes/param
Estimated memory needed
638 GB
Will it fit?
Assumes ~92% of device memory is usable. Multi-GPU counts are for tensor/pipeline parallel serving (vLLM, SGLang); Apple Silicon uses unified memory via llama.cpp or MLX.
FAQ
How much VRAM does Kimi K2 (1T MoE) need?
About 631 GB at the recommended Q4_K_M (GGUF) quantization with 16K context: 583 GB for weights, 1.1 GB for KV cache, plus runtime overhead. Longer context grows the KV cache.
What is the minimum hardware for Kimi K2 (1T MoE)?
Roughly 4× B200 192GB, assuming 92% of device memory is usable. Lower-end setups can try more aggressive quantization (Q3/Q2) at a noticeable quality cost.
Can I run Kimi K2 (1T MoE) on a Mac?
Not realistically. Even at Q4_K_M (GGUF) it needs about 631 GB, which exceeds the largest Mac M3 Ultra 512GB. Use a multi-GPU server or an API instead.
Which quantization should I use for Kimi K2 (1T MoE)?
Q4_K_M is the recommended balance of size and quality; with headroom, Q6_K or Q8_0 reduce quality loss further.