Can You Run Kimi K3 (2.8T MoE)? VRAM Requirements
Kimi K3 (2.8T MoE) has 2.8T total parameters (MoE, 48B active per token). Running it at MXFP4 with 16K context takes roughly 1.46 TB of memory (1.35 TB weights + 1.4 GB KV cache + runtime overhead). The minimum viable hardware is about 9× B200 192GB. All numbers are planning estimates, verified 2026-07-27.
⚠ Some architecture details of this model are estimated — treat results as ballpark figures.
VRAM by quantization (16K context)
| Quantization | Weights | Total needed | Minimum hardware |
|---|---|---|---|
| FP16 / BF16 | 5.09 TB | 5.50 TB | 32× B200 192GB |
| FP8 | 2.55 TB | 2.75 TB | 16× B200 192GB |
| Q8_0 (GGUF) | 2.70 TB | 2.92 TB | 17× B200 192GB |
| Q6_K (GGUF) | 2.09 TB | 2.26 TB | 14× B200 192GB |
| Q5_K_M (GGUF) | 1.81 TB | 1.95 TB | 12× B200 192GB |
| MXFP4recommended | 1.35 TB | 1.46 TB | 9× B200 192GB |
| Q4_K_M (GGUF) | 1.55 TB | 1.68 TB | 10× B200 192GB |
| Q3_K_M (GGUF) | 1.20 TB | 1.29 TB | 8× B200 192GB |
| Q2_K (GGUF) | 913 GB | 987 GB | 6× B200 192GB |
How context length changes memory (MXFP4)
| Context | KV cache | Total needed |
|---|---|---|
| 4K | 0.3 GB | 1.46 TB |
| 16K | 1.4 GB | 1.46 TB |
| 64K | 5.5 GB | 1.46 TB |
| 128K | 11.0 GB | 1.47 TB |
| 256K | 22.0 GB | 1.48 TB |
| 1M | 83.8 GB | 1.54 TB |
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.
2.8T total params · 48B active (MoE) · max 1M context · specs partially estimated
Native format of Kimi K3 / gpt-oss · ~0.53 bytes/param
Estimated memory needed
1.47 TB
Architecture details for this model are estimated — treat results as a ballpark.
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 K3 (2.8T MoE) need?
About 1.46 TB at the recommended MXFP4 quantization with 16K context: 1.35 TB for weights, 1.4 GB for KV cache, plus runtime overhead. Longer context grows the KV cache.
What is the minimum hardware for Kimi K3 (2.8T MoE)?
Roughly 9× 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 K3 (2.8T MoE) on a Mac?
Not realistically. Even at MXFP4 it needs about 1.46 TB, which exceeds the largest Mac M3 Ultra 512GB. Use a multi-GPU server or an API instead.
Which quantization should I use for Kimi K3 (2.8T MoE)?
MXFP4 is the model's native/recommended format — Native format of Kimi K3 / gpt-oss. Pick Q4_K_M for a smaller footprint or FP8 / Q8_0 for near-lossless quality.