How much VRAM does Kimi K2.6 1.1T (MoE) need at Q4_K_M? Moonshot 256K frontier

Kimi K2.6 1.1T MoE at Q4_K_M with its native 256K context needs about 772 GB of VRAM for an all-resident deployment. Moonshot designed this model with 1.1 trillion total parameters, activating 32B parameters per token. If you utilize active-expert offload to hold only the hot routed experts in VRAM, the memory footprint drops to roughly 114 GB. This approach requires streaming cold experts from system RAM, which reduces processing speeds.

Total VRAM required
772 GB
Kimi K2.6 1.1T (MoE) at Q4_K_M
Weights
616 GB
1100B params
KV cache
85.9 GB
256K tokens, FP16 KV

Calculator

Estimated VRAM required

772 GB

1100B params at Q4_K_M, 262,144 token context, batch 1, inference.

Weights
616 GB
KV cache
85.9 GB
Overhead
70.2 GB

Estimate accuracy: Weights within ~2%. KV cache within ~5% for standard GQA models, ~10% for MLA (DeepSeek). Real VRAM may vary with framework (vLLM vs llama.cpp vs Transformers), Flash Attention, and driver overhead.

Hardware that fits

No single GPU in our catalog has enough memory. Multi-GPU or CPU offload required.

How this is calculated

The model has 80 layers, hidden size 8192, and 8 key-value heads. The total parameter pool requires 616 GB of weights at Q4_K_M. Moonshot's architecture results in an 86 GB key-value cache at the full 262,144 token context window. Overhead adds roughly 70 GB, creating the 772 GB resident total. In active-only mode, the resident weight memory shrinks to 17.9 GB, which helps fit the model on smaller setups if you can tolerate slower routing speeds.

Verdict

An all-resident Kimi K2.6 deployment is an enterprise-grade effort. It requires six 141 GB H200 cards or a cluster of ten 80 GB GPUs. Active-only offload works on dual 80 GB cards or pooled pro GPUs, but performance will suffer from PCIe bottlenecking. Using a hosted endpoint is recommended for general workloads.

Frequently asked questions

Why is the Kimi K2.6 key-value cache smaller than other large models?
Kimi K2.6 uses group-query attention with 8 key-value heads. This design makes the cache memory requirements scale much slower at long contexts compared to models with more heads.
Can I run this model on unified memory machines?
Yes, a high-end unified memory workstation with at least 192 GB of memory can run Kimi K2.6 in active-only offload mode. If you want to run the model in full resident mode, you'll need a specialized system with more than 800 GB of memory.