How much VRAM does Kimi K2.6 1.1T (MoE) need at Q4_K_M? Moonshot 256K frontier
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.
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.
By TechCompare · Updated
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Estimated VRAM required
772 GB
1100B params at Q4_K_M, 262,144 token context, batch 1, inference.
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
The 772 GB resident budget sums as 616 GB of Q4 weights, 86 GB of FP16 KV cache at the 256K native context (8 KV heads at head_dim 128 across 80 layers), and 70 GB of overhead. Hardware-wise that's six H200 141GB cards or ten H100 80GB cards with NVLink. Active-only loading shrinks the resident weights to 17.9 GB by routing cold experts through system RAM, dropping the total to roughly 114 GB while keeping the cache fixed. The 8-head GQA design is what keeps the cache budget tractable at 256K: more KV heads would scale the cache cost faster than the model's context growth, which is what lets K2.6 stay runnable on dual 80 GB cards or a 192 GB unified-memory Mac Studio in active-offload mode. For most production use, hosted Kimi endpoints deliver the per-token math that makes self-hosting the niche case.
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Frequently asked questions
Why is the Kimi K2.6 key-value cache smaller than other large models?
Can I run this model on unified memory machines?
Can I run Kimi K2.6 1.1T on unified memory machines?
Why is Apple Silicon unified memory the unusual fit for trillion-parameter MoEs?
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