How much VRAM does MiMo-V2.5-Pro 1.02T (MoE) need at Q4_K_M? Xiaomi 1M SWA frontier
MiMo-V2.5-Pro 1.02T at Q4_K_M is a 687 GB resident deployment for 1M context, or about 85 GB with active-expert offload.
MiMo-V2.5-Pro 1.02T at Q4_K_M with native 1M context needs about 687 GB of VRAM with all experts resident. Xiaomi's flagship MoE activates 42B parameters per token from a 1.02 trillion total pool. Active-expert offload drops the resident footprint to roughly 85 GB. The asymmetric QK and V attention dims plus sliding-window attention keep the key-value cache manageable even at full context.
By TechCompare · Updated
Calculator
Estimated VRAM required
687 GB
1020B params at Q4_K_M, 1,048,576 token context, batch 1, inference.
Estimate accuracy: Weights within ~2%. KV cache within ~5% for standard GQA models, ~10% for MLA or hybrid state estimates. Real VRAM may vary with framework (vLLM vs llama.cpp vs Transformers), Flash Attention, and driver overhead.
Sliding-window attention applied: This model caps 6 of every 7 layers at a 128-token window. KV cache estimate is 86% smaller than naive full-attention math at this context length.
Hardware that fits
Apple entries show installed unified memory separately from a model budget based on an 8 GB reserve. That budget is a planning heuristic, not a fixed hardware limit.
No single GPU in our catalog has enough memory. Multi-GPU or CPU offload required.
How this is calculated
MiMo-V2.5-Pro's 1.02T parameter pool requires 571 GB of weights at Q4_K_M. The 70-layer architecture uses group-query attention with 8 KV heads and asymmetric key and value dims: keys use 192, values use 128, averaged to an effective head_dim of 160 in the cache math. Sliding-window attention caps 60 of 70 layers at a 128-token window with 10 layers doing full global attention. The result is a 54 GB key-value cache at the full 1M context. Activation and software overhead adds about 62 GB. In active-only mode the resident weights shrink to 24 GB, which drops total usage to 85 GB.
Verdict
The 687 GB resident total sums from 571 GB of Q4 weights, 54 GB of FP16 KV cache at the full 1M context (8 KV heads with asymmetric QK/V dims averaging to head_dim 160 across 70 layers), and 62 GB of overhead. Hardware-wise that's nine 80 GB datacenter cards or five 141 GB H200 cards, both with NVLink. Active-only loading shrinks the weights to about 24 GB by routing cold experts through system RAM, dropping the total to roughly 85 GB while the cache stays at 54 GB. The 60-of-70-layer sliding-window attention is why active-only stays runnable: capping 60 layers at a 128-token window keeps the cache near-constant regardless of context length, so active-offload works on a single 96 GB pro card or a 128 GB unified-memory workstation. Context capping at 128K drops active-only further to about 60 GB.
More MiMo scenarios
Related guides
Frequently asked questions
Why does MiMo-V2.5-Pro use asymmetric QK and V dimensions?
Can I run MiMo-V2.5-Pro locally?
Can I run MiMo-V2.5-Pro 1.02T locally?
How do MoE throughput and latency change when experts live in system RAM?
Related tools
RAM Latency Calculator
Convert DDR3/DDR4/DDR5 timings (CL, tRCD, tRP, tRAS) into true latency in nanoseconds.
Use tool ➜Power Cost Estimator
Estimate annual electricity costs for your PC, Server, or TV.
Use tool ➜Data Transfer Calculator
Estimate transfer times for files over USB, WiFi, Ethernet, and more.
Use tool ➜Memory and Storage Throughput Visualizer
Visualize the massive speed difference between CPU cache, RAM, and storage.
Use tool ➜