How much VRAM does MiMo-V2.5 310B (MoE) need at Q4_K_M? Xiaomi 1M SWA model
MiMo-V2.5 310B at Q4_K_M is a 244 GB resident deployment for 1M context, or about 62 GB with active-expert offload.
MiMo-V2.5 310B at Q4_K_M with native 1M context needs about 244 GB of VRAM with all experts resident. The smaller sibling of MiMo-V2.5-Pro activates 15B parameters per token from a 310B total pool. Active-expert offload drops the resident footprint to roughly 62 GB. Sliding-window attention with a 128-token window keeps the key-value cache under 49 GB at the full 1M context.
By TechCompare · Updated
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Estimated VRAM required
244 GB
310B 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 4 of every 5 layers at a 128-token window. KV cache estimate is 81% 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.
How this is calculated
The 310B parameter pool requires 174 GB of weights at Q4_K_M. The 48-layer architecture uses group-query attention with variable KV heads: 8 in the 9 full-attention layers, 4 in the 39 sliding-window layers. The QK and V dims share the same asymmetry as the Pro variant, so the effective head_dim is 160. Sliding-window attention caps 39 of 48 layers at a 128-token window. The key-value cache at the full 1M context uses about 48 GB. Activation and software overhead adds about 22 GB. In active-only mode the resident weights shrink to 8 GB, which drops total usage to 62 GB.
Verdict
The 244 GB resident total sums as 174 GB of Q4 weights, 48 GB of FP16 KV cache at the full 1M context (variable KV heads: 8 in the 9 global layers, 4 in the 39 sliding-window layers, asymmetric QK/V averaging to head_dim 160), and 22 GB of overhead. Hardware-wise that fits four H100 80GB cards or two H200 141GB cards with NVLink. Active-only loading shrinks the weights to about 8 GB by routing cold experts through RAM, dropping the total to roughly 62 GB, which fits a single 80 GB pro card or a 96 GB unified-memory workstation. The cost-performance case here is the practical one in the MiMo lineup: the Pro variant at 1.02T needs about 2.8x the resident VRAM and 2.8x the active parameters, while the 310B sibling carries the same architectural family at a footprint that fits single-datacenter-card hardware.
More MiMo scenarios
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Frequently asked questions
What's the difference between MiMo-V2.5 and MiMo-V2.5-Pro?
Does MiMo-V2.5 fit on a single GPU?
Does MiMo-V2.5 310B fit on a single GPU?
How does MiMo-V2.5 compare to the Mixtral-style MoEs for self-hosting?
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