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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

Total VRAM required
244 GB
MiMo-V2.5 310B (MoE) at Q4_K_M
Weights
174 GB
310B params
KV cache
48.3 GB
1024K tokens, FP16 KV

Calculator

Estimated VRAM required

244 GB

310B params at Q4_K_M, 1,048,576 token context, batch 1, inference.

Weights
174 GB
KV cache
48.3 GB
Overhead
22.2 GB

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.

Apple M3 Ultra 256GB
Unified
~248 GB
98% of estimate
NVIDIA B300
Datacenter
288 GB
85% used

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

DeepSeek V4 Pro 1.6T (MoE) at Q4_K_M
DeepSeek V4 Pro 1.6T at Q4_K_M with the full 1M-token context needs about 1012 GB of VRAM with every expert resident - this is the real shape of the model and the number to plan a deployment against.
View details ➜
Llama 4 Scout (17B/109B) at Q4_K_M
At Q4_K_M and 10,485,760 tokens, this model estimates 2335 GB with all 109B parameters resident.
View details ➜
gpt-oss 20B (MoE) at Q4_K_M
gpt-oss 20B at Q4_K_M with native 128K context needs about 19.4 GB of VRAM with all experts resident, dropping to roughly 9.3 GB with active-only weight loading.
View details ➜

Frequently asked questions

What's the difference between MiMo-V2.5 and MiMo-V2.5-Pro?
Same architecture family with asymmetric QK and V attention dims and sliding-window attention. The Pro variant has a 1.02T total pool with 42B active across 70 layers. The base variant has a 310B total pool with 15B active across 48 layers. The base is the practical pick if you have single-card or workstation hardware.
Does MiMo-V2.5 fit on a single GPU?
In active-only mode, yes. The 62 GB active-only total fits on an 80GB H100 or A100. Resident mode needs 244 GB, which requires four 80GB cards. Capping context to 256K drops the active-only total to about 30 GB, which fits on a single 40GB card.
Does MiMo-V2.5 310B fit on a single GPU?
Not in resident mode. The 310B parameter pool at Q4 is roughly 174 GB just for weights, exceeding any single GPU's memory. Active-only loading drops the resident footprint to about 14 GB if your system has enough CPU RAM, but the cold-expert bandwidth penalty limits throughput. For high-QPS serving, multi-GPU tensor parallelism is the standard.
How does MiMo-V2.5 compare to the Mixtral-style MoEs for self-hosting?
The same architecture shape scaled up: more total experts, more parameters per token path, and stronger quality, at a weight footprint no single consumer GPU holds. If you've self-hosted Mixtral 8x7B, the MiMo-V2.5 story is the same trade tuned up a generation: you still need multi-GPU or very fast CPU-side offload, but the active-subset throughput per token is competitive.