MiMo-V2.6-Pro VRAM requirements: about 687 GB at Q4 and 1M context
MiMo-V2.6-Pro needs about 687 GB in this Q4_K_M, 1,048,576-token, batch-one scenario. The full 1.02T weight pool accounts for 571 GB.
MiMo-V2.6-Pro is a large MoE deployment even though each token activates about 42B parameters. This estimate keeps its full 1.02T parameter pool resident, uses Q4_K_M's planning factor of 0.56 bytes per parameter, and stores the text attention cache in FP16. The roughly 687 GB result includes a 10% runtime allowance. It isn't a measured checkpoint footprint or a guarantee that a particular quantization backend supports the architecture.
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: Planning estimate. Rounded model counts, tensor formats, cache packing, and workspace can change actual allocation. Confirm the exact checkpoint and serving engine with a load test.
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.
MiMo-V2.6-Pro uses 10 global and 60 sliding attention layers. The cache uses 192-wide keys and 128-wide values. This budget covers the backbone and text cache. Vision/audio processing, speculative decoding, and backend workspace can need extra memory.
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
Xiaomi's published configuration has 70 backbone layers with hidden size 6,144. Ten use global attention and 60 use a 128-token sliding window. Both groups have eight KV heads. Keys are 192 values wide and values are 128, so the cache calculation uses their combined width of 320 rather than assuming identical key and value dimensions. At 1,048,576 tokens, the global layers plus sliding buffers consume about 53.73 GB in FP16. Add 571.20 GB for Q4 weights and 62.49 GB of modeled overhead to reach 687.42 GB, or about 640.21 GiB. Reducing context to 32,768 tokens cuts the cache to 1.72 GB and the total to about 630.21 GB. These calculations use the RL/MOPD backbone layout and rounded published parameter count. They don't separately predict image or audio activation peaks, speculative draft buffers, communication workspace, or a runtime's prefill allocation strategy.
Verdict
Plan MiMo Pro around a large memory pool and verify the actual artifact before choosing hardware. Context reduction helps, but it can't remove the 571 GB weight budget. Eight nominal 80 GB GPUs provide only 640 GB in aggregate, below this full-context estimate before uneven sharding is considered. More aggregate memory, lower weight precision, or an explicitly supported offload design is needed. A memory fit alone doesn't establish acceptable throughput or supported multi-GPU execution.
More MiMo scenarios
Related guides
Frequently asked questions
Why can't I size MiMo Pro as a 42B model?
Does sliding attention make the 1M cache constant size?
Is Q4_K_M the format of Xiaomi's download?
Do the RL and MOPD variants need different backbone presets?
What should I measure after the model loads?
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 ➜