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MiMo-V2.5-Pro-FP4-DFlash GPU Requirements: VRAM & Cheapest GPU

MiMo-V2.5-Pro-FP4-DFlash has about 554B parameters. See exactly how much GPU memory it needs at FP16, INT8, and INT4, and the cheapest GPU to run it, with live hourly pricing from 5+ data center partners.

554BParameters
302 GBMin VRAM
$3.28/hrCheapest
< 2 minDeploy
XiaomiMiMo/MiMo-V2.5-Pro-FP4-DFlash
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554B paramstext-generationmimo_v248 downloads68 likesupdated Jun 8, 2026

To run MiMo-V2.5-Pro-FP4-DFlash for inference at FP16, you need roughly 1208 GB of VRAM. The cheapest fit on Spheron is 8x B200 192GB at about $21.92/hr. Quantize to INT4 to run it on a smaller, cheaper GPU.

GB VRAM REQUIRED
FP16INFERENCEBATCH 1CTX 4k

Estimated peak VRAM including weights, activations, and KV cache. Add 10% headroom for production traffic.

RANKCONFIGURATIONPER GPUTOTAL $/HR
  • 01
    8× B200 192GBCHEAPEST
    Blackwell · HBM3e
    $2.74/hr$21.92/hr
  • 02
    8× B300 288GB
    Blackwell Ultra · HBM3e
    $3.29/hr$26.32/hr

Live pricing aggregated from 5+ data center partners. Per-minute billing, no commitments.

VRAM required to run MiMo-V2.5-Pro-FP4-DFlash

Estimated peak VRAM at context length 4,096 and batch size 1, including weights, activations, and KV cache. Quantizing to INT8 (Q8) or INT4 (Q4) cuts memory roughly in half and in quarter.

PrecisionInferenceLoRA fine-tuneFull fine-tune
FP161208 GB1813 GB4834 GB
INT8604 GB906 GB2417 GB
INT4302 GB453 GB1208 GB

Cheapest GPU to run MiMo-V2.5-Pro-FP4-DFlash by precision

FP16
VRAM required1208GB

Full precision. Best quality, highest memory.

Cheapest GPU
8x B200 192GB
Blackwell · HBM3e
$21.92/hr · $2.74/hr/gpu
8x B200 192GB on Spheron
INT8
VRAM required604GB

8-bit quantized. ~2x smaller, minimal quality loss.

Cheapest GPU
8x A100 80GB
Ampere · HBM2e
$6.56/hr · $0.82/hr/gpu
8x A100 80GB on Spheron
INT4
VRAM required302GB

4-bit quantized. ~4x smaller, runs on smaller GPUs.

Cheapest GPU
4x A100 80GB
Ampere · HBM2e
$3.28/hr · $0.82/hr/gpu
4x A100 80GB on Spheron

Inference vs fine-tuning MiMo-V2.5-Pro-FP4-DFlash

InferenceWeights + KV cache
LoRA fine-tune~1.5×+ low-rank adapter
Full fine-tune~4×+ gradients + optimizer state

Inference only holds the model weights plus a KV cache, so it is the cheapest setup. LoRA fine-tuning adds a small adapter and roughly 50% more memory. Full fine-tuning holds gradients and optimizer state on top of the weights, which is about 4x the inference footprint, so it often needs multiple GPUs even when inference fits on one. For MiMo-V2.5-Pro-FP4-DFlash, an on-demand B200 192GB instance covers inference and LoRA, while a full fine-tune needs several times that memory and often spans multiple GPUs. Check the live GPU pricing for current rates.

Deployment guideDeploy MiMo-V2-Flash step by stepHands-on production setup, GPU configs, and benchmarks for MiMo-V2.5-Pro-FP4-DFlash.Read guide

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MiMo-V2.5-Pro-FP4-DFlash GPU questions