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MiniMax-M2.5 GPU Requirements: VRAM & Cheapest GPU

MiniMax-M2.5 has about 229B 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.

229BParameters
125 GBMin VRAM
$1.64/hrCheapest
< 2 minDeploy
MiniMaxAI/MiniMax-M2.5
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229B paramstext-generationminimax_m2693.3K downloads1.5K likesupdated Mar 10, 2026

To run MiniMax-M2.5 for inference at FP16, you need roughly 499 GB of VRAM. The cheapest fit on Spheron is 8x A100 80GB at about $6.56/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× A100 80GBCHEAPEST
    Ampere · HBM2e
    $0.82/hr$6.56/hr
  • 02
    8× RTX PRO 6000 96GB
    Blackwell · GDDR7
    $1.32/hr$10.56/hr
  • 03
    4× H200 141GB
    Hopper · HBM3e
    $3.31/hr$13.24/hr
  • 04
    8× GH200 96GB
    Grace Hopper · HBM3
    $1.88/hr$15.04/hr
  • 05
    8× H100 80GB
    Hopper · HBM3
    $2.01/hr$16.08/hr

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

VRAM required to run MiniMax-M2.5

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
FP16499 GB748 GB1994 GB
INT8249 GB374 GB997 GB
INT4125 GB187 GB499 GB

Cheapest GPU to run MiniMax-M2.5 by precision

FP16
VRAM required499GB

Full precision. Best quality, highest memory.

Cheapest GPU
8x A100 80GB
Ampere · HBM2e
$6.56/hr · $0.82/hr/gpu
8x A100 80GB on Spheron
INT8
VRAM required249GB

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

Cheapest GPU
4x A100 80GB
Ampere · HBM2e
$3.28/hr · $0.82/hr/gpu
4x A100 80GB on Spheron
INT4
VRAM required125GB

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

Cheapest GPU
2x A100 80GB
Ampere · HBM2e
$1.64/hr · $0.82/hr/gpu
2x A100 80GB on Spheron

Inference vs fine-tuning MiniMax-M2.5

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 MiniMax-M2.5, an on-demand A100 80GB 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.

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FAQ / 05

MiniMax-M2.5 GPU questions