MiniMax-M3 GPU Requirements: VRAM & Cheapest GPU
MiniMax-M3 has about 427B 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.
To run MiniMax-M3 for inference at FP16, you need roughly 931 GB of VRAM. The cheapest fit on Spheron is 8x H200 141GB at about $14.16/hr. Quantize to INT4 to run it on a smaller, cheaper GPU.
Estimated peak VRAM including weights, activations, and KV cache. Add 10% headroom for production traffic.
- 01$1.77/hr$14.16/hr8× H200 141GBCHEAPESTHopper · HBM3e
- 02$2.71/hr$21.68/hr8× B200 192GBBlackwell · HBM3e
- 03$9.02/hr$36.08/hr4× B300 288GBBlackwell Ultra · HBM3e
Live pricing aggregated from 5+ data center partners. Per-minute billing, no commitments.
VRAM required to run MiniMax-M3
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.
| Precision | Inference | LoRA fine-tune | Full fine-tune |
|---|---|---|---|
| FP16 | 931 GB | 1396 GB | 3724 GB |
| INT8 | 465 GB | 698 GB | 1862 GB |
| INT4 | 233 GB | 349 GB | 931 GB |
Cheapest GPU to run MiniMax-M3 by precision
Full precision. Best quality, highest memory.
8-bit quantized. ~2x smaller, minimal quality loss.
4-bit quantized. ~4x smaller, runs on smaller GPUs.
Inference vs fine-tuning MiniMax-M3
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-M3, an on-demand H200 141GB 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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