What GPU do I need to run MiniMaxAI/MiniMax-H3?
A 33.1B language model for chat and instruction-following. 33.1B parameters, published in BF16. View on Hugging Face
MiniMax-H3 is published by MiniMaxAI on Hugging Face, with 5,532,597 downloads and 4,729 likes to date. It's a unlisted-architecture model built for image-text-to-video, published natively in BF16.
What MiniMax-H3 is
MiniMax-H3 is a 33.1B-parameter language model published by MiniMax on Hugging Face. It is released under Custom license.
License note: a lab-specific license (tagged "other" on Hugging Face); read the model card's own license section before commercial use. Facts in this section are sourced from MiniMax-H3's Hugging Face model card, not benchmarked by Aquanode.
What it's used for
- Chat assistants
- Instruction following
- Synthetic data generation
VRAM required & cheapest live GPU fit
Required VRAM = weight size at each precision, plus a fixed overhead for KV-cache, activations, and fragmentation. Full formula and assumptions: methodology.
| Precision | Weight size | Required VRAM | Cheapest live fit | GPUs needed | Est. $/hr (full fit) |
|---|---|---|---|---|---|
| BF16 | 61.7 GB | 74.0 GB | A100 | 1 | $1.31/hr |
| cheaper alt. | RTX 5060 Ti | 5 | $0.550/hr | ||
| FP8 (quantized) | 30.8 GB | 37.0 GB | RTX 4090 | 1 | $0.441/hr |
| cheaper alt. | RTX 5060 Ti | 3 | $0.330/hr | ||
| INT4 (quantized) | 15.4 GB | 18.5 GB | RTX A5000 | 1 | $0.176/hr |
A GPU is only matched to a row if its hardware supports that precision, and the primary recommendation is always a single-GPU fit when one exists.
INT4 requires a quantized checkpoint actually published for this model, check its Hugging Face page before relying on this row.
Cheapest way to run MiniMax-H3 at its published (BF16) precision: 1× A100, at $1.31/hr per GPU ($1.31/hr total). Quantizing to FP8 or INT4 (rows above) can cost less, but requires a compatible quantized checkpoint to exist for this model.
MiniMax-H3: common questions
Can MiniMax-H3 run on a single GPU?
Yes, but not on a desktop card. At BF16 it needs 74.0 GB of VRAM, more than a single 32 GB desktop card holds. The cheapest live single-card fit is a 80.0 GB A100 at $1.31/hr.
What is the least VRAM MiniMax-H3 can run in?
18.5 GB, at INT4 (quantized), which fits a 24 GB card, against 74.0 GB at BF16. That row assumes an INT4 checkpoint has actually been published for this model, so check its Hugging Face page before planning around it.
Does quantizing MiniMax-H3 lower the GPU bill?
Yes. At BF16 the cheapest live fit is one A100 at $1.31/hr. At INT4 (quantized) it drops to one RTX A5000 at $0.176/hr, provided a quantized checkpoint exists for it.
How to run MiniMax-H3
Run MiniMax-H3 with SGLang
From MiniMaxAI/MiniMax-H3's own deployment docs.
# Original checkpoint, both task families (SGLang, vLLM):
hf download MiniMaxAI/MiniMax-H3 --include "model_index.json" "FL2VA/*" "Ref2VA/*" --local-dir MiniMax-H3
# Or a single task family:
hf download MiniMaxAI/MiniMax-H3 --include "model_index.json" "FL2VA/*" --local-dir MiniMax-H3Source: https://huggingface.co/MiniMaxAI/MiniMax-H3/raw/main/README.md
Run MiniMax-H3 with GGUF quantizations
Prebuilt GGUF weights published at unsloth/MiniMax-H3-GGUF. Run with llama.cpp's llama-server or load the repo directly in LM Studio.
llama-server -hf unsloth/MiniMax-H3-GGUFDeploy MiniMax-H3 on Aquanode
Aquanode has no one-click deploy template for MiniMax-H3; it comes with ComfyUI preinstalled, so you only need to load the checkpoint, not install anything. Aquanode sells GPU pods billed per second, not a hosted inference API.
- Launch the ComfyUI template sized to the requirement above (1× A100 or larger).
- Open a terminal on the pod, or save one of the commands above as a startup script so it runs automatically the first time the pod boots.
- Run the command and connect to the resulting endpoint.
Launch the ComfyUI templateWeight-to-VRAM math, the fit rules, and how live prices are normalized: full methodology.
More MiniMaxAI models
- MiniMax-Music3 (2.4B, F32)
Related reading: A100 pricing and specs, and The best GPUs for AI, ranked.
