vllm-omni/recipes/MiniMaxAI/MiniMax-H3-RTX-PRO-5000.md

MiniMax-H3 on RTX PRO 5000 Blackwell GPUs

This recipe runs MiniMax-H3 in BF16 on 72 GiB RTX PRO 5000 Blackwell GPUs. It contains the validated two-GPU DLO configuration and the recommended resident configurations: TP1 x Ulysses2 with 20 resident layers on two GPUs, TP2 x Ulysses2 on four GPUs, and TP4 x Ulysses2 on eight GPUs. The four- and eight-GPU routes do not require offload.

Capacity requirements

Resource	Requirement
GPUs	2, 4, or 8 x RTX PRO 5000 Blackwell
GPU HBM	72 GiB per GPU
Checkpoint storage	135 GiB per partition
Available system RAM	200 GiB minimum
Recommended system RAM	384 GiB

FL2VA and Ref2VA are separate checkpoint partitions. Start one server at a time on a host sized for the minimum system-memory requirement.

PCIe topology and GPU order

RTX PRO 5000 does not provide NVLink. Before starting the server, identify the closest PCIe pairs and their NUMA affinity:

nvidia-smi topo -m
nvidia-smi nvlink -s

For two GPUs, select the closest PCIe pair on one NUMA node. For four GPUs, select all cards from one NUMA node. On the validated host, physical GPUs (0,1) and (2,3) are the two PXB pairs on NUMA node 0. The order CUDA_VISIBLE_DEVICES=0,2,1,3 maps the Ulysses groups to those local pairs. For eight GPUs, the validated order is 0,4,1,5,2,6,3,7, with host memory interleaved across NUMA nodes 0 and 1. Do not copy these IDs blindly: reproduce the same PCIe and NUMA relationships on the target host.

Recommended serving configurations

Two GPUs

Two 72 GiB cards require distributed layerwise offload. The validated route uses TP1 x Ulysses2, keeps 20 leading DiT layers resident, and streams rank-local weights without AllGather. Eager execution avoids regional-compile instability on this offload path.

export MODEL_ROOT=/path/to/MiniMax-H3
export MODEL="${MODEL_ROOT}/FL2VA"
export PORT=8091

CUDA_VISIBLE_DEVICES=0,1 \
VLLM_WORKER_MULTIPROC_METHOD=spawn \
VLLM_OMNI_VIDEO_SYNC_TIMEOUT=1800 \
numactl --cpunodebind=0 --membind=0 \
vllm serve "${MODEL}" \
  --omni \
  --host 0.0.0.0 \
  --port "${PORT}" \
  --trust-remote-code \
  --num-gpus 2 \
  --tensor-parallel-size 1 \
  --usp 2 \
  --ring 1 \
  --text-encoder-tp-size 2 \
  --vae-patch-parallel-size 2 \
  --vae-parallel-mode tile \
  --vae-use-tiling \
  --diffusion-attention-backend CUDNN_ATTN \
  --enable-distributed-layerwise-offload \
  --dlo-no-use-allgather \
  --dlo-resident-layers 20 \
  --enforce-eager

Four GPUs

The validated baseline uses TP2 x Ulysses2, text-encoder TP4, VAE patch parallelism 4, and explicit cuDNN BF16 attention. Selecting the backend explicitly keeps the recipe independent of platform-default backend changes.

export MODEL_ROOT=/path/to/MiniMax-H3
export MODEL="${MODEL_ROOT}/FL2VA"
export PORT=8091

CUDA_VISIBLE_DEVICES=0,2,1,3 \
VLLM_WORKER_MULTIPROC_METHOD=spawn \
VLLM_OMNI_VIDEO_SYNC_TIMEOUT=1800 \
numactl --cpunodebind=0 --membind=0 \
vllm serve "${MODEL}" \
  --omni \
  --host 0.0.0.0 \
  --port "${PORT}" \
  --trust-remote-code \
  --num-gpus 4 \
  --tensor-parallel-size 2 \
  --usp 2 \
  --ring 1 \
  --text-encoder-tp-size 4 \
  --vae-patch-parallel-size 4 \
  --vae-parallel-mode tile \
  --vae-use-tiling \
  --diffusion-attention-backend CUDNN_ATTN

Eight GPUs

The recommended eight-GPU route uses TP4 x Ulysses2, text-encoder TP8, VAE patch parallelism 8, and host-memory interleaving across both NUMA nodes.

export MODEL_ROOT=/path/to/MiniMax-H3
export MODEL="${MODEL_ROOT}/FL2VA"
export PORT=8091

CUDA_VISIBLE_DEVICES=0,4,1,5,2,6,3,7 \
VLLM_WORKER_MULTIPROC_METHOD=spawn \
VLLM_OMNI_VIDEO_SYNC_TIMEOUT=1800 \
numactl --interleave=0,1 \
vllm serve "${MODEL}" \
  --omni \
  --host 0.0.0.0 \
  --port "${PORT}" \
  --trust-remote-code \
  --num-gpus 8 \
  --tensor-parallel-size 4 \
  --usp 2 \
  --ring 1 \
  --text-encoder-tp-size 8 \
  --vae-patch-parallel-size 8 \
  --vae-parallel-mode tile \
  --vae-use-tiling \
  --diffusion-attention-backend CUDNN_ATTN

For the four- and eight-GPU resident routes, do not add --enforce-eager. Warm the server once before measuring so regional compilation is outside the measured request. The two-GPU DLO route intentionally remains eager.

For Ref2VA, stop the FL2VA server and restart the same command with MODEL="${MODEL_ROOT}/Ref2VA".

Target-hardware validation

All three configurations were exercised on a PCIe-only, dual-socket host with eight RTX PRO 5000 GPUs. The run used PyTorch 2.11.0+cu130, CUDA 13.0, driver 580.95.05, 1344x768 output, 124 frames, and two warmups.

The four-GPU route also received a five-step profiling validation for GPU balance:

Measurement	Result
Maximum externally sampled peak	69,219 MiB (67.60 GiB) per GPU
T2VA worker-reported peak	65,314 MiB
First-frame FL2VA worker-reported peak	66,780 MiB
T2VA maximum GPU kernel-time deviation	0.56%
First-frame FL2VA maximum GPU kernel-time deviation	0.09%

The recommended routes produced the following 50-step results. MiniMax-H3 requested 50 denoise steps and executed 49 denoise updates, so per-step latency is denoise / 49.

GPUs	Workload	Parallelism	E2E (s)	Text encode (s)	Denoise (s)	VAE decode (s)	Per step (ms)	Peak memory (GiB)
2	T2VA	TP1 x Ulysses2, DLO resident 20	515.57	1.19	504.69	8.79	10,300	36.38
2	FL2VA first-frame I2VA	TP1 x Ulysses2, DLO resident 20	553.45	1.27	541.90	8.76	11,059	36.38
4	T2VA	TP2 x Ulysses2	284.01	0.03	278.73	4.36	5,688	67.58
4	FL2VA first-frame I2VA	TP2 x Ulysses2	305.72	0.28	299.85	4.36	6,119	67.58
8	T2VA	TP4 x Ulysses2	163.20	0.04	159.40	2.58	3,253	45.83
8	FL2VA first-frame I2VA	TP4 x Ulysses2	171.62	0.27	167.25	2.57	3,413	45.83

Peak memory is the maximum per-GPU value sampled externally with nvidia-smi. The two-GPU route leaves about 34.7 GiB below the reported 73,415 MiB device capacity, the four-GPU route leaves about 4.1 GiB, and the eight-GPU route leaves about 25.9 GiB. Re-measure memory for longer reference inputs, concurrency greater than one, or a different output shape.

T2VA request example

export API_URL="http://127.0.0.1:${PORT}/v1/videos/sync"

curl -sS --max-time 1800 -X POST "${API_URL}" \
  -F 'prompt=At night, three cats march into a bedroom playing tiny brass instruments, then abruptly file out, with synchronized room ambience.' \
  -F 'width=1344' \
  -F 'height=768' \
  -F 'aspect_ratio=16:9' \
  -F 'fps=24' \
  -F 'num_inference_steps=50' \
  -F 'flow_shift=12' \
  -F 'seed=1101' \
  -F 'extra_params={"task":"t2va","duration":5.0,"audio_flow_shift":3.0}' \
  -o t2va.mp4