vllm-omni/recipes/MiniMaxAI/MiniMax-H3-Disaggregated.md

MiniMax H3 disaggregated text encoder

This opt-in topology runs the Qwen3-VL text encoder as a vLLM stage and sends its hidden states and token-role metadata to an encoder-free diffusion stage. The standard MiniMax H3 recipes remain single-stage and continue to load the text encoder inside the diffusion pipeline.

Start the server

Choose the topology explicitly and load its deployment defaults:

vllm-omni serve MiniMaxAI/MiniMax-H3 \
  --omni \
  --deploy-config vllm_omni/deploy/minimax_h3_disaggregated.yaml

The default deployment assigns stage 0 to GPUs 0-1 with tensor parallel size 2 and max_num_seqs: 1. Stage 1 uses GPUs 2-5 with diffusion tensor parallel size 1, Ulysses degree 4, and VAE patch parallel size 4. Adjust the devices, tensor_parallel_size, and stage 1 parallel_config values in a deployment override for the available hardware. Diffusion quantization, layerwise offload, distributed layerwise offload, VAE parallelism, and USP settings use the same stage 1 options documented in MiniMax-H3.md.

For example, this five-GPU topology assigns one GPU to the encoder and four to the diffusion stage. --stage-overrides keeps placement and parallelism scoped to the owning stage rather than broadcasting an override to both:

CUDA_VISIBLE_DEVICES=0,1,2,3,4 \
vllm-omni serve MiniMaxAI/MiniMax-H3 \
  --omni \
  --deploy-config vllm_omni/deploy/minimax_h3_disaggregated.yaml \
  --stage-overrides '{"0":{"devices":"0","tensor_parallel_size":1},"1":{"devices":"1,2,3,4","tensor_parallel_size":1,"ulysses_degree":4,"vae_patch_parallel_size":4}}'

For memory-constrained deployments, start from the CPU-offload or distributed layerwise-offload profiles in MiniMax-H3.md. Apply memory and quantization options only to Stage 1 with --stage-overrides; retain the encoder's BF16 configuration and the video/audio VAEs' FP32 precision. Select one offload strategy per deployment:

# Stage 1 full-topology model offload, including MiniMax-H3's VAEs. Keep the
# compatibility alias because the compact selector covers only DiT/text encoder.
--stage-overrides '{"1":{"enable_cpu_offload":true}}'

# Stage 1 distributed layerwise offload. Tune resident layers for available RAM.
--stage-overrides '{"1":{"enable_distributed_layerwise_offload":true,"dlo_use_allgather":false,"dlo_resident_layers":20}}'

# Stage 1 online FP8 quantization of the DiT only.
--stage-overrides '{"1":{"diffusion_quantization_config":"{\"transformer\":{\"method\":\"fp8\"}}"}}'

The Stage 1 VAE patch-parallel options remain independent of offload and quantization. See MiniMax-H3.md for memory requirements and hardware-qualified profiles before combining these options.

Stage 1 sets model_loaded.text_encoder: false; it must not load or download text-encoder weights. This H3 topology explicitly keeps its single-replica diffusion stage inline, avoiding serialization of decoded video through a subprocess. It expects both stages to run in one deployment; cross-node payload transport is outside this configuration.

The /v1/videos request schema and extra_params.task values (t2va, fl2va, and ref2va) are unchanged from the single-stage recipe.

Turbo LoRA

The deploy config below carries the four-step 768p contract -- five sigma points, flow_shift=6, audio_flow_shift=3 -- so requests that omit sampling controls do not inherit the 50-step base schedule:

vllm-omni serve MiniMaxAI/MiniMax-H3 \
  --omni \
  --lora-path /path/to/minimax_h3_fl2v_turbo_4step_v1.0_768p_bf16.safetensors \
  --deploy-config vllm_omni/deploy/minimax_h3_disaggregated_turbo.yaml

--lora-path must name one artifact, or a directory holding exactly one; the Turbo repository holds several and a directory of them is rejected as ambiguous. Serving a different artifact means overriding those defaults with that artifact's own contract -- see the Turbo LoRA table for every published row.

The base deployment intentionally retains 50 inference steps for non-LoRA quality. This disaggregated topology is validated on the fl2v artifacts, which serve T2VA and FL2VA; the ref2v artifacts need a --task-type ref2va server.