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

MiniMax H3

Joint video and audio generation with text, first/last keyframes, and mixed image/video/audio references

Summary

• Vendor: MiniMaxAI
• Model: MiniMaxAI/MiniMax-H3
• Tasks: T2VA, FL2VA, and Ref2VA
• Mode: OpenAI-compatible /v1/videos HTTP serving
• Maintainer: Community

MiniMax H3 is a CFG-distilled joint video/audio diffusion transformer. Its checkpoint has two task-specific DiT partitions:

• FL2VA: text-to-video+audio (t2va) and first-frame-to-video+audio (fl2va)
• Ref2VA: up to 9 images, 3 videos, and 3 audio references in supported image/video/audio combinations (ref2va; audio-only is rejected)

One vLLM-Omni diffusion stage can load both DiTs while instantiating the tokenizer, processor, Qwen3-VL text encoder, video VAE, and audio VAE only once. Requests select the DiT with extra_params.task.

The generated MP4 contains H.264 video and synchronized stereo audio.

Prerequisites

The checkpoint requires Hugging Face access approval. Authenticate once; vllm serve downloads the required components automatically:

hf auth login
export MODEL=MiniMaxAI/MiniMax-H3

The vLLM-Omni pipeline downloads FL2VA/**, Ref2VA/model_index.json, and Ref2VA/transformer/**. It does not download or load the diffusers-format transformer, transformer_ref, or vae weights at the repository root, nor duplicate Ref2VA copies of shared components.

Install vLLM-Omni from the checkout containing MiniMax H3 support. The two-GPU RTX 5090/4090 profiles use cuDNN attention and do not need FlashAttention-4. Install the optional dependency only for the four-GPU B300/GB200 FLASH_ATTN profile:

uv venv
source .venv/bin/activate
uv pip install -e .

To keep FA4 available as an explicit option on Blackwell, install the FlashAttention-4 extra:

uv pip install -e '.[fa4]'

On AMD ROCm, install without the [fa4] extra (FA4 is CUDA-only) and use --diffusion-attention-backend FLASH_ATTN; see AMD ROCm (gfx942 / gfx950).

ffmpeg and ffprobe must be available on PATH. They are used for reference-video preparation and MP4 output.

CPU MP4 response encoding

For CUDA and ROCm deployments, non-streaming MP4 responses are encoded on the host CPU through PyAV/libx264 after generation. The response encoder selects the path automatically at runtime. The server-owned parallel converter accepts supported frame shapes and dtypes with either per-channel-contiguous or strided RGB planes, including interleaved arrays materialized by output transport. Standalone callers without a parallel converter retain the legacy fallback for strided planes. No CLI flag, model declaration, or user configuration is required. Streaming fMP4 output is unchanged.

A community benchmark on 2x Xeon 8480C reported the following comparison between the legacy and direct planar paths (full result):

Metric	Legacy	Direct planar	Change
Median wall time	1.805 s	1.394 s	-22.8%
Median process CPU time	3.613 s	3.207 s	-11.2%
Peak RSS	3182 MiB	2794 MiB	-387 MiB (-12.2%)

Across the 1.0-8.7 s sweep, wall-time improvement was approximately 21.8-22.6%; outputs were byte-identical and full decode passed. This is evidence for host CPU response encoding. Actual gains depend on the CPU and runtime, and should not be interpreted as GPU, DiT, or stage 0 speedups.

Start a server

Pass the repository ID directly. The pipeline uses FL2VA for model discovery and shared components, and loads the second DiT from Ref2VA/transformer.

Memory and storage requirements

Treat GPU HBM, host RAM, and checkpoint storage as separate requirements. Each H3 checkpoint partition (FL2VA or Ref2VA) contains about 134 GiB of BF16 safetensors (about 135 GiB on disk). Keeping both partitions locally therefore needs roughly 270 GiB of model storage. A combined service downloads both; --task-type fl2va or --task-type ref2va downloads only the selected partition.

CPU offload and distributed layerwise offload reduce GPU residency; they do not make the model weights disappear. With --dlo-no-use-allgather, each worker retains its standard-loader rank-local weights in host memory, including pinned CPU buffers used for H2D streaming. Use at least 200 GiB available system RAM before starting the two-GPU recipe; a 384 GiB host is recommended to leave room for the OS, CUDA/PyTorch allocations, request inputs, and filesystem cache. Do not run the FL2VA and Ref2VA servers at the same time on a host sized for this minimum.

The consumer-GPU profiles below are HBM budgets only. They still require the host-RAM budget above.

Single GPU: accuracy and memory first

The single-GPU configuration uses model-level CPU offload. This matches the accuracy-qualified reference path and prevents the Qwen3-VL encoder and DiT from being resident on the GPU at the same time.

export MODEL=MiniMaxAI/MiniMax-H3
export PORT=8091

CUDA_VISIBLE_DEVICES=0 \
VLLM_WORKER_MULTIPROC_METHOD=spawn \
VLLM_OMNI_VIDEO_SYNC_TIMEOUT=1800 \
vllm serve "${MODEL}" \
  --omni \
  --host 0.0.0.0 \
  --port "${PORT}" \
  --trust-remote-code \
  --num-gpus 1 \
  --enable-cpu-offload \
  --diffusion-attention-backend FLASH_ATTN

Use a GPU with enough memory for the active H3 component and enough system RAM for both offloaded DiTs plus the shared components. Model-level offload keeps the two DiTs mutually exclusive on GPU, but adds PCIe/NVLink transfer latency.

Two 24/32 GB GPUs: TP2 distributed layerwise offload

For two PCIe consumer GPUs, combine TP2 with distributed layerwise offload (DLO). The standard loader first creates the rank-local TP shard. DLO keeps that shard in pinned host memory and streams the 30 tail DiT blocks through a shared two-buffer window without a DP AllGather. The first 20 DiT blocks are copied to the GPUs once per denoise stage, reused by every sampling step, and released before VAE decode so the decoder can reuse their HBM.

export MODEL=MiniMaxAI/MiniMax-H3
export PORT=8091

CUDA_VISIBLE_DEVICES=0,1 \
VLLM_WORKER_MULTIPROC_METHOD=spawn \
VLLM_OMNI_VIDEO_SYNC_TIMEOUT=14400 \
vllm serve "${MODEL}" \
  --omni \
  --host 0.0.0.0 \
  --port "${PORT}" \
  --trust-remote-code \
  --task-type fl2va \
  --num-gpus 2 \
  --tensor-parallel-size 2 \
  --usp 1 \
  --ring 1 \
  --text-encoder-tp-size 2 \
  --vae-patch-parallel-size 2 \
  --vae-parallel-mode tile \
  --vae-use-tiling \
  --enable-distributed-layerwise-offload \
  --dlo-no-use-allgather \
  --dlo-resident-layers 20 \
  --enforce-eager \
  --diffusion-attention-backend CUDNN_ATTN

Use the profile that matches the per-GPU memory capacity:

Profile	GPUs	Starting shape	Resident DiT blocks	Attention	Execution	Status
rtx5090	2 x 32 GB	1344x768	20	cuDNN attention	eager	Target-hardware validated
rtx4090	2 x 24 GB	1024x576	12	cuDNN attention	eager	Capacity-proxy starting point

This topology uses all available parallel capacity: TP2 shards both the DiT and text encoder, --dlo-no-use-allgather streams each rank's local TP shard without reconstructing full blocks, and VAE patch parallelism splits tiled decode across both GPUs. cuDNN attention is selected explicitly for the RTX consumer path; the server stays eager to avoid an unqualified compile path.

The resident count changes placement and transfer frequency only; it does not quantize or change the BF16/FP32 denoise math. Re-measure peak memory before increasing it on a different request shape.

RTX 5090 target-hardware validation

At vLLM-Omni commit ae6577ea, one full 50-step T2VA request completed on 2 x RTX 5090 without OOM:

Shape	Frames	Client E2E	Sampled peak/GPU	Output validation
1344x768	124 at 24 FPS	8 min 38 s	approximately 22.6 GiB	H.264 video + 32 kHz stereo AAC; full ffmpeg decode passed

This is a single end-to-end validation run, not a warmed multi-run latency benchmark. The sampled nvidia-smi peak is also not a CUDA allocator high-water mark. The environment used vLLM 0.26.0, vLLM-Omni 0.26.1.dev14+gae6577ea, and PyTorch 2.11.0+cu130. The run record captures the environment, output contract, elapsed time, and sampled peak.

Before the target run, both profiles were exercised on two B300 ranks as an allocation and correctness proxy. At 1344x768, 124 frames, and 50 steps, the 20-layer profile peaked at 27,726 MiB per rank. At 1024x576, the 12-layer profile peaked at 18,888 MiB per rank in a 5-step capacity run. The resident and fully streamed placements produced identical decoded video-frame and audio hashes for the same shape, step count, prompt, and seed. The B300 result does not establish RTX 4090 PCIe latency; treat the 4090 profile as a conservative starting point until it is measured on that GPU.

To run T2VA, FL2VA, image+audio Ref2VA, and two-video Ref2VA in order, validate every MP4's H.264/AAC streams, and retain live server and GPU-memory logs:

RUN_ROOT=/path/to/run-root \
MODEL_ROOT=/path/to/MiniMax-H3 \
GPU_IDS=0,1 \
PROFILE=rtx5090 \
bash examples/offline_inference/minimax_h3/run_h3_2gpu_all_tasks.sh

The script selects 20 resident layers for PROFILE=rtx5090 and 12 for PROFILE=rtx4090; DLO_RESIDENT_LAYERS=N overrides either default.

Four GPUs: throughput-oriented combined service

For a combined service on four high-memory GPUs, use:

• no CPU or layerwise offload;
• Ulysses sequence parallelism degree 4;
• native tiled VAE patch parallelism degree 4;
• regional torch.compile for the repeated DiT blocks;
• dense BF16 TRTLLM_ATTN, with Ring and TP left at 1.

Both DiTs remain resident in this no-offload configuration. If they do not fit, use model-level CPU offload.

export MODEL=MiniMaxAI/MiniMax-H3
export PORT=8091

CUDA_VISIBLE_DEVICES=0,1,2,3 \
VLLM_WORKER_MULTIPROC_METHOD=spawn \
VLLM_OMNI_VIDEO_SYNC_TIMEOUT=1800 \
vllm serve "${MODEL}" \
  --omni \
  --host 0.0.0.0 \
  --port "${PORT}" \
  --trust-remote-code \
  --num-gpus 4 \
  --usp 4 \
  --ring 1 \
  --vae-patch-parallel-size 4 \
  --vae-parallel-mode tile \
  --vae-use-tiling

Do not add --enforce-eager to this performance configuration. The first request includes regional compilation; warm the server once before measuring steady-state latency. H3 is CFG-distilled, so --cfg-parallel-size must remain 1. The H3 VAE supports its native tile mode, not spatial_shard_height or spatial_shard_width.

Attention Backends

On datacenter Blackwell GPUs, MiniMax H3 defaults to dense BF16 TRTLLM_ATTN; no attention backend flag is required. To select it explicitly, use:

--diffusion-attention-backend TRTLLM_ATTN

Stable measurements with the four-GPU profile above put dense TRTLLM_ATTN and FA4 within 2% of each other. TRTLLM_ATTN remains the datacenter Blackwell default and enables the optional optimizations below. Confirm the server log contains Defaulting to diffusion attention backend TRTLLM_ATTN before recording measurements when using the default selection.

FA4 remains available by explicitly selecting the FLASH_ATTN backend:

--diffusion-attention-backend FLASH_ATTN

On Blackwell, FLASH_ATTN selects FA4. Confirm the server log contains Using CuTe FlashAttention-4 on Blackwell before recording FA4 measurements.

TRTLLM_ATTN additionally supports two lossy optimizations for the long main DiT attention sequence: SAGE attention quantization and Skip-Softmax Sparse Attention. SAGE quantizes Q/K to the configured dtype and V to FP8. This example uses fp8_e4m3 for Q/K; B200 also supports int8 Q/K. The TRTLLM SAGE path fixes V to FP8, so vLLM-Omni only exposes the Q/K dtype. The token refiner is a short attention path, so the per_role override leaves SAGE and Skip-Softmax disabled for it. The example enables the calibration-free Skip-Softmax path with threshold=0.05, after the normalized timestep reaches 0.97:

--diffusion-attention-config '{
  "default": {
    "backend": "TRTLLM_ATTN",
    "quant": {
      "dtype_qk": "fp8_e4m3",
      "q_block_size": 1,
      "k_block_size": 16
    },
    "skip_softmax": {
      "threshold": 0.05,
      "disabled_until_timestep": 0.97
    }
  },
  "per_role": {
    "minimax_h3.token_refiner": {
      "backend": "TRTLLM_ATTN"
    }
  }
}'

For configuration details, see TRTLLM_ATTN Backend and Skip-Softmax and TRTLLM_ATTN SAGE Quantization.

Text encoder tensor parallelism

The Qwen3-VL text encoder (~51.5 GB in BF16 for the retained 50 layers) is by default fully resident on the DiT main rank. On multi-GPU no-offload runs that rank becomes the peak-memory hotspot. Add --text-encoder-tp-size N to shard the encoder across the first N DiT ranks (the encoder is implemented with vLLM-style tensor-parallel layers and runs with distributed collectives over its own encoder process group):

export MODEL=MiniMaxAI/MiniMax-H3
export PORT=8091

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

N must divide the Qwen3-VL head counts (64 attention heads / 8 KV heads), so valid values on a 4-GPU server are 1, 2, and 4 (1, 2, 4, 8 on 8 GPUs). The encoder TP rank set is the first N DiT ranks; on 4 GPUs --text-encoder-tp-size 4 shards the encoder 4-way, dropping the DiT main rank's no-offload peak by roughly (N-1)/N of the ~51.5 GB encoder while the other ranks each gain ~51.5/N GB. The encoder output remains identical to the reference path within bf16 rounding: every encoder rank all-reduces the row-parallel projections, so the full [seq, 5120] layer-50 hidden state is replicated on every rank.

No restart is needed: task=fl2va routes to FL2VA/transformer, while task=ref2va routes to Ref2VA/transformer. T2VA uses the FL2VA DiT.

Step execution and continuous batching

H3 implements the step-wise execution contract, so the scheduler can admit and retire requests between denoise steps instead of running one request end to end. Add the feature gate, then raise --max-num-seqs to co-batch:

--step-execution --max-num-seqs 4

Co-batched requests are packed into a single sequence that keeps one attention document per request, so a batch costs one DiT forward. That packing requires --diffusion-attention-backend FLASH_ATTN; other backends fall back to one forward per request. --max-num-seqs 1 keeps the conservative single-request step path. Cache acceleration (--cache-backend) is not available in step mode.

!!! warning "Co-batching does not improve H3 throughput for large simultaneous requests" Keep --max-num-seqs 1 unless you specifically need scheduler-level control (admitting and retiring requests between denoise steps). Measured on two H100s (TP2, BF16, 672x384, 209 frames, 30 steps, 4 requests at concurrency 4 with --request-rate inf, i.e. all four submitted at time 0; one packed request is 16384 rows):

| Configuration | Wall time | Mean latency | Peak memory |
|---------------|-----------|--------------|-------------|
| request mode | 174.8 s | 111.5 s | 72.4 GB |
| `--step-execution --max-num-seqs 1` | 179.0 s | 113.8 s | 72.4 GB |
| `--step-execution --max-num-seqs 4` | 182.1 s | 175.7 s | 78.3 GB |

A single H3 denoise step is a compute-bound dense GEMM over an already long
packed sequence, so fusing N requests costs N times the FLOPs and buys almost
no amortization — unlike LLM decoding, which is memory-bandwidth bound.
Going from one request per step to four cuts the per-request denoise cost
only from 1.323 s to 1.291 s (2.4%), which the step-mode bookkeeping then
spends. Mean latency degrades further because co-batched requests finish
together instead of staggered. Quantization moves the absolute numbers
without changing this: with online `int8` the same workload runs in 153.3 s
at 56.9 GB (request mode), and `--max-num-seqs 4` is still 5.0% slower than
request mode.

Two workloads outside this table are unmeasured and may behave differently:

- **Staggered arrivals (admission latency).** A single 16384-row H3 request
  already saturates the GPUs, so submitting all requests at time 0 is the
  one arrival pattern where co-batching cannot win: it can only bunch
  completions. In request mode a new request queues behind the whole
  in-flight generation (~45 s at this size); step mode admits at the next
  denoise-step boundary (~1.3 s). Whether that shows up as a wall-clock
  benefit under Poisson arrivals is not yet measured for H3. To reproduce,
  run request mode vs `--step-execution --max-num-seqs 4` with
  `diffusion_benchmark_serving.py --request-rate 0.05` (roughly one request
  every 20 s) and report mean / p95 latency, which then includes queueing.
- **Small requests.** The numbers above are drawn from 672x384 / 209-frame
  requests. A short clip at lower resolution packs a few thousand rows and
  may not saturate the hardware; co-batching may amortize better there.
  This is also unmeasured.

Online FP8 quantization

MiniMax H3 supports online FP8 quantization of both the DiT and the Qwen3-VL text decoder. The checkpoint remains BF16 on disk; vLLM-Omni creates FP8 weights at runtime and uses dynamic activation scaling during inference. By default, --quantization fp8 quantizes eligible attention and MLP linears in the text decoder, token refiner, and main DiT blocks, as well as the DiT condition and AdaLN projections. The Qwen vision tower, embeddings, norms, RoPE, both VAEs, and the model's FP32 patch, timestep, and output projections keep checkpoint precision.

To select a component, use --diffusion-quantization-config with {"transformer":{"method":"fp8"}} for DiT-only FP8 or {"text_encoder":{"method":"fp8"}} for text-decoder-only FP8. The two entries can be combined. The shorthand below enables both components.

Add this option to an existing H3 server command:

--quantization fp8

Single 96 GB GPU, no-offload capacity check

Use the FL2VA-only partition for this capacity test. Loading the combined service would also load the Ref2VA DiT and would test a different memory budget. A no-offload capacity check should contain none of --enable-cpu-offload, --enable-layerwise-offload, or --enable-distributed-layerwise-offload. VAE tiling changes decode placement but does not offload model weights to the CPU.

The run passes the capacity check when the server initializes, the request finishes without CUDA OOM or Xid errors, peak_used_mib remains below the card's reported memory_total_mib, and ffprobe reports H.264 video plus 32 kHz stereo AAC audio. Report the measured headroom rather than assuming that every nominal 96 GB SKU exposes the same MiB total.

As a capacity proxy only, the same five-second case on one B300 measured a 92,946 MiB whole-device first-request peak and a 92,146 MiB worker peak. Its encode, diffuse, decode, and client wall times were 8.664 s, 134.504 s, 6.016 s, and 151.583 s. These numbers suggest that a 96 GB card may fit, but they are not an RTX PRO 6000 validation: kernels, allocator behavior, and reported device capacity differ across GPUs.

Use ignored_layers to keep any otherwise eligible linear in BF16. H3 resolves the transformer component before constructing the DiT, so names do not start with transformer.. Entries are exact runtime linear prefixes; a parent name such as blocks.0.attn does not exclude its children.

Eligible names are the attn.qkv_proj, attn.out_proj, mlp.fc1, and mlp.fc2 children under token_refiner.blocks.<0-1> or blocks.<0-49>. The other eligible names are condition_proj, blocks.<0-49>.adaln_proj.linear, and final_layer.adaln_proj.linear. For example, keep the first main block's attention projections in BF16 with:

--diffusion-quantization-config \
  '{"transformer":{"method":"fp8","ignored_layers":["blocks.0.attn.qkv_proj","blocks.0.attn.out_proj"]}}'

The structured option replaces --quantization fp8. Online FP8 can be used with H3 layerwise offload and with both DLO transfer paths. The default AllGather path uses the ordinary loader to finalize FP8 weights and scales before sharding them across ranks. --dlo-no-use-allgather instead retains complete rank-local tensors and avoids the synchronized request-wave contract.

AMD ROCm (gfx942 / gfx950)

The sections above describe the NVIDIA CUDA path. This section covers the AMD ROCm path; use it instead of the CUDA commands when running on AMD Instinct GPUs.

MiniMax H3 runs on AMD Instinct GPUs (gfx942 / gfx950) in BF16. Use --diffusion-attention-backend FLASH_ATTN, which resolves to AITER packed varlen attention on both architectures.

Select the device with HIP_VISIBLE_DEVICES (not CUDA_VISIBLE_DEVICES), drop the CUDA-only FLASHINFER_DISABLE_VERSION_CHECK, and install without the [fa4] extra (FA4 is CUDA-only). The VAE uses AITER GroupNorm on ROCm.

Install (ROCm wheel + source vLLM-Omni):

pip install "vllm==0.26.0+rocm723" \
  --extra-index-url https://wheels.vllm.ai/rocm/0.26.0/rocm723
VLLM_OMNI_TARGET_DEVICE=rocm pip install -e . --no-build-isolation

Prebuilt image: vllm/vllm-omni-rocm:minimax-h3. All tasks work out of the box: the image bundles TorchCodec (for image+audio Ref2VA) and ffmpeg (for video-reference Ref2VA).

ROCm single GPU

Single GPU with model-level CPU offload keeps the Qwen3-VL encoder and DiT from being co-resident:

export MODEL="${MODEL_ROOT}/FL2VA"
export PORT=8091

HIP_VISIBLE_DEVICES=0 \
VLLM_WORKER_MULTIPROC_METHOD=spawn \
VLLM_OMNI_VIDEO_SYNC_TIMEOUT=1800 \
vllm serve "${MODEL}" \
  --omni --host 0.0.0.0 --port "${PORT}" --trust-remote-code \
  --num-gpus 1 --enable-cpu-offload \
  --diffusion-attention-backend FLASH_ATTN

ROCm four GPUs

The best-practice CUDA four-GPU configuration works on ROCm with the changes above. It mirrors the CUDA command including Ulysses sequence parallelism, VAE patch parallelism, and text-encoder tensor parallelism, with no CPU offload when the model shards across the GPUs:

export MODEL="${MODEL_ROOT}/FL2VA"
export PORT=8091

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

As on CUDA, H3 is CFG-distilled, so keep --cfg-parallel-size at 1, and the H3 VAE supports only its native tile mode. --text-encoder-tp-size is validated on gfx942; on gfx950 it was not exercised.

Validated ROCm evidence

vLLM-Omni with MiniMax H3 support, BF16. gfx942 rows measured with the vllm/vllm-omni-rocm:minimax-h3 image; gfx950 rows measured with the 0.26.0+rocm723 wheel (HIP 7.2).

Workload	Configuration	Observed result
T2VA, 1344x768, 209 frames, 50 steps	4x gfx942 (MI300X), FLASH_ATTN, USP4, text-enc TP4, VAE PP4 tile	encode 0.09 s, denoise 244.04 s, decode 4.15 s, 267.42 s client E2E; H.264 24 FPS + 32 kHz stereo AAC
FL2VA, 1344x768, 209 frames, 50 steps	4x gfx942 (MI300X), FLASH_ATTN, USP4, text-enc TP4, VAE PP4 tile	encode 13.98 s, denoise 257.58 s, decode 4.11 s, 287.07 s client E2E; H.264 24 FPS + 32 kHz stereo AAC
T2VA, 832x480, ~4 s, 40 steps	1x gfx950 (MI350), FLASH_ATTN, CPU offload	valid MP4 (H.264 + synced audio); ~0.73 s/denoise-step (~1.37 it/s), ~55 s client E2E incl. warmup

gfx942 figures are the mean of three requests after one excluded warmup (external evidence: vllm-project/recipes#732). gfx950 figures are functional-correctness validations, not tuned throughput; the first request includes lazy regional compilation. MI325X (gfx942) and other MI355X SKUs are not listed until their own evidence is added.

HTTP API examples

The following requests use the synchronous endpoint so the returned body can be saved directly as an MP4. The asynchronous POST /v1/videos endpoint can also be used when job polling is preferred.

All four tasks use 24 FPS, 50 sigma points, seed values from the validated workloads, and the checkpoint-reference video/audio flow shifts of 12 and 3. Decimal durations are passed through extra_params.

Set the endpoint once:

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

1. T2VA: text to video and audio

Run this request against the combined service:

curl -sS -X POST "${API_URL}" \
  -F 'prompt=In a snowy blue-purple forest, Ori carefully walks past a sleeping giant; footsteps crunch in the snow while the creature breathes and softly snorts.' \
  -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":8.7,"audio_flow_shift":3.0}' \
  -o t2va.mp4

2. FL2VA: first frame to video and audio

Run this request against the combined service. When width and height are omitted, H3 preserves the first-frame aspect ratio and uses a 768-pixel short edge.

export FIRST_FRAME=/path/to/fl2va_first_frame.png

curl -sS -X POST "${API_URL}" \
  -F 'prompt=A man stands beside a yellow car at night. The car drives away; he follows it with his eyes and begins singing sadly, with synchronized voice and city ambience.' \
  -F 'fps=24' \
  -F 'num_inference_steps=50' \
  -F 'flow_shift=12' \
  -F 'seed=2101' \
  -F 'extra_params={"task":"fl2va","duration":8.7,"audio_flow_shift":3.0}' \
  -F "input_reference=@${FIRST_FRAME};type=image/png" \
  -o fl2va.mp4

Use the same FL2VA partition for the official tail-keyframe forms. A single image with frame_indices=[-1] conditions the last frame; two ordered images with frame_indices=[0,-1] condition the first and last frames:

export LAST_FRAME=/path/to/fl2va_last_frame.png
export FIRST_FRAME=/path/to/fl2va_first_frame.png

curl -sS -X POST "${API_URL}" \
  -F 'prompt=The subject moves naturally from the first image to the last image.' \
  -F 'num_inference_steps=50' \
  -F 'flow_shift=12' \
  -F 'seed=2102' \
  -F 'extra_params={"task":"fl2va","duration":8.7,"frame_indices":[0,-1],"audio_flow_shift":3.0}' \
  -F "input_references=@${FIRST_FRAME};type=image/png" \
  -F "input_references=@${LAST_FRAME};type=image/png" \
  -o fl2va_first_last.mp4

3. Ref2VA: image-only, image/audio, or mixed references

Run these requests against the combined service or a Ref2VA-only service. Image-only Ref2VA omits audio_reference; adding one or more audio references is optional. The typed fields accept one object or an ordered JSON list. audio_reference accepts an HTTP(S) URL or a data: URL. In one terminal, expose the local reference assets to the serving host:

python -m http.server 8092 \
  --bind 127.0.0.1 \
  --directory /path/to/reference_assets

Then submit an image-only request from another terminal:

export REF_IMAGE=/path/to/reference_assets/ref2va_image.png

curl -sS -X POST "${API_URL}" \
  -F 'prompt=A white cat sits on a beige couch and slowly looks toward the camera.' \
  -F 'aspect_ratio=adaptive' \
  -F 'short_edge=768' \
  -F 'num_inference_steps=50' \
  -F 'flow_shift=12' \
  -F 'seed=3100' \
  -F 'extra_params={"task":"ref2va","duration":8.0,"audio_flow_shift":3.0}' \
  -F "input_reference=@${REF_IMAGE};type=image/png" \
  -o ref2va_image_only.mp4

An image-plus-audio request is:

export REF_IMAGE=/path/to/reference_assets/ref2va_image.png
export AUDIO_URL=http://127.0.0.1:8092/ref2va_audio.mp3

curl -sS -X POST "${API_URL}" \
  -F 'prompt=A white cat with black mustache and eyebrow markings sits on a beige couch, lip-syncing precisely to the complete reference audio before shifting from confusion to deadpan speechlessness.' \
  -F 'width=1344' \
  -F 'height=768' \
  -F 'fps=24' \
  -F 'num_inference_steps=50' \
  -F 'flow_shift=12' \
  -F 'seed=3101' \
  -F 'extra_params={"task":"ref2va","duration":15.0,"audio_flow_shift":3.0}' \
  -F "input_reference=@${REF_IMAGE};type=image/png" \
  -F "audio_reference={\"audio_url\":\"${AUDIO_URL}\"}" \
  -o ref2va_image_audio.mp4

The requested duration should cover the complete audio. If duration is shorter, the reference soundtrack is truncated to the generated clip.

4. Ref2VA: video, separate audio, and mixed references

Run this request against the combined service. Repeat the input_references multipart field once per source video. H3 consumes the videos in form order and preserves their original soundtracks during conditioning.

export SUBJECT_VIDEO=/path/to/green_screen_subject.mp4
export BACKGROUND_VIDEO=/path/to/fairytale_background.mov

curl -sS -X POST "${API_URL}" \
  -F 'prompt=Remove the green screen background of Video 1 and replace it with the fairytale environment from Video 2. Match the background motion to the character actions and relight the character to fit the scene.' \
  -F 'width=1344' \
  -F 'height=768' \
  -F 'fps=24' \
  -F 'num_inference_steps=50' \
  -F 'flow_shift=12' \
  -F 'seed=3101' \
  -F 'extra_params={"task":"ref2va","duration":15.0,"audio_flow_shift":3.0}' \
  -F "input_references=@${SUBJECT_VIDEO};type=video/mp4" \
  -F "input_references=@${BACKGROUND_VIDEO};type=video/quicktime" \
  -o ref2va_video_video.mp4

The server stores uploaded references only for the lifetime of the request and deletes temporary files after generation. A video may use its embedded soundtrack, a separate audio_reference, or both. To send a mixed multipart request, repeat input_references for each image, video, or audio file; the server classifies them by MIME type and preserves the per-type order.

Reference videos must be MP4/MOV with H.264/H.265 video, optional AAC/MP3 audio, 2–15 seconds each, and at most 15 seconds combined. They may still be longer than the generated clip. Use start_time_seconds to select a synchronized segment; for multiple typed video references, pass one value per video in extra_params.start_time_seconds.

Reference images accept JPG/JPEG, PNG, WEBP, HEIC, or HEIF up to 30 MiB. Standalone audio references accept WAV or MP3 up to 15 MiB, with 2–15 seconds per file and at most 15 seconds combined.

Official input matrix and limits

Task	Supported references	Limits
T2VA	text only	prompt must be non-empty
FL2VA	first image, last image, or ordered first+last images	at most 2 images; frame_indices is [0], [-1], or [0,-1]
Ref2VA	image-only, image+image, image+video, video+audio, and mixed image/video/audio	images ≤9, videos ≤3, audios ≤3, total references ≤12; audio requires a visual reference

The H3 output contract is 4–15 seconds at 24 FPS, stereo 32 kHz audio, and a 32-pixel canvas multiple. T2VA requires one named output ratio from 21:9, 16:9, 4:3, 1:1, 3:4, or 9:16. FL2VA always follows the first input image's ratio and ignores a generic aspect_ratio override. Ref2VA defaults to 16:9; adaptive and SGLang's auto spelling are accepted aliases for that default. short_edge controls the 768-pixel canvas and must be 768. num_outputs_per_prompt accepts 1–10 and derives each output seed as seed + output_index. The asynchronous endpoint returns all outputs; the synchronous raw-MP4 endpoint returns the first output when more than one is requested.

Request-scoped quality

Add one of these fields to any HTTP request above. No Cache-DiT startup option is required; H3 installs its conservative profile when a high request arrives and removes it for a lossless request.

-F 'quality=lossless'  # Native reference path for this request.
-F 'quality=high'      # H3's conservative Cache-DiT profile.

Omitting quality preserves the startup default: it uses the reference path normally, or the server-configured profile when the server was started with --cache-backend cache_dit.

Turbo is independent of this quality switch: quality selects a Cache-DiT policy, while Turbo changes the active LoRA weights and sampling schedule. See LoRA below.

The following result was measured on 4× NVIDIA H200 with SP4, text-encoder TP4, 1344×768, 124 frames, 24 FPS, and 50 inference steps. One full lossless warmup was excluded, followed by three fixed prompt/seed pairs in balanced switch order.

quality	Median inference latency	Speedup	SSIM vs lossless	PSNR vs lossless	Expected trade-off
lossless	85.49 s	1.00×	1.0000	exact	Native reference path
high	63.36 s	1.35×	0.9709	34.98 dB	Faster with measured same-seed deviation

Note

high selects a fixed Cache-DiT profile, but its cache hit rate and resulting latency/quality trade-off may vary by hardware, topology, and workload. The values above apply to this deployment and are not universal guarantees. lossless remains the exact reference path.

LoRA

Turbo LoRA

Only the native Diffusers 4-step FL2VA/T2VA v1.0 artifact is supported:

lightx2v/Minimax-h3-Turbo/minimax_h3_fl2v_turbo_4step_v1.0_768p_bf16.safetensors

Download only that file:

export TURBO_DIR=/path/to/minimax-h3-turbo
export TURBO_FILE=minimax_h3_fl2v_turbo_4step_v1.0_768p_bf16.safetensors
hf download lightx2v/Minimax-h3-Turbo "${TURBO_FILE}" --local-dir "${TURBO_DIR}"
export TURBO_LORA="${TURBO_DIR}/${TURBO_FILE}"

Start from a non-offloaded or DLO FL2VA server command and add --task-type fl2va --lora-backend peft --lora-path "${TURBO_LORA}". --lora-path preloads the adapter; each request still activates it and uses the published sampling settings:

-F 'num_inference_steps=5' \
-F 'flow_shift=6' \
-F 'extra_params={"task":"t2va","duration":4.4,"audio_flow_shift":3.0}' \
-F "lora={\"name\":\"h3-turbo-v1.0\",\"path\":\"${TURBO_LORA}\",\"scale\":1.0}"

For FL2VA, change task and add input_reference as shown above. The 8-step, ComfyUI, Ref2VA, and v1.1 artifacts are not supported. This integration is dynamic-only and does not support prefusion or LoRA composition. DLO is supported by keeping the request-switchable LoRA A/B buffers resident on the accelerator while DLO streams only the base blocks; budget for this additional fixed HBM usage. Model-level and standard layerwise offload remain unsupported. The five requested sigma points produce the four denoiser evaluations expected by the Turbo artifact.

FlashGen native LoRA

The FlashGen 4-step T2VA artifact uses the native MiniMax-H3 module layout and declares its distilled sigma schedule in safetensors metadata. It is published on ModelScope:

FlashGen/Minimax-H3-4step-lora-flashgen/minimax_h3_t2va_flashgen_4step_v1.0_768p_bf16.safetensors

Download only that file:

python -m pip install modelscope
export FLASHGEN_DIR=/path/to/minimax-h3-flashgen-lora
export FLASHGEN_FILE=minimax_h3_t2va_flashgen_4step_v1.0_768p_bf16.safetensors
modelscope download FlashGen/Minimax-H3-4step-lora-flashgen \
  --local_dir "${FLASHGEN_DIR}" \
  --include "${FLASHGEN_FILE}"
export FLASHGEN_LORA="${FLASHGEN_DIR}/${FLASHGEN_FILE}"

Start from a non-offloaded or DLO FL2VA server command and add --task-type fl2va --lora-backend peft --lora-path "${FLASHGEN_LORA}". Each request must use T2VA and the distilled interval-count contract:

-F 'num_inference_steps=4' \
-F 'extra_params={"task":"t2va","duration":5.2}' \
-F "lora={\"name\":\"h3-flashgen-v1.0\",\"path\":\"${FLASHGEN_LORA}\",\"scale\":1.0}"

This path rejects Ref2VA and checkpoints that already pin base_schedule in model_index.json. The adapter metadata carries base_schedule=1.0,0.7,0.4,0.15,0.0, so num_inference_steps=4 means four denoiser evaluations, not five sigma points. Request-mode generation may omit the field and take the count from the adapter schedule; --step-execution requires it explicitly, because the step scheduler reads the total step count off the request at admission, before the adapter schedule is known.

DLO is supported in request-mode generation on the same terms as the Turbo adapter: the request-switchable LoRA A/B buffers stay resident on the accelerator while DLO streams only the base blocks, so budget for that additional fixed HBM usage. The native artifact is rank 64 over 259 target modules, and its packed qkv_proj and fc1 layers reuse the full-input A tensor per slice while B carries slice-local output rows, so the resident footprint exceeds the on-disk payload; measure it for your parallel layout rather than assuming the checkpoint size. Pure Ulysses replicates the adapter on every rank, while DiT tensor parallelism shards the B buffers. Model-level and standard layerwise offload remain unsupported, and --step-execution cannot be combined with --enable-distributed-layerwise-offload.

To validate a deployment, post the same fixed-seed T2VA request twice with the adapter and twice without it, then compare the four output digests. The adapter is bound and deterministic when each pair matches internally and the two pairs differ from each other.

FastH3 adapter

FastH3 is FastVideo's four-step DMD2 student of H3-Base. It reuses H3's text encoder, VAEs, tokenizers, and schedulers unchanged, replacing 49 denoiser evaluations with four. It is fused into the checkpoint at load time rather than switched per request, because it carries full-rank deltas that no LoRA layer can express.

The bundle publishes four variants, so download one and point --lora-path at it; the repository root is refused rather than guessed at:

export FASTH3_DIR=/path/to/fasth3
hf download FastVideo/FastVideo-FastH3-4-step-Preview-v1-LoRA \
  dense-datafree/adapter_model.safetensors --local-dir "${FASTH3_DIR}"
export FASTH3_LORA="${FASTH3_DIR}/dense-datafree/adapter_model.safetensors"

Add --task-type fl2va --lora-path "${FASTH3_LORA}" to a non-offloaded server command. T2VA is served by the FL2VA partition, so --task-type fl2va is correct even though FastH3 preview v1 distills T2VA only. Because the adapter is fused, --lora-backend does not apply and a request carrying a lora= field is rejected rather than served without the adapter it asked for.

-F 'num_inference_steps=4' \
-F 'extra_params={"task":"t2va","duration":4.4}'

Requests must ask for num_inference_steps=4 and task=t2va: the release's five sigma points bound four denoiser evaluations, and that count is what the step scheduler admits a request on. The server denoises on the release's own ladder, keeping H3's per-modality shifts at the checkpoint values, so a request that overrides flow_shift or audio_flow_shift is rejected - it would sample the student at noise levels it was never distilled at.

Only a release that identifies itself as FastH3 is fused; any other fastvideo-lora-v2 adapter stays on the dynamic LoRA route. A claimed artifact is then held to its own metadata: one that misdeclares its tensor counts or leaves a transformer block unedited is refused at startup instead of serving mostly base H3 weights on a four-step schedule. Offload is refused for the same reason - --enable-cpu-offload, --enable-layerwise-offload and --enable-distributed-layerwise-offload all bypass the fusion, so they fail fast.

The VSA variants are supported through FastVideo's external kernel. Install a fastvideo-kernel build that provides the fastvideo_kernel Python module, then add the following flags to the same command:

--diffusion-attention-backend FASTVIDEO_VSA \
--fastvideo-vsa-topk 64

FastH3 VSA applies its learned .set_weight compression gates to the complete packed [text | cond | audio | video] document using the official H3 geometry: text/condition/audio prefix tiles never cross segment boundaries, and target video rows use (4, 4, 4) 3-D tiles (64 tokens). Prefix queries remain dense; video queries select all prefix tiles plus the configured top-k video tiles. Pure Ulysses sequence parallelism is supported: the learned gate follows the same sequence-to-head all-to-all as Q/K/V before VSA runs. Ring and all-gather SP remain unsupported because they do not present a complete packed sequence to each block-sparse kernel rank.

The Dense / Data-Free variant does not require fastvideo-kernel and should be served with a dense attention backend.

Measured on 8x NVIDIA B300 with USP8, VAE patch-parallel 8, TRTLLM_ATTN, at 1344x768, 4.4 s, seed 1101, one warmup excluded and two runs recorded:

Adapter	Steps	End-to-end	Diffusion engine
none (base H3)	50	25.8 / 26.4 s	16.22 / 16.28 s
FastH3 Dense	4 (5 sigma points)	11.7 / 11.8 s	2.37 / 2.36 s

The denoising speedup is 6.9x. End-to-end is 2.2x because text encoding, VAE decoding and muxing are a fixed cost that dominates a clip this short; longer generations move the end-to-end figure toward the denoising one. Fusing the adapter does not measurably change startup: weight loading took 77.3 s with it against 85.8 s without.

Key parameters

Parameter	Recommended value	Notes
quality	omitted or lossless	Request-level quality intent; high dynamically installs H3's conservative Cache-DiT profile
extra_params.force_refresh_step_hint	omitted	Optional positive 1-based denoising-step hint for an active Cache-DiT request; pair with extra_params.force_refresh_step_policy=once or repeat
task	t2va, fl2va, or ref2va	Passed in extra_params; selects the task-specific DiT
duration	Workload-specific	Decimal seconds in extra_params; converted to H3-compatible frame count
fps	24	H3 output FPS is fixed
num_inference_steps	50	Matches the reference accuracy workloads
flow_shift	12	Video sigma shift
audio_flow_shift	3	Audio sigma shift, passed in extra_params
seed	Task-specific	Use a fixed value for reproducibility
aspect_ratio	Task-specific	T2VA requires a named ratio; FL2VA follows the input image; Ref2VA defaults to 16:9
short_edge	768	H3 shape policy requires exactly 768 when width/height are omitted
num_outputs_per_prompt	1	1–10; async API returns every output
start_time_seconds	0	Reference-video segment start; use a list in extra_params for multiple videos
width, height	Multiples of 32	Output aspect ratio must be between 1:4 and 4:1

ComfyUI Frontend

Users can also use a ComfyUI frontend to interact with a hosted MiniMax-H3 service. The ComfyUI frontend can run in a separate environment or machine. Refer to vLLM-Omni ComfyUI Integration for details.

Validated four-GPU evidence

The four-GPU recommendation was measured on four NVIDIA B300 GPUs with one excluded warmup followed by three requests.

Workload	Configuration	Observed result
FL2VA, 209 frames, 1248x768	no offload, U4, VPP4 tile, regional compile	86.964 s mean HTTP client latency
Two-video Ref2VA, 362 frames, 1344x768	no offload, U4, VPP4 tile, regional compile	784.394 s accounted model-stage mean

These measurements describe the validated shapes rather than a general throughput guarantee. Multi-video Ref2VA is much slower because the two reference videos expand both the Qwen3-VL vision sequence and the packed DiT attention sequence.

Validated FP8 evidence

With eager DiT/text-encoder TP2 and VAE tiling, the 384x672, 107-frame, 10-step quality case measured LPIPS 0.1156 (limit 0.20), PSNR 23.6316 dB, audio spectral cosine 0.9589 (minimum 0.80), and audio RMS ratio 0.9342. The resident per-GPU peak was 68.52 GiB for BF16 and 53.51 GiB for FP8, a 22% reduction.

For direct human inspection, see the external BF16 versus global-FP8 comparison, which includes matched five-second T2VA, I2VA, and Ref2VA videos. The comparison sources also record per-task fidelity metrics and provenance without storing generated media in this repository.

TeaCache acceleration

TeaCache reuses DiT block residuals across denoising steps when consecutive timestep embeddings are similar. MiniMax-H3 TeaCache is currently calibrated only for the FL2VA partition. In combined serving, FL2VA requests use TeaCache while Ref2VA requests run uncached; Ref2VA-only serving rejects TeaCache.

TeaCache and Cache-DiT are mutually exclusive; pick one cache backend per server.

The model-specific default and examples use rel_l1_thresh=0.17, which provided the best conservative speed/quality balance in the validated 107-frame T2VA workload. Lower values may produce few or no cache hits, while higher values can improve performance at the cost of output quality. Validate the threshold on representative prompts and generation settings before changing it.

Offline (Python API)

Export the directory containing the FL2VA and Ref2VA subdirectories before running the Python example, for example export MODEL_ROOT=/models/MiniMax-H3.

import os

from vllm_omni import Omni
from vllm_omni.inputs.data import OmniDiffusionSamplingParams

omni = Omni(
    model=os.path.join(os.environ["MODEL_ROOT"], "FL2VA"),
    cache_backend="tea_cache",
    cache_config={"rel_l1_thresh": 0.17},
    trust_remote_code=True,
    enable_cpu_offload=True,
)
outputs = omni.generate(
    "A quiet cinematic night scene with matching ambient sound.",
    OmniDiffusionSamplingParams(
        height=256,
        width=448,
        num_frames=29,
        fps=24,
        num_inference_steps=50,
        seed=42,
        extra_args={
            "task": "t2va",
            "duration": 4.0,
            "aspect_ratio": "16:9",
            "flow_shift": 12.0,
            "audio_flow_shift": 3.0,
        },
    ),
)

Online serving

vllm serve "${MODEL_ROOT}/FL2VA" \
  --omni \
  --trust-remote-code \
  --cache-backend tea_cache \
  --cache-config '{"rel_l1_thresh":0.17}'

Known limitations

• TeaCache is calibrated for FL2VA only; Ref2VA requests run uncached.
• Combined serving requires sibling FL2VA and Ref2VA directories, loads both task-specific DiTs, and loads shared components once from FL2VA.
• Request mode executes one generation request per diffusion batch. Use --step-execution with --max-num-seqs N to admit several requests at once (see Execution modes); step mode does not support cache_backend.
• The first regional-compile request is a warmup and should not be included in steady-state performance measurements.
• The serving path accepts fewer references than the model supports. H3 documents up to 9 images, 3 video clips, and 3 audio clips (12 files) per Omni Reference request; the current vLLM-Omni path takes exactly one image plus one audio reference, or one or more videos with no separate audio_reference (it uses the source soundtracks).
• The 768 px short-edge mode is available for T2VA and FL2VA; 1344x768 is the documented 16:9 request shape.
• --cfg-parallel-size > 1 is rejected by design (CFG-distilled, no negative branch).
• VAE patch parallelism requires size 1 or the full DiT group size and supports the H3 native tile mode only.
• A U2 x Ring2 hybrid currently fails with an attention-mask length mismatch; use pure Ulysses.
• Online FP8 with DLO AllGather temporarily materializes the complete FP8 model in host memory on every rank during startup before retaining only each rank's shard. Size startup host memory for that transient peak.
• TeaCache and Cache-DiT cannot be enabled on the same server.
• Image+audio Ref2VA accepts exactly one image and one audio reference.
• Video Ref2VA accepts one or more video files, but not an additional standalone audio reference.
• Pure Ulysses still replicates the full DiT on every rank, so smaller-memory GPUs cannot use --usp N --tp 1 as a resident capacity path. Use DiT tensor parallelism or model-level CPU offload; text-encoder TP alone is not sufficient.

Additional resources

• Supported models
• Video API
• Diffusion parallelism