MiniMax-H3

1. Model introduction

MiniMax-H3 generates a video and a synchronized stereo audio track in one request. SGLang Diffusion provides a native pipeline for the three public task profiles, split across the released FL2VA (First-and-Last-Frame-to-Video-and-Audio) and Ref2VA (Reference-to-Video-and-Audio) checkpoint partitions:

Video-to-video (V2V) is a supported ref2va use case, not a fourth task value. Run the Ref2VA partition and provide a video reference in conditions.

Use the selected Hub's root model ID: MiniMaxAI/MiniMax-H3 on Hugging Face or MiniMax/MiniMax-H3 on ModelScope. Select the checkpoint variant with --model-variant: fl2va serves both t2va and fl2va, while ref2va serves reference-conditioned requests. SGLang owns the checkpoint-directory mapping; do not point --model-path at a manually downloaded subdirectory.

2. Installation

Install SGLang with the diffusion dependencies:

uv pip install "sglang[diffusion]" --prerelease=allow

For platform-specific setup, see the SGLang Diffusion installation guide.

3. Serve MiniMax-H3

Use the interactive selector to choose a hardware platform, deployment profile, one of the two checkpoint partitions, a request mode, and deployment features. It generates Python and, where available, Docker launch forms. AMD selections use the Python form until an H3-capable ROCm image is validated. The $ cURL button follows the selected request mode and switches the payload across text-only, all three first/last-frame signatures, and the image/audio/video reference combinations listed below. Set Outputs per prompt in the picker’s Env panel to generate more than one output without mixing request sampling controls into the deployment matrix.

The Docker form does not assume the base SGLang image contains optional diffusion dependencies. It installs the platform-specific diffusion extra from the source bundled in the image before starting the server. Set Host media directory in the Env panel for FL2VA, V2V, or Ref2VA; the picker mounts that directory read-only at /data/minimax-h3 inside the container.

Every hardware/topology cell in this picker has completed a real request on that exact GPU model. Approximate load-time features such as online quantization are called out separately in the generated command. Sampling behavior such as Cache-DiT is documented separately below.

Deployment Profile exposes resident and FSDP placement on B200, B300, H200, and H100. Resident is the latency-oriented default; FSDP reduces DiT weight residency at the cost of per-block parameter collectives. Online Quantization appears only on B200 and B300. AMD keeps its resident AITER recipe, while RTX 5090 uses its dedicated layerwise-offload profile.

import { Deployment } from "/src/snippets/_deployment.jsx"; import { config } from "/src/snippets/configs/MiniMaxAI/minimax-h3.jsx";

The selector uses the verified Hugging Face ID. To use ModelScope through the same normal sglang serve path, prefix the copied command with SGLANG_USE_MODELSCOPE=true and replace the model path with MiniMax/MiniMax-H3; keep its selected variant and topology flags unchanged.

For a four-card H200 host, keep the full BF16/FP32 model resident by default. The model fits without FSDP, so this path avoids the per-block parameter all-gathers of the memory-oriented FSDP profile:

sglang serve \
  --model-path MiniMaxAI/MiniMax-H3 \
  --model-variant fl2va \
  --num-gpus 4 \
  --ulysses-degree 4 \
  --performance-mode speed \
  --port 30010

Pure Ulysses4 is also the faster measured topology on H200, not just a capacity default. The 4×H100 TP2 + Ulysses2 recipe below fits on 141 GB H200 cards, but it replaces the Ulysses all-to-all exchange with two per-block tensor-parallel all-reduces and measured slower end-to-end, at about 30 GB lower peak memory per GPU. See the H200 topology comparison in the Benchmarks section for the measured numbers; treat TP2 + Ulysses2 on H200 as a deliberate memory trade, not a latency default.

For 4×H100 80 GB, balance the large packed activation with resident weight sharding. TP2 + Ulysses2 was the fastest measured lossless topology while the Qwen encoder still folds across all four GPUs:

sglang serve \
  --model-path MiniMaxAI/MiniMax-H3 \
  --model-variant fl2va \
  --num-gpus 4 \
  --tp-size 2 \
  --ulysses-degree 2 \
  --performance-mode speed \
  --port 30010

Pure Ulysses4 could not keep the full pipeline resident on 80 GB H100s. Use --tp-size 4 --ulysses-degree 1 when lower resident memory matters more than the last few percent of latency. FSDP remains a verified capacity option, but its per-block weight all-gathers do not make it the H100 speed default:

sglang serve \
  --model-path MiniMaxAI/MiniMax-H3 \
  --model-variant fl2va \
  --num-gpus 4 \
  --ulysses-degree 4 \
  --performance-mode speed \
  --use-fsdp-inference true \
  --port 30010

For a two-card RTX 5090 host, use TP2 and keep 20 DiT blocks resident. Layerwise placement is lossless: it changes parameter placement and transfer scheduling, not the BF16/FP32 denoising or VAE math. This is the fastest measured 32 GB operating point:

sglang serve \
  --model-path MiniMaxAI/MiniMax-H3 \
  --model-variant fl2va \
  --num-gpus 2 \
  --tp-size 2 \
  --ulysses-degree 1 \
  --performance-mode memory \
  --layerwise-offload-components dit,text_encoder,vae \
  --dit-offload-prefetch-size 1 \
  --dit-layerwise-resident-layers 20 \
  --enable-torch-compile false \
  --port 30010

The DiT residency and prefetch knobs apply only to the repeatedly executed DiT blocks. The text encoder and the video VAE decoder blocks use one-layer prefetch with zero resident layers. The video VAE encoder stays resident because its indexed down blocks cannot host executable layerwise hooks; the roughly 577 MiB audio VAE also stays resident because offloading it only adds transfer overhead. This exact recipe was validated on 2× RTX 5090 (32 GB each) and a 377 GiB host; use a 384 GiB-class machine. The latency and memory comparison is collected in the benchmark section below.

The first launch downloads the model through the selected Hub. If the Hugging Face repository requires authentication, export a Hugging Face token in the server environment.

For MiniMax-H3, --performance-mode speed deliberately keeps the DiT eager. The current torch.compile path changes the model's numerical output, so it is not enabled implicitly by any recommended lossless preset. An explicit --enable-torch-compile true remains available for controlled experiments, but it should not be used to generate consistency ground truth.

4. Generate video and audio

MiniMax-H3 uses the asynchronous OpenAI-compatible video endpoint. Choose a generation mode below, submit a job, poll its status, and then download the completed MP4.

MiniMax-H3 supports output durations from 4 through 15 seconds, inclusive. The following request keeps the verified 5-second profile at a 768-pixel short edge. MiniMax-H3 resolves the aligned output canvas and frame count from target.

video_id=$(
  curl -sS -X POST http://127.0.0.1:30010/v1/videos \
    -H "Content-Type: application/json" \
    -d '{
      "model": "MiniMaxAI/MiniMax-H3",
      "prompt": "At night, while their owner sleeps in a bedroom, three cats march in loudly playing tiny brass instruments, then abruptly file out.",
      "seconds": 5,
      "task": "t2va",
      "conditions": [],
      "target": {
        "short_edge": 768,
        "aspect_ratio": "16:9",
        "duration_seconds": 5.0
      },
      "num_outputs_per_prompt": 1,
      "num_inference_steps": 50,
      "flow_shift": 12.0,
      "audio_flow_shift": 3.0,
      "seed": 1101
    }' |
    jq -r '.id'
)

while true; do
  status=$(curl -sS "http://127.0.0.1:30010/v1/videos/${video_id}" | jq -r '.status')
  [ "$status" = "completed" ] && break
  [ "$status" = "failed" ] && exit 1
  sleep 1
done

curl -sS -L "http://127.0.0.1:30010/v1/videos/${video_id}/content" \
  -o minimax-h3-t2va.mp4

The output contract is an MP4 containing H.264 video at 24 fps and one AAC stereo audio stream at 32 kHz.

For fl2va, provide one or two image conditions with role keyframe. The supported frame-index sets are [0], [-1], and [0, -1].

The following request uses one server-local first frame. Use frame_index: -1 for a last frame, or include both entries for first-and-last conditioning.

Choose FL2VA when the supplied image should be the actual first or last frame of the generated clip. Use image-based Ref2VA instead when the image should guide identity, style, or composition without being preserved as an endpoint; Ref2VA may recompose or crop the reference.

curl -sS -X POST http://127.0.0.1:30010/v1/videos \
  -H "Content-Type: application/json" \
  -d '{
    "model": "MiniMaxAI/MiniMax-H3",
    "prompt": "The supplied frame continues with calm, natural motion and synchronized ambient sound.",
    "seconds": 5,
    "task": "fl2va",
    "conditions": [
      {
        "type": "image",
        "uri": "file:///data/minimax-h3/first-frame.png",
        "role": "keyframe",
        "frame_index": 0
      }
    ],
    "target": {
      "short_edge": 768,
      "aspect_ratio": "auto",
      "duration_seconds": 5.0
    },
    "num_outputs_per_prompt": 1,
    "num_inference_steps": 50,
    "flow_shift": 12.0,
    "audio_flow_shift": 3.0,
    "seed": 2101
  }'

V2V uses the reference-conditioning weights. Launch the server with --model-variant ref2va, keep the request task set to ref2va, and provide a video reference in conditions. There is no separate v2v task value.

Use type: "video" when the input may be silent. If the file has a soundtrack, H3 also uses it as an audio reference. Use type: "video_audio" only when both streams are required; that form rejects an input without audio. The prompt tag for the visual stream is <Video 1>; an available soundtrack is exposed as <Audio 1>.

Set conditions[].start_time_seconds to select a segment from a longer source. The default is 0. SGLang seeks the visual stream and soundtrack to the same offset, then decodes at most the requested target duration in one pass; the source is not re-encoded into an intermediate clip.

curl -sS -X POST http://127.0.0.1:30010/v1/videos \
  -H "Content-Type: application/json" \
  -d '{
    "model": "MiniMaxAI/MiniMax-H3",
    "prompt": "Follow the motion and appearance of <Video 1>, changing the setting to a moonlit bedroom while preserving coherent timing.",
    "seconds": 5,
    "task": "ref2va",
    "conditions": [
      {
        "type": "video",
        "uri": "file:///data/minimax-h3/input.mp4",
        "role": "reference",
        "start_time_seconds": 35.0
      }
    ],
    "target": {
      "short_edge": 768,
      "aspect_ratio": "16:9",
      "duration_seconds": 5.0
    },
    "num_outputs_per_prompt": 1,
    "num_inference_steps": 50,
    "flow_shift": 12.0,
    "audio_flow_shift": 3.0,
    "seed": 4101
  }'

Use conditions[].uri for H3 V2V. The generic top-level video_path, video_url, and video_reference upload fields are not lowered into H3 reference conditions.

For ref2va, first launch the reference-conditioning capability with --model-variant ref2va, then provide conditions with role reference. Image, video, and audio references can be combined. Material tags in the prompt use the one-based order for each modality.

An image condition here is semantic reference material rather than a pixel-aligned first frame. Use the FL2VA tab when animating a screenshot from that exact starting composition.

curl -sS -X POST http://127.0.0.1:30010/v1/videos \
  -H "Content-Type: application/json" \
  -d '{
    "model": "MiniMaxAI/MiniMax-H3",
    "prompt": "Use <Picture 1> as the visual subject and <Audio 1> as the sound reference, with coherent natural motion.",
    "seconds": 5,
    "task": "ref2va",
    "conditions": [
      {
        "type": "image",
        "uri": "file:///data/minimax-h3/reference.png",
        "role": "reference"
      },
      {
        "type": "audio",
        "uri": "file:///data/minimax-h3/reference.mp3",
        "role": "reference"
      }
    ],
    "target": {
      "short_edge": 768,
      "aspect_ratio": "auto",
      "duration_seconds": 5.0
    },
    "num_outputs_per_prompt": 1,
    "num_inference_steps": 50,
    "flow_shift": 12.0,
    "audio_flow_shift": 3.0,
    "seed": 3101
  }'

Poll and download any conditioned request with the same job-status and content endpoints used in the T2VA example. Server-local file:// URIs must refer to files visible inside the SGLang server environment.

5. Sampling and output controls

MiniMax-H3 supports more than one output per prompt. The video API accepts num_outputs_per_prompt (or OpenAI-compatible n) from 1 through 10. Offline generation accepts --num-outputs-per-prompt N; --num-outputs N is the short alias. A scalar seed is expanded deterministically as seed + output_index, so the outputs do not reuse the same noise.

Same-prompt fan-out reuses text conditioning. On the verified 2× RTX 5090 recipe, a 5-step two-output request completed in 155.39 seconds versus 78.11 seconds for one output, while producing two distinct valid MP4 files. The independent denoise and decode passes remain sequential on this 32 GB profile to keep peak memory bounded; the grouped path adds essentially no orchestration overhead. Use server replicas when lower wall-clock latency for many variants matters more than per-server memory efficiency.

For example, set "num_outputs_per_prompt": 2 in any request above. After the job completes, download both outputs by selecting each zero-based variant:

video_id="<completed-job-id>"
for variant in 0 1; do
  curl -sS -L \
    "http://127.0.0.1:30010/v1/videos/${video_id}/content?variant=${variant}" \
    -o "minimax-h3-${variant}.mp4"
done

Choose the quality level

quality is a request-scoped sampling parameter with two validated levels:

• "lossless" (default): the exact reference path. Output is bit-exact against the reference implementation and the CI ground truth.
• "high": the audited accelerated path. Quality is guaranteed (the audited Cache-DiT configuration measures SSIM 0.931 / PSNR 28.16 dB against lossless), but output is no longer bit-identical to the reference.

One resident server serves both levels; a quality: "high" request mounts its audited Cache-DiT policy at the batch boundary, and a later quality: "lossless" request removes the hooks before denoising.

Start the validated server once:

sglang serve \
  --model-path MiniMaxAI/MiniMax-H3 \
  --model-variant fl2va \
  --num-gpus 4 \
  --tp-size 1 \
  --sp-degree 4 \
  --ulysses-degree 4 \
  --ring-degree 1 \
  --performance-mode speed \
  --use-fsdp-inference false \
  --enable-torch-compile false \
  --port 30010

Then choose a request level:

Native denoising with no feature-cache approximation. This is the default; omitting the field is equivalent.

{
  "quality": "lossless"
}

The audited accelerated path. Use it when you can trade bit-exactness for latency while keeping output closest to the same-seed lossless trajectory.

{
  "quality": "high"
}

The measured trade-off is:

| quality | Mean inference latency | Speedup | SSIM vs lossless | PSNR vs lossless | Expected trade-off | | --- | ---: | ---: | ---: | ---: | --- | | lossless | 75.10 s | 1.00× | 1.000 | exact | Native reference path | | high | 53.70 s | 1.40× | 0.931 | 28.16 dB | Smallest same-seed visual change |

These numbers use 1344×768, 124-frame, 24 fps T2VA with 50 inference steps, video flow shift 12, audio flow shift 3, and three fixed prompt/seed pairs on 4×H200. The prompts cover a quiet detailed scene, fast multi-subject action, and a moving close-up portrait. inference_time_s is averaged across the three prompts; the quiet-scene point is itself the mean of two repeats.

SSIM and PSNR compare decoded, frame-aligned output with the lossless result for the same prompt and seed. They measure trajectory deviation, not absolute perceptual quality: the high path can produce a different but still plausible realization. It also changes the joint audio-video denoise trajectory, while these two metrics cover video only.

quality: "high" currently accepts only the exact workload and 4×H200 deployment above; other hardware, task modes, request shapes, step counts, or flow shifts fail before denoising. Offline generation uses the same level name, for example sglang generate --quality high.

For manually tuned Cache-DiT experiments outside that validated path, omit the request quality field and set the process-wide environment controls directly. An explicit quality: "lossless" request overrides those controls and restores native denoising:

SGLANG_CACHE_DIT_ENABLED=true \
SGLANG_CACHE_DIT_FN=1 \
SGLANG_CACHE_DIT_BN=0 \
SGLANG_CACHE_DIT_WARMUP=4 \
SGLANG_CACHE_DIT_RDT=0.12 \
SGLANG_CACHE_DIT_MC=2 \
sglang serve \
  --model-path MiniMaxAI/MiniMax-H3 \
  --model-variant ref2va \
  --num-gpus 8 \
  --ulysses-degree 8 \
  --performance-mode speed \
  --port 30010

6. Runtime feature recipes

The recommended speed launch already combines resident components with Ulysses sequence parallelism. Validation status below applies only to the listed hardware and topology; it is not inherited by a similar GPU family.

The verified parallel, placement, and matching-signature BCG paths keep the BF16/FP32 weights and denoising math. torch.compile is the exception called out above. Always use the eager BF16/FP32 launch when producing CI consistency ground truth.

For the validated 1344×768 Ref2VA profile, use a 5504-row text bucket so both the server warmup and reference-conditioned requests share the captured signature:

sglang serve \
  --model-path MiniMaxAI/MiniMax-H3 \
  --model-variant ref2va \
  --num-gpus 8 \
  --ulysses-degree 8 \
  --performance-mode speed \
  --enable-breakable-cuda-graph true \
  --warmup-resolutions 1344x768 \
  --bcg-text-buckets 5504 \
  --port 30010

BCG is lossless for a matching captured signature, but capture reserves extra GPU memory. Re-measure the live H3 text length before reusing this bucket for a different task profile, reference set, resolution, or prompt template.

On the verified 8× B200 topology, quantize the BF16 transformer at server load:

sglang serve \
  --model-path MiniMaxAI/MiniMax-H3 \
  --model-variant ref2va \
  --num-gpus 8 \
  --ulysses-degree 8 \
  --performance-mode speed \
  --quantization fp8 \
  --port 30010

H3 automatically keeps its video/audio patch projections, timestep MLP, and final video/audio heads in FP32. All other linear layers have stable full module prefixes, so additional layers can be kept unquantized:

sglang serve \
  --model-path MiniMaxAI/MiniMax-H3 \
  --model-variant ref2va \
  --num-gpus 8 \
  --ulysses-degree 8 \
  --quantization fp8 \
  --quantization-ignored-layers blocks.0.attn token_refiner \
  --port 30010

7. Configuration notes

• MiniMax-H3 produces the canonical 24 fps output; request duration is expressed through target.duration_seconds.
• target.duration_seconds must be between 4 and 15 seconds, inclusive. The command picker defaults to the verified 5-second profile.
• Use a 768-pixel short edge for the released quality recipe. The aligned output dimensions are derived from target.aspect_ratio.
• flow_shift controls video diffusion and audio_flow_shift controls audio diffusion.
• V2V uses task: "ref2va" with a video or video_audio reference; it is served by the Ref2VA partition and is not a separate public task value.
• conditions[].start_time_seconds selects a non-negative offset for a video reference. Its visual and audio streams are always sought together.
• Ref2VA condition order is semantic and must match the one-based material tags in the prompt. For Ref2VA, target.aspect_ratio: "auto" resolves to the model's 16:9 fallback rather than inheriting a reference asset's geometry.
• The distilled pipeline uses a single denoising branch, so CFG parallelism does not apply. Do not enable it: --enable-cfg-parallel true or --cfg-parallel-size greater than 1 is rejected instead of duplicating the positive branch. Explicitly disabling CFG, or setting its size to 1, remains a valid no-op.
• The released visual VAE quality recipe uses overlapping tiled decode. SGLang keeps that recipe by default and distributes complete tiles across the decode group; this changes scheduling, not the computation inside each tile.
• H3 rejects --vae-config.parallel-decode-mode spatial and spatial_shard: validation found output mismatches. Use the default released tiled recipe.
• Keep the default --encoder-parallel auto. With the server’s default batching_max_size of 1, single-node H100/H200/B200/B300 recipes with peer-to-peer access fold the Qwen text encoder over otherwise idle Ulysses ranks. This is separate from DiT tensor parallelism. A pure-TP recipe already shards the encoder over its TP group and does not add a world fold.
• For throughput-oriented serving, select DP (batched throughput). The picker pairs --encoder-parallel dp with an editable --batching-max-size greater than 1; compatible requests are distributed across ranks, while every rank keeps a full encoder replica. Encoder DP requires TP1 and DiT DP1, so it is disabled for the H100 TP2 + Ulysses2 and RTX 5090 TP2 recipes. It provides no benefit for a batch of one and is not bitwise-identical to the folded deployment.
• Use explicit Fold to prioritize single-request latency and encoder memory on a measured high-bandwidth single-node topology. Use Replicate as the compatibility path when folding or encoder DP is unsuitable.
• --use-fsdp-inference true shards only the DiT. MiniMax-H3 preserves the original FP32 dtype of its patch, time, and output projections during FSDP all-gather, so this path does not trade numerical correctness for memory. On 4×H100, prefer TP2 + Ulysses2 for speed; use FSDP as an explicit capacity policy rather than assuming it is faster.
• speed keeps model components resident, auto applies the model-aware 120 GiB residency threshold, and memory enables the memory-saving placement policy. Explicit --layerwise-offload-components overrides that placement list. DiT residency/prefetch knobs are scoped to the DiT; the text encoder and video VAE decoder use one-layer prefetch and zero residency, while the H3 video VAE encoder stays resident. When memory is combined with explicit FSDP, H3 instead keeps the sharded DiT on GPU and layerwise-offloads the text encoder and executable VAE decoder blocks. Use speed only after confirming that the complete target workload fits.
• Breakable CUDA graph execution is an explicit opt-in, not part of the recommended speed preset. It requires --enable-breakable-cuda-graph, every served size in --warmup-resolutions, and --bcg-text-buckets that cover the live H3 condition sequence. The validated 1344×768 Ref2VA recipe uses 5504; other task profiles and reference sets may need a different value. It preserves eager output for matching captured signatures, but graph capture consumes additional GPU memory and may provide little latency benefit when Ulysses attention and collectives dominate, so benchmark it on the target topology before enabling it.

8. Benchmarks

The picker exposes resident and FSDP profiles on NVIDIA datacenter GPUs. GPU counts are properties of the selected recipes, not a claim that every platform requires that many GPUs. The detailed tables below report performance only for the configurations with collected measurements:

B300 precision and encoder placement

A 12-configuration sweep on a single 8× B300 host, covering both checkpoint partitions, both transformer precisions, and all three text-encoder placements. It answers one question — how long does one request take, and how much memory does it need.

What was measured

Hardware. 8× NVIDIA B300 SXM6, single node.

Model. MiniMaxAI/MiniMax-H3, both released weight partitions.

Serve command. Exactly the recipe the picker emits for B300, plus the one or two overlay flags under test:

sglang serve \
  --model-path MiniMaxAI/MiniMax-H3 \
  --model-variant fl2va \
  --num-gpus 8 \
  --ulysses-degree 8 \
  --performance-mode speed \
  --host 0.0.0.0 \
  --port 30010

The swept axes are --model-variant (fl2va / ref2va), --quantization (unset for BF16 / fp8), and --encoder-parallel (auto / fold / replicate). Nothing else differs between the 12 servers.

This is a single-request latency sweep (batching_max_size: 1), so encoder DP is intentionally excluded: it cannot distribute a batch of one. Use the picker’s DP (batched throughput) option for a multi-request throughput deployment; the table below does not claim a measured H3 DP speedup.

Driver.

python3 -m sglang.multimodal_gen.benchmarks.bench_serving \
  --host 127.0.0.1 --port 30010 \
  --model MiniMaxAI/MiniMax-H3 \
  --dataset vbench --task text-to-video \
  --num-prompts 1 --max-concurrency 1 \
  --warmup-requests 1 --warmup-inference-steps 50 \
  --extra-body '{"task":"t2va","conditions":[],"target":{"short_edge":768,"aspect_ratio":"16:9","duration_seconds":5.0},"seconds":5,"flow_shift":12.0,"audio_flow_shift":3.0}'

Workload

Results

H200 topology comparison

The same four-card H200 host completed both lossless resident placements with the standard 1344×768, 5-second, 50-step T2VA request (fixed prompt and seed, eager BF16/FP32, back-to-back runs on an otherwise idle host). Latency is the warmed-up request; the first pair uses the default warmup request, the second pair adds --warmup-resolutions 1344x768 so warmup already covers the served resolution:

Ulysses4 stays the H200 latency default: 5.0 % faster end-to-end than TP2 + Ulysses2 once warmup covers the served resolution (1.6 % with the default warmup, where first-request cold start masks the topology gap). TP2 + Ulysses2 shards the DiT weights and holds peak memory about 30 GB per GPU lower, which is why it remains the 80 GB H100 recipe. Matching the warmup request to the served resolution removes the cold first-request cost on both topologies (about 10 s end-to-end on this workload).

H100 topology comparison

The same four-card H100 host completed three lossless placements. TP2 with Ulysses2 was the fastest; TP4 used the least memory:

RTX 5090 capacity run

The verified two-card RTX 5090 host used TP2 with layerwise offload. The full 50-step, 1344×768, 5-second request completed in 559.67 seconds: 525.05 seconds of denoising and 33.61 seconds of decoding, with a 26.3 GiB sampled peak per GPU.

AMD Instinct task and scaling runs

The AMD recipes keep the released BF16/FP32 precision policy and use AITER packed attention. The picker emits the fastest measured topology, 8 GPUs with Ulysses degree 8. All runs below completed full H.264/AAC decoding and representative-frame inspection.

The task matrix used 8 GPUs and 50 denoising steps. The scaling matrix uses one 1344×768, 209-frame T2VA request and changes only the GPU count and matching Ulysses degree:

For a measured lower-count AMD deployment, set both --num-gpus and --ulysses-degree to 4, 2, or 1. AITER packed attention matched segment-wise BF16 SDPA at cosine similarity 0.9999991655 on MI355X and 0.9999991059 on MI300X.