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h3-blackwell-runtime/README.md
Daniel Maddern eaf9324145 Add KJ Sage attention backends
2026-08-14 20:28:56 +07:00

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H3 Blackwell Runtime

Direct MiniMax H3 Ref2VA runtime research project. ComfyUI is the checkpoint and correctness oracle, not the target runtime.

First Gate

Inspect the mounted H3 NVFP4 safetensors headers before designing an importer:

python .\tools\inspect_safetensors.py /runpod-volume/ComfyUI/models/diffusion_models/minimax_h3_ref2va_pruned_nvfp4.safetensors

Write the output to artifacts/checkpoints/ on the mounted volume. The result must identify packed weights, scales, and tensor naming before any kernel conversion work begins.

Benchmark Contract

benchmarks/ref2va-960x544-124f.json is the single-GPU performance contract. Record direct-runner results as JSON and compare them with:

python .\tools\compare_benchmark.py --result direct-result.json

DGX Spark

Dockerfile.spark and compose.spark.yml prepare an ARM64 GB10 development image using the existing AEON CUDA 13/SageAttention3 base. The compose target opens a shell only; it does not start inference.

Forgejo Pulls From Spark

The Spark checkout uses Forgejo through the host's published local SSH port and a dedicated key:

cd /home/daniel/aeon-spark-test/h3/h3-blackwell-runtime
git config core.sshCommand 'ssh -i ~/.ssh/id_ed25519_forgejo_h3 -o IdentitiesOnly=yes'
git remote set-url origin ssh://git@127.0.0.1:2222/daniel/h3-blackwell-runtime.git
git pull --ff-only origin master

The private key remains on Spark at ~/.ssh/id_ed25519_forgejo_h3; only its public key is registered in Forgejo.

Runtime Output

Generation and latent-decode tools are quiet by default: they suppress ffmpeg banners and only print compact JSON summaries. Use these flags when debugging:

  • --progress: print per-step sampler timing in tools/direct_t2v_preview.py.
  • --profile-memory: print memory checkpoints in tools/direct_t2v_preview.py.
  • --ffmpeg-loglevel info: show ffmpeg details instead of the default error level.
  • --quiet: suppress JSON summary lines.
  • --vae-dtype float16: use Comfy-style FP16 video VAE decode in tools/direct_t2v_preview.py or tools/decode_video_latent.py; this is the default runtime path. Use --vae-dtype float32 only for exact direct-path diagnostics. tools/direct_t2v_preview.py also accepts H3_VAE_DTYPE.
  • --vae-tile-size 256: set the direct video VAE spatial tile size. tools/direct_t2v_preview.py also accepts H3_VAE_TILE_SIZE.

Standalone tools/compare_*, tools/trace_*, tools/inspect_*, and tools/patch_comfy_* scripts are debugging utilities and remain opt-in by being separate commands.

Hot Runtime Service

tools/serve_hot_runtime.py keeps Qwen, H3, video VAE, and audio VAE resident in one process. Start the optional Spark service with:

docker compose -f compose.spark.yml up -d h3-hot-runtime

Use GET /ready to confirm resident model readiness. Use POST /generate with JSON fields like prompt, output, width, height, frames, steps, seed, and optional attention. Supported request-level attention values are reported by /ready; switching attention does not reload model weights.