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Hanashi
111 lines
6.7 KiB
Markdown
111 lines
6.7 KiB
Markdown
# MiniMax H3 — concurrency & cost-per-video report
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Written 2026-08-06. Measured numbers come from our B200 pod
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(`bench/results.csv`); everything marked *est.* is extrapolated and needs
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validation on real hardware. Provider/worker-availability research is a
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follow-up task.
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## 1. Can one GPU run two generations at once?
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**Compute: no meaningful headroom.** During denoising — 85–90% of wall time —
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the H3 DiT saturates the GPU's SMs. Two co-scheduled jobs time-slice and each
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run ~2× slower; total throughput is ~flat. This matches vLLM's own profiling
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(DiT ≈ 88% of request time) and SGLang's `num_outputs_per_prompt` scaling,
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which is near-linear in time (2 outputs ≈ 2× one output's duration).
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**What is NOT saturated:**
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| Resource | Observation on B200 |
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|--------------------------------|------------------------------------------------------|
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| VRAM | 48–96 GB peak of 180 GB (pruned int8, by resolution) |
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| Non-denoise phases | text encode, VAE decode, mux, queue gaps: ~10–15% |
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**Utilization caps (MPS / MIG):** the B200 supports both — MPS SM partitioning
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(`CUDA_MPS_ACTIVE_THREAD_PERCENTAGE`) and MIG (e.g. 2 × 90 GB instances, each
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big enough for the pruned-int8 stack). But partitioning divides FLOPs, it
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doesn't create them: two half-GPU workers each generate ~2× slower. The only
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real gain from two workers on one GPU is **pipeline overlap** — worker A's
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text-encode/VAE-decode running under worker B's denoise. Expected benefit:
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**+10–20% throughput at the cost of ~2× per-video latency**. Planned
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experiment on this pod (second ComfyUI instance, concurrent vs sequential
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pairs) will measure the real number.
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**Bottom line:** concurrency = number of GPUs (or GPU partitions, at pro-rata
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speed). Scale horizontally with one resident worker per GPU; don't co-schedule
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for throughput unless the overlap experiment surprises us.
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## 2. Cost per video across GPU tiers
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Reference workload: **5 s clip, 864×480, 20 steps, pruned int8** (the
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cheapest-to-run family; quality holds up well in our tests).
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Measured baseline on B200 (this pod): **66 s** warm generation.
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Scaling anchors from the community/vendors: RTX 4090 Laptop 960×540 ≈ 182 s
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(SageAttention); 2×5090 via SGLang layerwise offload, 1344×768/50 steps ≈ 560 s;
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RTX 3060 12 GB 864×480 < 9 min. Everything else is interpolated from relative
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bf16/int8 compute throughput and should be treated as ±40%.
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Prices are RunPod list (2026-08, Community | Secure) with Vast.ai market
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ranges where relevant.
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| GPU | VRAM | $/hr (community) | $/hr (secure) | time/video | videos/GPU-hr | $/video (community) |
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|------------------|--------|------------------|---------------|-----------------|---------------|---------------------|
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| B200 | 180 GB | $5.89 | $5.89 | **66 s (meas.)**| 54 | $0.108 |
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| H200 | 141 GB | $3.59 | $4.39 | ~85 s *est.* | ~42 | ~$0.085 |
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| H100 SXM | 80 GB | $2.69 | $2.99 | ~150 s *est.* | ~24 | ~$0.112 |
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| H100 PCIe | 80 GB | $1.99 | $2.89 | ~175 s *est.* | ~21 | ~$0.095 |
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| A100 SXM | 80 GB | $1.39 | $1.49 | ~300 s *est.* | ~12 | ~$0.116 |
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| L40S | 48 GB | $0.79 | $0.99 | ~300 s *est.* | ~12 | ~$0.066 |
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| RTX Pro 6000 | 96 GB | $1.69 | $1.99 | ~110 s *est.* | ~33 | ~$0.051 |
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| RTX 6000 Ada | 48 GB | $0.74 | $0.77 | ~250 s *est.* | ~14 | ~$0.051 |
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| RTX 5090 | 32 GB | $0.69 | $0.99 | ~120 s *est.* | ~30 | ~$0.023 |
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| RTX 4090 | 24 GB | $0.34 | $0.69 | ~180 s *est.* | ~20 | ~$0.017 |
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| RTX 3090 | 24 GB | $0.22 | $0.46 | ~450 s *est.* | ~8 | ~$0.028 |
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Vast.ai market rates run lower still: RTX 5090 ~$0.30–0.60/hr, RTX 4090 from
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~$0.20/hr — pushing the 4090 toward **~$0.010/video**, an order of magnitude
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below the B200.
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## 3. What the table implies
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1. **Cheapest concurrency: fleets of 4090/5090-class cards** running pruned
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int8. ~5–10× cheaper per video than big-iron GPUs, if 2–4 min/video latency
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is acceptable. Concurrency N = N cards, at ~$0.25–0.70/hr each.
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2. **Big GPUs buy latency and capability, not economy**: B200/H200 for fast
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turnaround, bf16 quality, full-res 15 s clips, or 8-ref workloads (our 8-ref
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full-res runs peaked near 96 GB — that config simply doesn't fit consumer
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cards).
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3. **The dollar-optimal fleet is probably mixed**: consumer-card pool for bulk
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draft/short generations + a small big-GPU pool for premium/long/ref-heavy
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jobs.
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## 4. Caveats before betting on the cheap tier
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- **VRAM offloading**: 24–32 GB cards need ComfyUI's RAM offload for the
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text encoder; host RAM (64 GB+) and PCIe bandwidth matter. Times above
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include typical offload overhead but vary with host specs.
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- **RunPod Community Cloud has no network volumes** (Secure Cloud only) — each
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community worker must pull ~42 GB of pruned-int8 weights at boot (or bake
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them into the image). At consumer-pod bandwidth that's tens of minutes of
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cold start; image-baking is near-mandatory.
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- **Interruptibility/reliability**: community/marketplace pods can disappear;
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the queue layer must tolerate worker churn.
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- **Quality parity of pruned int8 vs bf16** at these settings is exactly what
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the current benchmark chain is quantifying — check `docs/BENCHMARKS.md`.
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- All *est.* numbers need a 30-minute validation pass per GPU type with
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`bench/minimax_bench.py --suite key` on a rented instance of each.
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## Sources
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- Our measurements: `bench/results.csv` (B200 pod, ComfyUI 0.30, 2026-08-06)
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- [RunPod pricing](https://www.runpod.io/pricing) (retrieved 2026-08-06)
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- [Vast.ai RTX 5090 pricing](https://vast.ai/pricing/gpu/RTX-5090) ·
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[Vast.ai live pricing guide](https://vast.ai/article/how-much-does-it-cost-to-rent-a-gpu-in-the-cloud-live-pricing-guide) ·
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[getdeploying.com 5090 comparison](https://getdeploying.com/gpus/nvidia-rtx-5090)
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- SGLang MiniMax-H3 cookbook (multi-GPU + 2×5090 offload timings):
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https://docs.sglang.io/cookbook/diffusion/MiniMax/MiniMax-H3
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- vLLM-Omni MiniMax-H3 recipe (DiT ≈ 88% of request time):
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https://recipes.vllm.ai/MiniMaxAI/MiniMax-H3
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- Community datapoints: [kingy.ai benchmarks](https://kingy.ai/news/minimax-h3-benchmarks-specs-hardware-review/),
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[HF discussion #3](https://huggingface.co/Comfy-Org/MiniMax-H3/discussions/3)
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