quote: The real breakthrough in the NVIDIA SANA team’s Sol Engine work on MiniMax H3！ By splitting generation into a 4-step low-res H3 draft and a 3-step LTX refinement pass at target resolution with Sol-Attn, they’ve crushed 10s 768p latency on a single GB200 from 414s down to 14.93s (27.7x speedup). Replacing heavy VAE decodes with TAEH3/TAEHV while holding the latents stable for refinement is a masterclass in co-designing sampling topology with hardware kernel acceleration. When inference latency collapses this dramatically, unit economics fundamentally shift: a single node can suddenly serve 378K videos a month at 97%+ GPU margins. This is how high-fidelity AI video moves from asynchronous batch rendering to near-instant, interactive infrastructure. Huge respect to the team for setting a new engineering bar for our open-weights ecosystem! 🫡🩵 | 🚀 MiniMax H3 Super Acceleration in Sol-Engine🤩 We pushed H3 far beyond our previous 3–4× optimization regime — reaching 22.2× speedup for 5s video and 27.7× for 10s video vs. the published SGLang baseline. On a single NVIDIA GB200: • 5s 768p: 152.3s → 6.85s (22.2×) • 10s 768p: 414.1s → 14.93s (27.7×) But the more interesting part may be what this means economically. Using MiniMax’s published H3 API price as a reference, we translate inference speed directly into production economics. Under an ideal fully utilized GB200 scenario, Sol-Super can serve about 525 five-second videos/hour, corresponding to roughly $210/hour of output value at the reference API price. Assuming $5.50/GPU-hour, that implies a 97%+ GPU-only gross margin in the idealized model. At full utilization, one GB200 could produce: • 12.6K 5s videos/day • 378K videos/month • equivalent to 525 hours of finished video per month For us, this is the bigger point of inference optimization: a 20×+ speedup does not just reduce latency — it can fundamentally change the unit economics, serving capacity, and viable business models of video generation. 🔗 https://t.co/dI7uBD1dSo

quote: The real breakthrough in the NVIDIA SANA team’s Sol Engine work on MiniMax H3！ By splitting generation into a 4-step low-res H3 draft and a 3-step LTX refinement pass at target resolution with Sol-Attn, they’ve crushed 10s 768p latency on a single GB200 from 414s down to 14.93s (27.7x speedup). Replacing heavy VAE decodes with TAEH3/TAEHV while holding the latents stable for refinement is a masterclass in co-designing sampling topology with hardware kernel acceleration. When inference latency collapses this dramatically, unit economics fundamentally shift: a single node can suddenly serve 378K videos a month at 97%+ GPU margins. This is how high-fidelity AI video moves from asynchronous batch rendering to near-instant, interactive infrastructure. Huge respect to the team for setting a new engineering bar for our open-weights ecosystem! 🫡🩵 | 🚀 MiniMax H3 Super Acceleration in Sol-Engine🤩 We pushed H3 far beyond our previous 3–4× optimization regime — reaching 22.2× speedup for 5s video and 27.7× for 10s video vs. the published SGLang baseline. On a single NVIDIA GB200: • 5s 768p: 152.3s → 6.85s (22.2×) • 10s 768p: 414.1s → 14.93s (27.7×) But the more interesting part may be what this means economically. Using MiniMax’s published H3 API price as a reference, we translate inference speed directly into production economics. Under an ideal fully utilized GB200 scenario, Sol-Super can serve about 525 five-second videos/hour, corresponding to roughly $210/hour of output value at the reference API price. Assuming $5.50/GPU-hour, that implies a 97%+ GPU-only gross margin in the idealized model. At full utilization, one GB200 could produce: • 12.6K 5s videos/day • 378K videos/month • equivalent to 525 hours of finished video per month For us, this is the bigger point of inference optimization: a 20×+ speedup does not just reduce latency — it can fundamentally change the unit economics, serving capacity, and viable business models of video generation. 🔗 https://t.co/dI7uBD1dSo

Source:https://x.com/MiniMax_AI/status/2092008802984001919