The Next Frontier of AI Video Is Control
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What is H3 Max and how does post‑training make generative video run in real time?
Generative media is along with the coding agent market, what we call is token market fit. Everyone's waiting for a large consumer moment in AI. I believe H3 Max makes it possible.
Were you surprised by the speed up and the gain you could get from post-training this model?
We have a version called HTMAX Turbo that's public that can generate like a five-second video in like 1.5 seconds from a cost standpoint, it's also like 2x less. People starting
creating these beautiful scenes using an LLM model, GPT Astra, in Blender, and all of a sudden it unlocked the whole new workflow for Hollywood and professional people.
We have been very, very focused towards speed, performance, quality, and now we have a really good pace model. The next month or two is gonna be fully focused on.
What happens when AI video becomes fast enough to generate in real time? A16Z general partner Jennifer Lee sits down with FAL co-founder Gorka Meirdsevin and head of engineering Bakuan Tashkaya to discuss H3 Max and the rapidly changing generative video stack. They unpack how post-training and systems optimization made video generation significantly faster, opening up new experiences where video can run continuously, remember previous scenes, and respond to direction as it plays. But speed is only part of the story. They also discuss the push toward greater control over camera angles, lighting, characters, and motion, and why those tools could make generative video more useful for professional creative workflows.
Welcome Gorgum Batwan to our podcast again. We did the last one last year. This is long overdue and we have such an exciting model to talk about, which is Faus H three Max. The day when it came out I was calling it it's really in the league of its own. Like it's so funny to see the benchmarks where you have the dot of this model on the far left or far right and then everything else is on the other hand. And
that graph is actually log scale, so It's actually further, but we had to fit it in. We had to do log scale.
That is
hilarious. The time portion. The quality is not. Yeah.
For sure. The internet noticed for sure. There were so many viral tweets about it. Like people really played around with this model. Maybe just give us the backstory of what inspired you to post train this open weight model from Minimax and how did you get the quality and speed to
Yeah. F first of all, the Minimax H3 model is the first truly open source very capable, like latest generation video model out there. So even though we work with some of the other model labs to run inference for them, we never had this capability, like had the right to add this capability on top of it. So Minimax came up with their very capable open source model that is truly last generation, can take references, like very familiar architecture to any other video model, we thought this is a great opportunity to go all in and see what like we can do. And again, we did like many different things that we are gonna talk about that combined gave the results that you show on the graphs. But the biggest reason why everything came together for this particular moment was because H3 was the first truly next generation video
That's open source.
What is the idea, given like FAW has been known to be like a generative media inference serving platform? Like what is the idea to get into post-training open weight model? Like you talk quite a bit about it in the blog of like combining the system work with the model itself. Maybe you talk more about the work behind that.
Like generative media is, I would say, along with the coding agent market, what we call is token market fit. And the way we define it is as can a single person productively spend a lot of tokens? And the amount is like 10K amount, something like that. So there is incredible amount of demand in the market to generate video, to generate many things at the same time. time and a person who is doing this for their daily job, they spend in front of a computer and do this all day long, and they spend thousands of dollars, lots of tokens.
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Chapters
8 chapters
1
What is H3 Max and how does post‑training make generative video run in real time?
0:00–4:50
2
How did the team achieve a 35× speed‑up while keeping video quality high?
4:50–8:51
3
Why is controllability (camera, lighting, characters, lip‑sync) the next big challenge for AI video?
8:51–13:46
4
How does continuous‑generation video remember previous scenes and respond to new directions?
13:46–19:08
5
What workflow improvements does H3 Max enable for Hollywood and professional creators?
19:08–23:51
6
How are hardware and system optimizations combined to push past the GPU roof‑line?
23:51–28:43
7
What new creative experiences are emerging from real‑time AI video (live streaming, director‑style control, etc.)?
28:43–34:21
8
How is the AI video market evolving – from consumer hype to Hollywood adoption and future monetization?
34:21–39:21
Speakers
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