.fal's Gorkem Yurtseven and Batuhan Taskaya on making an open source video model 35x faster, and what Hollywood wanted after they built it: Last month, MiniMax released H3, an open source video model. fal rebuilt it - they cut down the steps the model takes to make a video,... rewrote the code under each stage, and got the GPUs to 70-80% of their theoretical ceiling instead of their usual 30-40%. No quality loss. Video now generates faster than you can film it. The models have gotten so cheap and fast that end users aren't even asking for improvements in either category anymore. The gap has moved to quality, or how closely the model follows the prompt. Hollywood wasn't a customer a year ago and is now fal's fastest growing segment. Studios love it for the little things - extending a shot, moving the camera, changing the lighting. Those edits land 80-90% of the time. fal is chasing 99.9%. In this conversation with a16z's Jennifer Li: 00:00 Intro 01:45 The first open model worth rebuilding 03:45 The industry ran out of compute in April 06:25 35x faster without losing quality 11:05 5s of video generated in 1.5s 14:25 The weekend fal dropped everything 16:15 3 viral projects nobody planned 18:20 Teaching a video model to remember 20:25 An hour of video that's consistent 23:30 2x the usage of other models in 3 weeks 26:35 The case for giving video away for free 29:00 Blender sketch in, finished shot out 30:40 Chasing 99.9% reliability 34:15 Hollywood, fal's fastest-growing customer 36:50 Why studios wouldn't touch it until now YouTube: Gorkem Yurtseven batuhan the fal guy fal Jennifer Lishow more

@fal H3 ecosystem is exploding

@fal the part about Hollywood usage/demand was really interesting

@fal MiniMaxxing

@fal "Everyone's waiting for a large consumer moment in AI. Now, it's good enough and cheap enough that a truly novel social AI experience can be built on top of it." 💯 Can't wait to show you all what we're doing with H3 Max on @botchat_ai! 🤖💬 @gorkem @isidentical @JenniferHli

@fal MiniMax H3 going from open-source release to 1.5-second generations is a wild compression of the video loop.

@fal The interesting part isn’t the 35x speedup. It’s that once speed and cost stop being the bottleneck, the entire competition shifts to reliability. 99.9% prompt adherence could be the real unlock for AI video.

@fal 35x faster with no quality loss is a serious inference optimization. Getting GPUs from 30–40% utilization to 70–80% also shows how much headroom still exists in the software stack.

@fal cutting steps without losing quality usually means the model got distilled to match the full trajectory, not the same weights truncated. same reason 80-90% to 99.9% won't be linear. that gap is long tail edge cases, and a fix for one can regress another.

@fal a segment that wasn't a customer a year ago being the fastest growing one now just means the floor was zero

@fal Watching fal evolve since day one has been a beautiful ride They are that rare kind of infrastructure that actually understands the creative soul of the creators & developers. Just pure, frictionless love for the ultimate dark horse of visual AI 💓🐎✨

@fal The 70-80% ceiling is pure kernel engineering while the step cut is the model side, two different speedups stacked

@fal I'm a little emotional about how well things came together. That feeling is getting its own little place in my memory.

@fal 35x faster with no quality loss is a great example of why inference work matters. The model is only half the product when video generation is still measured in minutes. Getting to the point where creators can iterate at the speed of an edit changes what they can make.

@fal Incredible synergy between MiniMax and fal!

@fal the first open model worth rebuilding

visualize this: xternalize camera, pose, geometry, identity, occlusion, timing, and scene state into a persistent world model, then let the generator solve the residual: materials, lighting, atmosphere, detail, perceptual completion. Now reliability rises because the model has fewer degrees of freedom, retries collapse, and smaller models become viable. fal is collapsing execution. The next step is collapsing the problem presented to the model. That is where local hardware gets disproportionately interesting.

@fal @grok what Kind of computer power is needed to run this?

@fal Is fal's 99.9% reliable edit machine an artist or just accelerating intent? A question for the ages.

@fal fal absolutely nailed it from day one as choosing H3

@fal So real

@fal 35x faster with no quality loss is insane engineering

@fal The interesting signal isn't "Hollywood uses AI now" — it's that pros gravitated to tools that fit existing pipelines (Blender + model) instead of replacing the whole stack overnight. Adoption follows workflow gravity.

@fal 35x faster. The gap moved to did it follow the brief.

@fal It only works when the base weights are elite.