Gorkem Yurtseven and Batuhan Taskaya on generating video faster than it plays
fal post-trained MiniMax's open-source H3 video model into H3 Max, combining model-side post-training with kernel and systems work to generate a five-second clip in about 1.5 seconds. Generating video faster than it plays turns it from a batch render you wait on into a live medium you can direct.
Token Market Fit
Generative media, like coding agents, has 'token market fit': a single person can productively spend around ten thousand dollars a month on tokens, so demand runs far ahead of compute and every efficiency gain is the whole game.
generative media is is I would say along with the coding agent market uh what we call is is token market fit and the way we define it is as can a single person productively spend a lot of tokens and and the the amount is like 10k a month something like that.
Five Seconds in 1.5 Seconds
fal's H3 Max matches or beats the base model's quality while running far faster; its Turbo variant trades a small, noticeable quality drop to generate a five-second clip in about 1.5 seconds.
We have a version called HDMax Turbo that's public that can generate like a 5second video in like 1.5 seconds.
Two Levers Past the Roofline
The speedup compounds two independent levers: post-training the model to run in about 20 diffusion steps instead of 50, and kernel work that lifts chip utilization from 30-40% up toward 70-80%.
This is just essentially how do you go from like running 50 steps to running something like 20 steps, right?
Not an Agent Decode
A video model is not one call but a pipeline (prompt-expansion LLM, diffusion, VAE decode, upscale), each stage optimized separately; even its LLM stage runs single-shot at low batch with no cache reuse, the opposite of an LLM agent decode.
This is not like something like an agent decoding LM workload where you have very high cache rates where you have higher sessions. It's a single shot. You take you give a prompt you get a prompt back and there's no caching. you're operating at low batch sizes.
Two Minutes of Memory, an Hour of Coherence
H3 Max Director keeps a compressed memory of the last two minutes of generated video, then carries an evolving system prompt from two to sixty minutes to hold a scene together while a viewer steers it live.
remembers like the last few scenes. If you think a scene is like 15 30 seconds, then it remembers like the last four to eight scenes and then on top of that there's like a continuously evolving gradually evolving system prompt
The Burst, Not the Line
Unlike an LLM's steady line of gains, generative media stays quiet for months and then bursts all at once when the base model, latency, and controllability finally line up.
This happens at fal once in every couple of months where like the whole company gets gets hold of something and and the creativity just explodes and everyone is just working on a new little app or or a different optimization Laura whatever it might
From Prompt to Director's Chair
The next frontier is control, not speed: references, camera angles supplied as JSON, lighting, and LoRA fine-tunes, all aimed at making the model do exactly what a director asks.
you just essentially like underneath you give a JSON of like I want camera at like 0000 at T0. I want camera at like 90 degrees angle at T1.
Hollywood Wants Point Solutions
Hollywood is fal's fastest-growing segment, and what it wants is small point solutions (extend a clip, change the camera, fix the lighting) rather than generating whole scenes from scratch.
these like small point solutions rather than generating everything from scratch. They want to be able to extend the video a little bit.