MP4
No estimate
No hardware requirements for this model
The weights for this model have not been published, so it cannot be downloaded or run on your own hardware at any size. It is reachable only through its provider, and no graphics card changes that.
On record
Full specification
Everything on record for this model. Most of it describes how it was trained rather than how it runs — useful context for judging how much work went into it, and how it compares with models built at a different scale.
Origin
Who built this model, where, and when it was published.
- Organisation
- 310.ai
- Organisation type
- Industry
- Country
- United States of America
- Published
- 15 July 2024
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Biology
- Task
- Molecular simulation, Protein generation, Protein design
Size
How large the model is and how much data it was trained on. Parameters are the figure that decides whether it fits on a given graphics card.
- Training data
- tokens
"MP4 is trained using 138K tokens and 3.2B unique datapoints across 70 synchronized tasks. "
Training compute
The arithmetic performed to train the model, measured in floating-point operations. It is a measure of what the training run cost, not of how fast the finished model answers you.
- Training compute
- 9.7 × 10²² FLOP
- How it was established
- Hardware
Assuming (!) AMD Instinct MI300, bf16 980600000000000 FLOP / GPU / sec * 91200 GPU-hours * 3600 sec / hour * 0.3 [assumed utilization] = 9.6585178e+22 FLOP "Likely" confidence, because exact AMD Instinct GPU model type is not reported
The training run
What it physically took to train: which chips, how many, for how long, and what that drew from the wall.
- Chip-hours
- 912,000
Availability
Whether you can obtain the model and run it on your own hardware, which is what decides if any of the graphics-card figures on this page apply.
- Weights
- Closed — provider access only
- Model access
- Unreleased
- Training code
- Unreleased
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Likely
Sources
Where this record came from and when it was last checked.
- Reference
- MP4 for Molecule Programming
- Last updated
- 28 November 2025
What the numbers mean
About this model
MP4 was published by 310.ai, in United States of America, in July 2024. The organisation is categorised as industry.
It works in Biology, and is recorded as doing molecular simulation, Protein generation, Protein design.
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
How it was trained
The training run consumed about 9.7 × 10²² FLOP. That figure describes the cost of creating it and has no bearing on how quickly it generates text.
Answers
MP4 — common questions
What is MP4 used for?
MP4 works in Biology, and is recorded as handling molecular simulation, Protein generation, Protein design. A model can carry several of each, so these are the areas it was built for rather than a limit on what it will attempt.
How much compute was used to train MP4?
Around 9.7 × 10²² FLOP. That measures what producing the model cost and says nothing about how quickly it answers once trained — inference speed comes from memory bandwidth, not from the training budget.
What GPU do I need to run MP4?
None. MP4 is a closed model — its weights were never published, so it cannot be downloaded or run on your own hardware at any price. It is reachable only through its provider.
Is MP4 open source?
No. MP4 has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does MP4 have?
No parameter count has been published for MP4, which is why no memory or speed figure appears on this page.
Who created MP4?
MP4 was published by 310.ai, based in United States of America, categorised as industry.
When was MP4 released?
MP4 was published in July 2024. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
The other direction
Looking at it from the other side?
This page starts from the model. If you already own a card and want to know everything it will run, start from the hardware instead.