Inflection-2
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
- Inflection AI
- Organisation type
- Industry
- Country
- United States of America
- Published
- 22 November 2023
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language
- Task
- Language modeling, Language modeling/generation, Chat, Question answering
- Numerical format
- FP8
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
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
- 1 × 10²⁵ FLOP
- How it was established
- Hardware,Benchmarks
"Inflection-2 was trained on 5,000 NVIDIA H100 GPUs in fp8 mixed precision for ~10²⁵ FLOPs" (the second 1 is there because of airtable being wonky, it's not a real sig fig)
The training run
What it physically took to train: which chips, how many, for how long, and what that drew from the wall.
- Training hardware
- NVIDIA H100 SXM5 80GB
- Chips used
- 5,000
- Power draw
- 6.9 MW
- Compute cost
- $13,461,144
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
- Hosted access (no API)
- Training code
- Unreleased
via Pi, no API
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Frontier model
- Yes
- Foundation model
- Yes
- Likely above 10²³ FLOP
- Yes
- Why it is tracked
- Significant use,Training cost
- Record confidence
- Confident
Inflection-2 either already powers Pi or soon will: https://inflection.ai/inflection-2 Inflection has claimed that Pi has >1m users: https://x.com/inflectionAI/status/1699100179390210091?s=20
Sources
Where this record came from and when it was last checked.
- Reference
- Inflection-2: The Next Step Up
- Last updated
- 28 November 2025
What the numbers mean
What this model is
Inflection-2 was published by Inflection AI, in the country recorded as United States of America, during November 2023. The publishing organisation is categorised as industry.
It works in the domain of Language, and is recorded as performing the task of language modeling, Language modeling/generation, Chat, Question answering.
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 1 × 10²⁵ FLOP, on hardware recorded as NVIDIA H100 SXM5 80GB. That figure measures what producing the model cost, and has no bearing on how fast it answers.
The reason it appears in this catalogue at all: significant use,Training cost.
Answers
Inflection-2 — common questions
Inflection-2— how many parameters does it have?
No parameter count has been published for it, which is why no memory or speed figure appears on this page.
Inflection-2— who created it?
It was published by Inflection AI, based in United States of America, an organisation categorised as industry.
Inflection-2— when was it released?
It was published in November 2023. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
Inflection-2— what is it used for?
It works in the domain of Language, and is recorded as handling the task of language modeling, Language modeling/generation, Chat, Question answering. These are the areas it was designed around; they describe intent rather than a hard boundary.
Inflection-2— how much compute was used to train it?
Training consumed around 1 × 10²⁵ FLOP, on hardware recorded as NVIDIA H100 SXM5 80GB. 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.
Inflection-2— what GPU do I need to run it?
None. This 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.
Inflection-2— is it open source?
No. Its weights have not been published, so it exists only as a service controlled by its owner.
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.