InternLM
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
- Shanghai AI Lab,SenseTime
- Organisation type
- Academia,Industry
- Country
- China, Hong Kong
- Published
- 6 July 2023
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language
- Task
- Language modeling
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.
- Parameters
- 104B
- Training data
- 1,600,000,000,000 tokens
"We present InternLM, a multilingual foundational language model with 104B parameters"
"InternLM is pre-trained on a large corpora with 1.6T 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
- 10 × 10²³ FLOP
- How it was established
- Operation counting
6 * 104b * 1.6T = 9.984e23
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 A100 SXM4 80 GB
- Compute cost
- $1,505,257
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
Though they released 7b and 20b models (https://github.com/InternLM/InternLM/tree/main/model_cards) 100b model is not found
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Foundation model
- Yes
- Likely above 10²³ FLOP
- Yes
- Why it is tracked
- SOTA improvement
- Record confidence
- Confident
(from Google-translated page) "In addition to using academic datasets to evaluate InternLM, we also use human examinations to assess its capabilities. InternLM can achieve good scores on examination benchmarks such as MMLU, AGIEval, C-Eval, and GAOKAO-bench that cover different languages and subjects, scoring higher than ChatGPT on multiple benchmarks"
Sources
Where this record came from and when it was last checked.
- Last updated
- 18 December 2025
What the numbers mean
What this model is
InternLM was published by Shanghai AI Lab,SenseTime, in the country recorded as China, during July 2023. The category the publisher falls under is academia,Industry.
It works in the domain of Language, and is recorded as performing the task of language modeling.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
Training and provenance
Training it took a computation budget of roughly 10 × 10²³ FLOP, on hardware recorded as NVIDIA A100 SXM4 80 GB. That figure measures what producing the model cost, and has no bearing on how fast it answers.
Training consumed a corpus of around 1,600,000,000,000 tokens of text.
Its inclusion criterion: sOTA improvement.
Answers
InternLM — common questions
InternLM— who created it?
It was published by Shanghai AI Lab,SenseTime, based in China, an organisation categorised as academia,Industry.
InternLM— when was it released?
It was published in July 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.
InternLM— what is it used for?
It works in the domain of Language, and is recorded as handling the task of language modeling. These are the areas it was designed around; they describe intent rather than a hard boundary.
InternLM— how much compute was used to train it?
Training consumed around 10 × 10²³ FLOP, on hardware recorded as NVIDIA A100 SXM4 80 GB. 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.
InternLM— 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.
InternLM— is it open source?
No. Its weights have not been published, so it exists only as a service controlled by its owner.
InternLM— how many parameters does it have?
It has a parameter count of 104B. "We present InternLM, a multilingual foundational language model with 104B parameters". That figure is the total, and it is what decides how much memory the model needs — roughly half a gigabyte per billion at the compression most people use.
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.