Llama 2-34B
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
- Meta AI
- Organisation type
- Industry
- Country
- United States of America
- Published
- 18 July 2023
- Authors
- Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, Dan Bikel, Lukas Blecher, Cristian Canton Ferrer, Moya Chen, Guillem Cucurull, David Esiobu, Jude Fernandes, Jeremy Fu, Wenyin Fu, Brian Fuller, Cynthia Gao, Vedanuj Goswami, Naman Goyal, Anthony Hartshorn, Saghar Hosseini, Rui Hou, Hakan Inan,…
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language
- Task
- Language modeling
- Approach
- Supervised
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
- 34B
- Training data
- 2,000,000,000,000 tokens
- Epochs
- 1
- Batch size
- 4,000,000
Llama has been released in 7B, 13B, 34B, and 70B variants.
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
- 4.1 × 10²³ FLOP
- How it was established
- Hardware,Operation counting
All models sizes trained on 2.0T tokens, per table 1 2T * 34b * 6 = 4.08e23 Also trained on 1038336 A100-hours, which is 3.5e23 at 30% utilization. So the utilization was probably around 35%.
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
- $600,470
- Data centre
- Meta’s Research Super Cluster
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.
- Likely above 10²³ FLOP
- Yes
- Why it is tracked
- Highly cited
- Record confidence
- Confident
- Citations
- 16,911
Model has been open-sourced and frequently downloaded. The paper claims that Llama 2 is the current best open-source chat model as of its release date.
Sources
Where this record came from and when it was last checked.
- Reference
- Llama 2: Open Foundation and Fine-Tuned Chat Models
- Last updated
- 25 May 2026
What the numbers mean
About this model
Llama 2-34B was published by Meta AI, in United States of America, in July 2023. It comes out of industry.
It works in Language, and is recorded as doing language modeling.
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
Training and provenance
Training it took roughly 4.1 × 10²³ FLOP of computation, on NVIDIA A100 SXM4 80 GB — a measure of what producing the model cost, not of how fast it answers.
Around 2,000,000,000,000 tokens went into training it.
The reason it appears in this catalogue at all is highly cited.
Answers
Llama 2-34B — common questions
Is Llama 2-34B open source?
No. Llama 2-34B has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does Llama 2-34B have?
Llama 2-34B has 34B parameters. Llama has been released in 7B, 13B, 34B, and 70B variants. 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.
Who created Llama 2-34B?
Llama 2-34B was published by Meta AI, based in United States of America, categorised as industry.
When was Llama 2-34B released?
Llama 2-34B 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.
What is Llama 2-34B used for?
Llama 2-34B works in Language, and is recorded as handling language modeling. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
How much compute was used to train Llama 2-34B?
Around 4.1 × 10²³ FLOP, on 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.
What GPU do I need to run Llama 2-34B?
None. Llama 2-34B 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.
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.