Amazon Titan
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
- Amazon
- Organisation type
- Industry
- Country
- United States of America
- Published
- 28 September 2023
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language, Image generation
- Task
- Semantic search, Image generation, Language modeling/generation, Code generation, Chat, Text-to-image, Translation
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
- 200B
- Training data
- 4,000,000,000,000 tokens
200B dense model https://importai.substack.com/p/import-ai-365-wmd-benchmark-amazon
4T tokens of data, based on comments from amazon engineer James Hamilton at a 2024 talk: https://perspectives.mvdirona.com/2024/01/cidr-2024/ Also cited here: https://lifearchitect.ai/titan/
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
- 4.8 × 10²⁴ FLOP
- How it was established
- Hardware,Operation counting
trained using NVIDIA NeMo: https://blogs.nvidia.com/blog/nemo-amazon-titan/ 13,760 NVIDIA A100 chips (using 1,720 P4d nodes). It took 48 days to train. from https://importai.substack.com/p/import-ai-365-wmd-benchmark-amazon counting operations: 6*200000000000*4000000000000=4.8e+24 gpu usage: 312000000000000(FLOP/s)*0.3*13760*1152*3600=5.3413281792e+24
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
- Chips used
- 13,760
- Wall-clock time
- 1,152 hours (48 days)
- Hardware utilisation
- MFU 27.0%
- Power draw
- 10.9 MW
- Compute cost
- $7,933,465
6ND gives 4.8e24 Training took 48 days on 13,760 NVIDIA A100 chips –> 3.12e14 * 13760 * 48 * 24 * 3600 = 1.78e25 FLOPs at full utilization Implies 0.2696 MFU.
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
- API access
- Training code
- Unreleased
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Frontier model
- Yes
- Likely above 10²³ FLOP
- Yes
- Why it is tracked
- Training cost
- Record confidence
- Likely
Sources
Where this record came from and when it was last checked.
- Last updated
- 28 November 2025
What the numbers mean
What this model is
Amazon Titan was published by Amazon, in United States of America, in September 2023. The organisation is categorised as industry.
It works in Language, Image generation, and is recorded as doing semantic search, Image generation, Language modeling/generation, Code generation, Chat, Text-to-image, Translation.
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
What went into building it
Training it took roughly 4.8 × 10²⁴ FLOP of computation, on NVIDIA A100 — a measure of what producing the model cost, not of how fast it answers.
The training set ran to roughly 4,000,000,000,000 tokens.
Its inclusion criterion is training cost.
Answers
Amazon Titan — common questions
When was Amazon Titan released?
Amazon Titan was published in September 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 Amazon Titan used for?
Amazon Titan works in Language, Image generation, and is recorded as handling semantic search, Image generation, Language modeling/generation, Code generation, Chat, Text-to-image, Translation. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
How much compute was used to train Amazon Titan?
Around 4.8 × 10²⁴ FLOP, on NVIDIA A100. 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 Amazon Titan?
None. Amazon Titan 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 Amazon Titan open source?
No. Amazon Titan has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does Amazon Titan have?
Amazon Titan has 200B parameters. 200B dense model https://importai.substack.com/p/import-ai-365-wmd-benchmark-amazon. 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 Amazon Titan?
Amazon Titan was published by Amazon, based in United States of America, categorised as industry.
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.