DeepSeek-LLM-1.3b-base
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
- DeepSeek
- Organisation type
- Industry
- Country
- China
- Published
- 5 January 2024
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language
- Task
- Language modeling/generation
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
- 1.3B
- Training data
- 500,000,000,000 tokens
1.3b
500B text 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
- 3.9 × 10²¹ FLOP
- How it was established
- Operation counting
6 FLOP / parameter / token * 1.3*10^9 parameters * 500*10^9 tokens = 3.9×10^21 FLOP
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
https://hf.rst.im/deepseek-ai/deepseek-llm-7b-base/discussions/2 "We don't have plans to release the 1.3B model"
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Confident
Sources
Where this record came from and when it was last checked.
- Last updated
- 28 November 2025
What the numbers mean
Background
DeepSeek-LLM-1.3b-base was published by DeepSeek, in China, in January 2024. The organisation is categorised as industry.
It works in Language, and is recorded as doing language modeling/generation.
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
What went into building it
Producing it required around 3.9 × 10²¹ FLOP of arithmetic, which is a statement about the training budget rather than about inference.
Around 500,000,000,000 tokens went into training it.
Answers
DeepSeek-LLM-1.3b-base — common questions
What GPU do I need to run DeepSeek-LLM-1.3b-base?
None. DeepSeek-LLM-1.3b-base 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 DeepSeek-LLM-1.3b-base open source?
No. DeepSeek-LLM-1.3b-base has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does DeepSeek-LLM-1.3b-base have?
DeepSeek-LLM-1.3b-base has 1.3B parameters. 1.3b. 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 DeepSeek-LLM-1.3b-base?
DeepSeek-LLM-1.3b-base was published by DeepSeek, based in China, categorised as industry.
When was DeepSeek-LLM-1.3b-base released?
DeepSeek-LLM-1.3b-base was published in January 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.
What is DeepSeek-LLM-1.3b-base used for?
DeepSeek-LLM-1.3b-base works in Language, and is recorded as handling language modeling/generation. 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 DeepSeek-LLM-1.3b-base?
Around 3.9 × 10²¹ 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.
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.