DeepSeek-V3.2
No estimate
No hardware requirements for this model
This model's weights are open, but no parameter count has been published for it. Every memory and speed figure starts from that number, so we would rather show nothing than a fabricated estimate.
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
- 1 December 2025
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
- 4.2 × 10²⁴ FLOP
Pre-trained base was DeepSeek-V3.1-Terminus 3.594058e+24 FLOP [base model] + 2.095014e+23 FLOP = 3.8035594e+24 FLOP for pre-training plus continued pre-training Then, "this framework allocates a post-training computational budget exceeding 10% of the pre-training cost", so 4.18e24
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
- Open — downloadable
- Model access
- Open weights (unrestricted)
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Likely
Sources
Where this record came from and when it was last checked.
- Last updated
- 8 April 2026
What the numbers mean
What this model is
DeepSeek-V3.2 was published by DeepSeek, in the country recorded as China, during December 2025. The category the publisher falls under is industry.
Published weights mean the model runs on your machine rather than someone else's, which is what makes the hardware question below answerable at all.
How it was trained
The training run consumed about 4.2 × 10²⁴ FLOP. That figure measures what producing the model cost, and has no bearing on how fast it answers.
Answers
DeepSeek-V3.2 — common questions
DeepSeek-V3.2— what GPU do I need to run it?
We cannot say. It has open weights, but no parameter count has been published for it, and every memory and speed calculation starts from that number. We would rather show nothing than a fabricated estimate.
DeepSeek-V3.2— is it open source?
Its weights are published, so it can be downloaded and run on your own hardware. Note that open weights is not the same as open source in the full sense — it says nothing about the training data, the training code, or the commercial terms attached.
DeepSeek-V3.2— 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.
DeepSeek-V3.2— who created it?
It was published by DeepSeek, based in China, an organisation categorised as industry.
DeepSeek-V3.2— when was it released?
It was published in December 2025.
DeepSeek-V3.2— where can I download it?
The weights are published, though we do not hold a repository link for it. This site calculates hardware requirements rather than hosting model files.
DeepSeek-V3.2— how much compute was used to train it?
Training consumed around 4.2 × 10²⁴ 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.