DeepSeek-V3.2-Exp TPS calculator
Each card below is assessed against this model at the context length and minimum quality you choose. Speed is an estimate for a single request, calculated from the card's memory bandwidth and the size of the model once compressed.
Calculated for this model
818 cards we hold specifications for
Which GPUs can run DeepSeek-V3.2-Exp?
Set the inputs, read the answer
A longer conversation needs more memory, which can push this model off smaller cards.
Hides cards that would only fit the model by compressing it below this point.
0 cards match
Calculating| Needs | Quantisation | Fit | |||||
|---|---|---|---|---|---|---|---|
|
No card in our catalogue can run this model with these settings. |
|||||||
Speeds are estimates for a single request — one conversation at a time — calculated from memory bandwidth, model size and quantisation. Real throughput varies with the inference runtime and its version. Figures published by hardware vendors measure many simultaneous requests and are much higher.
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
- 29 September 2025
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language
- Task
- Language modeling/generation, Code generation, Quantitative reasoning, Question answering, Search, System control, Instruction interpretation
- Base model
- DeepSeek-V3.1-Terminus
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
- 671B
- Training data
- 943,700,000,000 tokens
671B total, 37B active (assuming same as in the base model)
"We train both the main model and the indexer for 15000 steps, with each step consisting of 480 sequences of 128K tokens, resulting in a total of 943.7B 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
- How it was established
- Operation counting
- Fine-tuning compute
- 5.9 × 10²³ FLOP
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
Continued pretraining compute: 6 * 37B * 943.7B = 2.095e23 FLOP. In addition, V3.2 states post-training compute budget exceeds 10% of total pretraining cost, implying additional post-training FLOPs > 3.8e23 (so finetune total beyond V3.1-Terminus is >5.9e23 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
- Open — downloadable
- Model access
- Open weights (unrestricted)
- Training code
- Unreleased
- Hugging Face
- deepseek-ai
MIT license https://huggingface.co/deepseek-ai/DeepSeek-V3.2-Exp
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.
- Reference
- Introducing DeepSeek-V3.2-Exp
- Last updated
- 19 February 2026
What the numbers mean
What you need to run it
DeepSeek-V3.2-Exp reaches a parameter count of 671B. That is beyond what any single graphics card holds. Running it means either splitting it across several cards or renting hardware built for the job, and every card able to hold it alone is a datacentre part. The number that can: 0.
What this model is
DeepSeek-V3.2-Exp was published by DeepSeek, in the country recorded as China, during September 2025. The publishing organisation is categorised as industry.
It works in the domain of Language, and is recorded as performing the task of language modeling/generation, Code generation, Quantitative reasoning, Question answering, Search, System control, Instruction interpretation.
Its starting point was an existing base model, DeepSeek-V3.1-Terminus. That is the usual way a specialised model is produced.
The weights being open is what puts this page in the calculator rather than only in the catalogue: it is a model you can actually hold. On Hugging Face it is published under the organisation deepseek-ai.
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.
It was trained on a corpus of about 943,700,000,000 tokens of text.
Step by step
How to choose a GPU for DeepSeek-V3.2-Exp
The table above has already assessed every card we hold specifications for against this model. Getting to your answer takes six steps.
-
01
Read the memory figure first
The table lists every card able to hold DeepSeek-V3.2-Exp. That figure, not the headline performance of a card, is what decides whether it runs.
-
02
Decide how long your conversations run
Set the context to what you will actually use. The cache grows with the conversation, and it is the usual reason a card that seemed fine stops fitting DeepSeek-V3.2-Exp.
-
03
Set a quality floor
Each card runs the least-compressed copy it can hold. Setting a minimum quality drops the cards that only manage it by squeezing further than you would want, and holds the comparison at one level.
-
04
Rank by throughput rather than spec sheet
Ranking by tokens per second follows memory bandwidth rather than core counts, for DeepSeek-V3.2-Exp. It will not match a gaming ordering, because generation is bound by memory bandwidth.
-
05
Check the fit verdict before buying
Tight means it loads and works with no room to raise the context later, in the case of DeepSeek-V3.2-Exp. Comfortable means you can grow the context later. That difference matters more than a few tokens per second, so buy for comfortable if you expect to.
-
06
Check the card from the other side
Every card name links to its own page, which runs the same calculation across the whole model catalogue. A card is usually bought for more than one model, so it is worth a look before buying for DeepSeek-V3.2-Exp.
Answers
DeepSeek-V3.2-Exp — common questions
DeepSeek-V3.2-Exp— how many parameters does it have?
It has a parameter count of 671B. 671B total, 37B active (assuming same as in the base model). 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.
DeepSeek-V3.2-Exp— who created it?
It was published by DeepSeek, based in China, an organisation categorised as industry.
DeepSeek-V3.2-Exp— when was it released?
It was published in September 2025.
DeepSeek-V3.2-Exp— what is it used for?
It works in the domain of Language, and is recorded as handling the task of language modeling/generation, Code generation, Quantitative reasoning, Question answering, Search, System control, Instruction interpretation. 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.
DeepSeek-V3.2-Exp— where can I download it?
Its weights are published on Hugging Face, under the organisation deepseek-ai. We do not host model files — this site calculates what hardware is needed to run them.
DeepSeek-V3.2-Exp— 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.
DeepSeek-V3.2-Exp— can I run it if it does not fit in my GPU?
Partly. Layers that do not fit sit in system memory and run at a fraction of the speed, so a mostly-offloaded model is rarely worth using. The nearest miss we calculate falls short by 103.0 GB. Every figure here assumes the whole model is resident on the card.
DeepSeek-V3.2-Exp— would two GPUs run it faster?
A second card roughly doubles the memory available but not the generation rate. The number already able to run it alone: 0. So a second card is rarely the answer here.
DeepSeek-V3.2-Exp— why does the quantisation differ between cards?
Because capacity varies, so does how hard it has to be squeezed. The number of distinct levels in the table above: 1. Bigger cards get the more accurate version, and the quality floor above pins the comparison to one level.
DeepSeek-V3.2-Exp— how accurate are these speed estimates?
These are estimates with real error bars, and any of them could reasonably land anywhere in its published range depending on which runtime you use. The fastest result here: the range beneath each figure. The same model and card vary by thirty to fifty per cent depending on the inference software and its version.
DeepSeek-V3.2-Exp— 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.
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.