Hunyuan-Large 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
Smallest card that fits
Radeon Instinct MI325X
256 GB · IQ4_XS · 12.5 tok/s
Fastest card
B300
20.1 tok/s · 288 GB
Which GPUs can run Hunyuan-Large?
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.
4 cards match
Calculating| Needs | Quantisation | Fit | |||||
|---|---|---|---|---|---|---|---|
|
20.1
tok/s
12–32 · low confidence |
B300 NVIDIA | 288 GB | 8,000 GB/s | Sep 2025 | 236.0 GB | Q4_K_M | Tight |
|
16.1
tok/s
10–26 · low confidence |
Radeon Instinct MI350X AMD | 288 GB | 8,190 GB/s | Jan 2025 | 236.0 GB | Q4_K_M | Tight |
|
16.1
tok/s
10–26 · low confidence |
Radeon Instinct MI355X AMD | 288 GB | 8,190 GB/s | Jan 2025 | 236.0 GB | Q4_K_M | Tight |
|
12.5
tok/s
8–20 · low confidence |
Radeon Instinct MI325X AMD | 256 GB | 6,000 GB/s | Oct 2024 | 213.4 GB | IQ4_XS | Tight |
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
- Tencent
- Organisation type
- Industry
- Country
- China
- Published
- 6 November 2024
- Authors
- Xingwu Sun, Yanfeng Chen, Yiqing Huang, Ruobing Xie, Jiaqi Zhu, Kai Zhang, Shuaipeng Li, Zhen Yang, Jonny Han, Xiaobo Shu, Jiahao Bu, Zhongzhi Chen, Xuemeng Huang, Fengzong Lian, Saiyong Yang, Jianfeng Yan, Yuyuan Zeng, Xiaoqin Ren, Chao Yu, Lulu Wu, Yue Mao, Jun Xia, Tao Yang, Suncong Zheng, Kan Wu, Dian Jiao, Jinbao Xue, Xipeng Zhang, Decheng Wu, Kai Liu, Dengpeng Wu, Guanghui Xu, Shaohua Chen, …
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language
- Task
- Language modeling/generation, Question answering, Code generation, Translation
- Numerical format
- BF16
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
- 389B
- Training data
- 7,000,000,000,000 tokens
"a total of 389 billion parameters and 52 billion activation parameters"
"# Trained Tokens 7T" Table 1
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
- 3.5 × 10²⁴ FLOP
- How it was established
- Operation counting
52B activated parameters 6ND = 6*52*10^9*7*10^12 = 2.184 × 10^24 They also suggest more precise formula to calculate MoE compute budget: 9.59ND + 2.3 × 10^8D = 9.59*52*10^9*7*10^12 + 2.3 × 10^8 × 7*10^12 = 3.49237×10^24 which seems closer to projected compute on Figure 3
The training run
What it physically took to train: which chips, how many, for how long, and what that drew from the wall.
- Data centre
- The paper does not mention any hardware, GPUs or any information regarding the hardware used.
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 (restricted use)
- Training code
- Open (restricted use)
the license doesn't regulate usage in the EU also requires additional licensing in case of massive commercial use
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
- Training cost
- Record confidence
- Confident
Sources
Where this record came from and when it was last checked.
- Reference
- Hunyuan-Large: An Open-Source MoE Model with 52 Billion Activated Parameters by Tencent
- Last updated
- 18 December 2025
The extremes
The ten fastest GPUs that run Hunyuan-Large
Ranked by estimated tokens per second, newest card first where speeds tie. Because generation is bound by memory bandwidth, this ordering follows bandwidth rather than any gaming benchmark.
The smallest GPUs that still run Hunyuan-Large
The cheapest route in, by memory capacity. A tight fit runs the model but leaves nothing spare for a longer conversation.
What the numbers mean
What it takes to run this model
Minimum card
Radeon Instinct MI325X
Memory needed
213.4 GB
Fastest
20.1 tok/s
Hunyuan-Large reaches a parameter count of 389B. 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: 4.
The smallest card that holds it is Radeon Instinct MI325X, with a memory capacity of 256 GB, running it at a compression of IQ4_XS and producing around 12.5 tokens per second.
The fastest we calculate for it is B300, generating roughly 20.1 tokens per second on the strength of a memory bandwidth of 8,000 GB/s.
What this model is
Hunyuan-Large was published by Tencent, in the country recorded as China, during November 2024. 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, Question answering, Code generation, Translation.
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.
What decides the speed
Half the cards that hold it manage more than 16.1 tokens per second. Producing text faster than most people read it: 4 of them.
Being dense, it reads all of itself per token, which is why the ordering by speed below follows the ordering by memory bandwidth so closely.
Without the attention layout on record, the memory column is an approximation. It is close enough to choose hardware by, and least reliable at long context.
Training and provenance
Training it took a computation budget of roughly 3.5 × 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 7,000,000,000,000 tokens of text.
Its inclusion criterion: training cost.
Step by step
How to choose a GPU for Hunyuan-Large
The table above has already assessed every card we hold specifications for against this model. Getting to your answer takes six steps.
-
01
Start from the memory column
Start from what it actually needs, which is the requirement of Hunyuan-Large, needing around 213.4 GB at a compression of IQ4_XS. That figure, not the headline performance of a card, is what decides whether it runs.
-
02
Decide how long your conversations run
Longer conversations cost memory on top of what the weights need. Move the slider to your real working length before trusting any row for Hunyuan-Large.
-
03
Decide how much compression you will accept
Each card runs the least-compressed copy it can hold, reaching a compression of IQ4_XS on the smallest card that fits. 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
Compare tokens per second, not specifications
Ranking by tokens per second follows memory bandwidth rather than core counts, for Hunyuan-Large. It will not match a gaming ordering, because generation is bound by memory bandwidth. The card topping the list is B300, at 20.1 tok/s.
-
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 Hunyuan-Large. 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.
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06
Check the card from the other side
Following a card through to its own page shows every other model it can hold, which is the question that follows once you have settled on Hunyuan-Large.
Answers
Hunyuan-Large — common questions
Hunyuan-Large— who created it?
It was published by Tencent, based in China, an organisation categorised as industry.
Hunyuan-Large— when was it released?
It was published in November 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.
Hunyuan-Large— what is it used for?
It works in the domain of Language, and is recorded as handling the task of language modeling/generation, Question answering, Code generation, Translation. These are the areas it was designed around; they describe intent rather than a hard boundary.
Hunyuan-Large— 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.
Hunyuan-Large— how much compute was used to train it?
Training consumed around 3.5 × 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.
Hunyuan-Large— can I run it if it does not fit in my GPU?
It can be split between the card and system memory, but it generates painfully slowly that way. The nearest miss we calculate falls short by 63.2 GB. Every figure here assumes the whole model is resident on the card.
Hunyuan-Large— 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: 4. So a second card is rarely the answer here.
Hunyuan-Large— why does the quantisation differ between cards?
A larger card holds a more accurate copy. The number of compression levels used across the cards that run it: 2. Bigger cards get the more accurate version, and the quality floor above pins the comparison to one level.
Hunyuan-Large— how accurate are these speed estimates?
They are calculated from specifications rather than measured, and each carries a range. One example: 12–32 tok/s on B300. The same model and card vary by thirty to fifty per cent depending on the inference software and its version.
Hunyuan-Large— what GPU do I need to run it?
The smallest card in our catalogue that holds it is Radeon Instinct MI325X, with a memory capacity of 256 GB. It runs the model at a compression of IQ4_XS using about 213.4 GB, and produces roughly 12.5 tokens per second. The number of cards able to run it in total: 4.
Hunyuan-Large— how fast is it on a GPU?
It depends on the card. The quickest we calculate is B300, at about 20.1 tokens per second; the slowest that still runs it manages considerably less. Reading speed is around ten tokens per second, and the number of cards clearing that: 4.
Hunyuan-Large— how much VRAM does it need?
It needs about 213.4 GB at a compression of IQ4_XS, which is what the smallest card that runs it uses. Less compression needs more: the figures in the memory column above are recalculated for each card, because each one holds the least-compressed version it can.
Hunyuan-Large— 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.
Hunyuan-Large— how many parameters does it have?
It has a parameter count of 389B. "a total of 389 billion parameters and 52 billion activation parameters". 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.
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.