MiniMax-M1-80k 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 · Q3_K_M · 11.7 tok/s
Fastest card
B300
18.3 tok/s · 288 GB
Which GPUs can run MiniMax-M1-80k?
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 | |||||
|---|---|---|---|---|---|---|---|
|
18.3
tok/s
11–29 · low confidence |
B300 NVIDIA | 288 GB | 8,000 GB/s | Sep 2025 | 250.0 GB | IQ4_XS | Tight |
|
14.6
tok/s
9–23 · low confidence |
Radeon Instinct MI350X AMD | 288 GB | 8,190 GB/s | Jan 2025 | 250.0 GB | IQ4_XS | Tight |
|
14.6
tok/s
9–23 · low confidence |
Radeon Instinct MI355X AMD | 288 GB | 8,190 GB/s | Jan 2025 | 250.0 GB | IQ4_XS | Tight |
|
11.7
tok/s
7–19 · low confidence |
Radeon Instinct MI325X AMD | 256 GB | 6,000 GB/s | Oct 2024 | 223.5 GB | Q3_K_M | 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
- MiniMax
- Organisation type
- Industry
- Country
- China
- Published
- 13 June 2025
- Authors
- MiniMax: Aili Chen, Aonian Li, Bangwei Gong, Binyang Jiang, Bo Fei, Bo Yang, Boji Shan, Changqing Yu, Chao Wang, Cheng Zhu, Chengjun Xiao, Chengyu Du, Chi Zhang, Chu Qiao, Chunhao Zhang, Chunhui Du, Congchao Guo, Da Chen, Deming Ding, Dianjun Sun, Dong Li, Enwei Jiao, Haigang Zhou, Haimo Zhang, Han Ding, Haohai Sun, Haoyu Feng, Huaiguang Cai, Haichao Zhu, Jian Sun, Jiaqi Zhuang, Jiaren Cai, Jiayua…
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, Quantitative reasoning
- Base model
- MiniMax-Text-01
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
- 456B
- Training data
- 7,500,000,000,000 tokens
from the base model: Total Parameters: 456B Activated Parameters per Token: 45.9B
7.5T tokens "To enhance the reasoning and long context capabilities of the foundation model while ensuring diversity, we continue training the MiniMax-Text-01 model with additional 7.5T tokens with optimized data quality and mixture."
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
- 4.3 × 10²⁴ FLOP
- Fine-tuning compute
- 2.3 × 10²⁴ FLOP
1.9828799999999997e+24 FLOP [base model compute] + 2.3411262e+24 FLOP [see finetune compute notes] = 4.3240062e+24 FLOP
1. Continual pre-training for 7.5T tokens: 6 FLOP / token / active parameter * 45.9 * 10^9 activated parameters * 7.5 * 10^12 tokens = 2.0655e+24 FLOP 2. RL (3 weeks on 512 H800 GPUs): 989000000000000 FLOP / GPU / sec [bf16 assumed] * 512 GPUs * 3 weeks * 168 hours / week * 3600 sec / hour * 0.3 [assimed utilization] = 2.7562623e+23 FLOP 2.0655e+24 FLOP + 2.7562623e+23 FLOP = 2.3411262e+24 FLOP *there was also SFT stage with no details provided, I assumed its compute is negligible
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 H800 SXM5
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
- MiniMaxAI
Apache 2.0 https://huggingface.co/MiniMaxAI/MiniMax-M1-80k
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.
- Reference
- MiniMax-M1: Scaling Test-Time Compute Efficiently with Lightning Attention
- Last updated
- 28 November 2025
The extremes
The ten fastest GPUs that run MiniMax-M1-80k
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 MiniMax-M1-80k
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
Hardware requirements in practice
Minimum card
Radeon Instinct MI325X
Memory needed
223.5 GB
Fastest
18.3 tok/s
MiniMax-M1-80k reaches a parameter count of 456B. 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 entry point is Radeon Instinct MI325X, with a memory capacity of 256 GB, running it at a compression of Q3_K_M and producing around 11.7 tokens per second.
Top of the range is B300, generating roughly 18.3 tokens per second on the strength of a memory bandwidth of 8,000 GB/s.
What this model is
MiniMax-M1-80k was published by MiniMax, in the country recorded as China, during June 2025. It comes out of an organisation 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, Quantitative reasoning.
Its starting point was an existing base model, MiniMax-Text-01. Most models at this scale are adapted from an existing base rather than built from nothing.
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. On Hugging Face it is published under the organisation MiniMaxAI.
What decides the speed
Across every card that can run it, the middle of the range sits at 14.6 tokens per second. Exceeding reading speed outright: 4 of them.
It is a dense model, so every parameter is read for every token produced. That makes speed track memory bandwidth almost exactly — a card with twice the bandwidth generates roughly twice as fast.
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.
How it was trained
Producing it required arithmetic totalling around 4.3 × 10²⁴ FLOP, on hardware recorded as NVIDIA H800 SXM5. That figure measures what producing the model cost, and has no bearing on how fast it answers.
The training set ran to roughly 7,500,000,000,000 tokens of text.
Step by step
How to choose a GPU for MiniMax-M1-80k
The table above has already assessed every card we hold specifications for against this model. Getting to your answer takes six steps.
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01
Check what it needs before anything else
Start from what it actually needs, which is the requirement of MiniMax-M1-80k, needing around 223.5 GB at a compression of Q3_K_M. No amount of processing power compensates for a card that cannot hold it.
-
02
Match the context to your actual use
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 MiniMax-M1-80k.
-
03
Decide how much compression you will accept
Compression is what makes a model fit smaller cards, at some cost in accuracy, reaching a compression of Q3_K_M 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.
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04
Rank by throughput rather than spec sheet
Sort by speed to see how cards rank for MiniMax-M1-80k. It will not match a gaming ordering, because generation is bound by memory bandwidth. The card topping the list is B300, at 18.3 tok/s.
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05
Look at the headroom, not just the fit
A tight fit runs, but leaves nothing spare for a longer conversation, in the case of MiniMax-M1-80k. 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
See what else that card runs
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 MiniMax-M1-80k.
Answers
MiniMax-M1-80k — common questions
MiniMax-M1-80k— who created it?
It was published by MiniMax, based in China, an organisation categorised as industry.
MiniMax-M1-80k— when was it released?
It was published in June 2025.
MiniMax-M1-80k— 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, Quantitative reasoning. These are the areas it was designed around; they describe intent rather than a hard boundary.
MiniMax-M1-80k— where can I download it?
Its weights are published on Hugging Face, under the organisation MiniMaxAI. We do not host model files — this site calculates what hardware is needed to run them.
MiniMax-M1-80k— how much compute was used to train it?
Training consumed around 4.3 × 10²⁴ FLOP, on hardware recorded as NVIDIA H800 SXM5. 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.
MiniMax-M1-80k— 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 103.8 GB. Every figure here assumes the whole model is resident on the card.
MiniMax-M1-80k— 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.
MiniMax-M1-80k— 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.
MiniMax-M1-80k— how accurate are these speed estimates?
They are calculated from specifications rather than measured, and each carries a range. One example: 11–29 tok/s on B300. The same model and card vary by thirty to fifty per cent depending on the inference software and its version.
MiniMax-M1-80k— 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 Q3_K_M using about 223.5 GB, and produces roughly 11.7 tokens per second. The number of cards able to run it in total: 4.
MiniMax-M1-80k— how fast is it on a GPU?
It depends on the card. The quickest we calculate is B300, at about 18.3 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.
MiniMax-M1-80k— how much VRAM does it need?
It needs about 223.5 GB at a compression of Q3_K_M, 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.
MiniMax-M1-80k— 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.
MiniMax-M1-80k— how many parameters does it have?
It has a parameter count of 456B. from the base model: Total Parameters: 456B Activated Parameters per Token: 45.9B. 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.