MiniMax-Text-01 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
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-Text-01?
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
- 14 January 2025
- Authors
- MiniMax, Aonian Li, Bangwei Gong, Bo Yang, Boji Shan, Chang Liu, Cheng Zhu, Chunhao Zhang, Congchao Guo, Da Chen, Dong Li, Enwei Jiao, Gengxin Li, Guojun Zhang, Haohai Sun, Houze Dong, Jiadai Zhu, Jiaqi Zhuang, Jiayuan Song, Jin Zhu, Jingtao Han, Jingyang Li, Junbin Xie, Junhao Xu, Junjie Yan, Kaishun Zhang, Kecheng Xiao, Kexi Kang, Le Han, Leyang Wang, Lianfei Yu, Liheng Feng, Lin Zheng, Linbo Ch…
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
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
- 7,200,000,000,000 tokens
- Batch size
- 128,000,000
Total Parameters: 456B Activated Parameters per Token: 45.9B Number Layers: 80 Hybrid Attention: a softmax attention is positioned after every 7 lightning attention. Number of attention heads: 64 Attention head dimension: 128 Mixture of Experts: Number of experts: 32 Expert hidden dimension: 9216 Top-2 routing strategy Positional Encoding: Rotary Position Embedding (RoPE) applied to half of the attention head dimension with a base frequency of 10,000,000 Hidden Size: 6144 Vocab Size: 200,064
7.2T tokens
1.28e8
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
- 3.1 × 10²⁴ FLOP
- How it was established
- Operation counting
6 FLOP / token / parameter * 45.9 * 10^9 activated parameters * 1.1408e+13 tokens = 3.1417632e+24 FLOP "Likely" confidence because the model is MoE (formula might not be that accurate) data trained on is 1.1408e+13 = ((16 million * 500)+7.2 trillion + 3.2 trillion + 1 trillion)
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 (restricted use)
- Training code
- Unreleased
- Hugging Face
- MiniMaxAI
https://huggingface.co/MiniMaxAI/MiniMax-Text-01 "MiniMax may terminate this Agreement if you are in breach of any term or condition of this Agreement." code seems to be just inference code: https://github.com/MiniMax-AI/MiniMax-01
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
- Record confidence
- Likely
Sources
Where this record came from and when it was last checked.
- Reference
- MiniMax-01: Scaling Foundation Models with Lightning Attention
- Last updated
- 10 March 2026
The extremes
The ten fastest GPUs for MiniMax-Text-01
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-Text-01
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 you need to run it
Minimum card
Radeon Instinct MI325X
Memory needed
223.5 GB
Fastest
18.3 tok/s
At 456B parameters, MiniMax-Text-01 is beyond what any single graphics card holds. Running it means either splitting it across several cards or renting hardware built for the job — 4 of the cards we track can hold it on their own, and all of them are datacentre parts.
The least hardware that works is a Radeon Instinct MI325X. Its 256 GB is enough at Q3_K_M compression, giving roughly 11.7 tokens per second.
The quickest result comes from a B300 at around 18.3 tokens per second — its 8,000 GB/s of bandwidth is what buys that.
What this model is
MiniMax-Text-01 was published by MiniMax, in China, in January 2025. The organisation is categorised as industry.
It works in Language, and is recorded as doing language modeling/generation, Question answering.
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. It is published under the MiniMaxAI organisation on Hugging Face.
What decides the speed
Across every card that can run it, the middle of the range is about 14.6 tokens per second, and 4 of them clear the ten tokens per second that roughly matches reading speed.
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.
Its internal architecture is not on file, so memory is approximated from the parameter count and marked accordingly. Expect the real figure to differ, more so at long context.
How it was trained
Producing it required around 3.1 × 10²⁴ FLOP of arithmetic, on NVIDIA H800 SXM5, which is a statement about the training budget rather than about inference.
Around 7,200,000,000,000 tokens went into training it.
Step by step
How to choose a GPU for MiniMax-Text-01
The table above has already assessed every card we hold specifications for against this model. Getting to your answer takes six steps.
-
01
Check what it needs before anything else
Every card here has been checked against MiniMax-Text-01 — around 223.5 GB at Q3_K_M. Capacity is the gate — a card either holds it or it does not.
-
02
Set the context length you will work at
Longer conversations cost memory on top of what the weights need. Move the slider to your real working length before trusting any row for MiniMax-Text-01.
-
03
Set a quality floor
The quantisation column varies by card, because a bigger card holds a more accurate copy of MiniMax-Text-01 — Q3_K_M on the smallest card that fits. Set a floor to hold the comparison at one level.
-
04
Sort by speed
The speed ordering for MiniMax-Text-01 is effectively an ordering by memory bandwidth, which is why the B300 tops it at 18.3 tok/s.
-
05
Check the fit verdict before buying
Tight means MiniMax-Text-01 loads and works, with no room to raise the context later. Comfortable means you can. The difference matters more than a few tokens per second.
-
06
Open the card you have settled on
Every card name links to its own page, which runs the same calculation across the whole model catalogue. Worth a look before buying for MiniMax-Text-01 alone — a card is usually bought for more than one model.
Answers
MiniMax-Text-01 — common questions
Why does the quantisation differ between cards for MiniMax-Text-01?
Each card is shown running the least-compressed copy it can hold, and MiniMax-Text-01 appears at 2 different compression levels across the cards that fit it. Bigger cards get the more accurate version.
How accurate are these MiniMax-Text-01 speed estimates?
They are calculated from specifications rather than measured, and each carries a range — 11–29 tok/s on the B300, for instance. The same model and card vary by thirty to fifty per cent depending on the inference software and its version.
What GPU do I need to run MiniMax-Text-01?
The smallest card in our catalogue that holds MiniMax-Text-01 is the Radeon Instinct MI325X, with 256 GB of memory. It runs the model at Q3_K_M using about 223.5 GB, and produces roughly 11.7 tokens per second. 4 cards in total can run it.
How fast is MiniMax-Text-01 on a GPU?
It depends on the card. The quickest we calculate is a 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 4 of the cards that can run MiniMax-Text-01 clear that.
How much VRAM does MiniMax-Text-01 need?
About 223.5 GB at Q3_K_M compression, 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.
Is MiniMax-Text-01 open source?
Its weights are published, so MiniMax-Text-01 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.
How many parameters does MiniMax-Text-01 have?
MiniMax-Text-01 has 456B parameters. Total Parameters: 456B Activated Parameters per Token: 45.9B Number Layers: 80 Hybrid Attention: a softmax attention is positioned after every 7 lightning attention. Number of attention heads: 64 Attention head dimension: 128 Mixture of Experts: Number of experts: 32 Expert hidden dimension: 9216 Top-2 routing strategy Positional Encoding: Rotary Position Embedding (RoPE) applied to half of the attention head dimension with a base frequency of 10,000,000 Hidden Size: 6144 Vocab Size: 200,064. 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 MiniMax-Text-01?
MiniMax-Text-01 was published by MiniMax, based in China, categorised as industry.
When was MiniMax-Text-01 released?
MiniMax-Text-01 was published in January 2025.
What is MiniMax-Text-01 used for?
MiniMax-Text-01 works in Language, and is recorded as handling language modeling/generation, Question answering. These are the areas it was designed around; they describe intent rather than a hard boundary.
Where can I download MiniMax-Text-01?
Its weights are published under the MiniMaxAI organisation on Hugging Face. We do not host model files — this site calculates what hardware is needed to run them.
How much compute was used to train MiniMax-Text-01?
Around 3.1 × 10²⁴ FLOP, on 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.
Can I run MiniMax-Text-01 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 MiniMax-Text-01 is rarely worth using — the nearest miss we calculate is short by 103.8 GB. Every figure here assumes the whole model is on the card.
Would two GPUs run MiniMax-Text-01 faster?
Capacity adds across cards; throughput does not. Since 4 of the cards we track already hold MiniMax-Text-01 on their own, a second card is rarely the answer here.
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.