MiniMax-M2.1 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 MI250
128 GB · Q3_K_M · 12.8 tok/s
Fastest card
B200
34.2 tok/s · 180 GB
Which GPUs can run MiniMax-M2.1?
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.
17 cards match
Calculating| Needs | Quantisation | Fit | |||||
|---|---|---|---|---|---|---|---|
|
34.2
tok/s
20–55 · low confidence |
B200 NVIDIA | 180 GB | 8,000 GB/s | Jan 2024 | 139.2 GB | Q4_K_M | Tight |
|
25.5
tok/s
15–41 · low confidence |
Radeon Instinct MI300 AMD | 128 GB | 6,550 GB/s | Jan 2023 | 112.6 GB | Q3_K_M | Tight |
|
22.2
tok/s
13–36 · low confidence |
H200 NVL NVIDIA | 141 GB | 4,890 GB/s | Nov 2024 | 125.9 GB | IQ4_XS | Tight |
|
22.2
tok/s
13–36 · low confidence |
H200 SXM 141 GB NVIDIA | 141 GB | 4,890 GB/s | Nov 2024 | 125.9 GB | IQ4_XS | Tight |
|
20.7
tok/s
12–33 · low confidence |
Radeon Instinct MI300A AMD | 128 GB | 5,325 GB/s | Dec 2023 | 112.6 GB | Q3_K_M | Tight |
|
14.8
tok/s
9–24 · low confidence |
B300 NVIDIA | 288 GB | 8,000 GB/s | Sep 2025 | 245.9 GB | Q8_0 | Tight |
|
13.7
tok/s
8–22 · low confidence |
Radeon Instinct MI300X AMD | 192 GB | 5,325 GB/s | Dec 2023 | 165.9 GB | Q5_K_M | Tight |
|
13.7
tok/s
8–22 · low confidence |
Radeon Instinct MI308X AMD | 192 GB | 5,325 GB/s | Dec 2023 | 165.9 GB | Q5_K_M | Tight |
|
12.8
tok/s
8–20 · low confidence |
Radeon Instinct MI250 AMD | 128 GB | 3,280 GB/s | Nov 2021 | 112.6 GB | Q3_K_M | Tight |
|
12.8
tok/s
8–20 · low confidence |
Radeon Instinct MI250X AMD | 128 GB | 3,280 GB/s | Nov 2021 | 112.6 GB | Q3_K_M | Tight |
|
12.6
tok/s
8–20 · low confidence |
Radeon Instinct MI325X AMD | 256 GB | 6,000 GB/s | Oct 2024 | 192.6 GB | Q6_K | Tight |
|
11.8
tok/s
7–19 · low confidence |
Radeon Instinct MI350X AMD | 288 GB | 8,190 GB/s | Jan 2025 | 245.9 GB | Q8_0 | Tight |
|
11.8
tok/s
7–19 · low confidence |
Radeon Instinct MI355X AMD | 288 GB | 8,190 GB/s | Jan 2025 | 245.9 GB | Q8_0 | Tight |
|
10.6
tok/s
6–17 · low confidence |
Data Center GPU Max 1550 Intel | 128 GB | 3,280 GB/s | Jan 2023 | 112.6 GB | Q3_K_M | Tight |
|
10.4
tok/s
6–17 · low confidence |
Data Center GPU Max Subsystem Intel | 128 GB | 3,210 GB/s | Jan 2023 | 112.6 GB | Q3_K_M | Tight |
|
1.4
tok/s
1–2 · low confidence |
GB10 NVIDIA | 128 GB | 273 GB/s | Oct 2025 | 112.6 GB | Q3_K_M | Tight |
|
1.4
tok/s
1–2 · low confidence |
Jetson T5000 NVIDIA | 128 GB | 273 GB/s | Aug 2025 | 112.6 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
- 23 December 2025
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language
- Task
- Chat, Language modeling/generation, Code generation, Tool use, Agentic workflows
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
- 229B
- Training data
- tokens
HF lists 229B params 10B active parameters
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
Weights available on Hugging Face, license shown as modified-mit. API also available via MiniMax Open Platform.
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Why it is tracked
- Discretionary
- Record confidence
- Confident
Plausibly on the pareto frontier of cost and performance for open models. Flagship from MiniMax
Sources
Where this record came from and when it was last checked.
- Reference
- Meet MiniMax-M2.1
- Last updated
- 11 February 2026
The extremes
The ten fastest GPUs for MiniMax-M2.1
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.
- 01 B200 180 GB · 8,000 GB/s · Q4_K_M 34.2 tok/s
- 02 Radeon Instinct MI300 128 GB · 6,550 GB/s · Q3_K_M 25.5 tok/s
- 03 H200 NVL 141 GB · 4,890 GB/s · IQ4_XS 22.2 tok/s
- 04 H200 SXM 141 GB 141 GB · 4,890 GB/s · IQ4_XS 22.2 tok/s
- 05 Radeon Instinct MI300A 128 GB · 5,325 GB/s · Q3_K_M 20.7 tok/s
- 06 B300 288 GB · 8,000 GB/s · Q8_0 14.8 tok/s
- 07 Radeon Instinct MI300X 192 GB · 5,325 GB/s · Q5_K_M 13.7 tok/s
- 08 Radeon Instinct MI308X 192 GB · 5,325 GB/s · Q5_K_M 13.7 tok/s
- 09 Radeon Instinct MI250 128 GB · 3,280 GB/s · Q3_K_M 12.8 tok/s
- 10 Radeon Instinct MI250X 128 GB · 3,280 GB/s · Q3_K_M 12.8 tok/s
The smallest GPUs that still run MiniMax-M2.1
The cheapest route in, by memory capacity. A tight fit runs the model but leaves nothing spare for a longer conversation.
- 01 GB10 128 GB · needs 112.6 GB · Q3_K_M · tight 1.4 tok/s
- 02 Jetson T5000 128 GB · needs 112.6 GB · Q3_K_M · tight 1.4 tok/s
- 03 Radeon Instinct MI300A 128 GB · needs 112.6 GB · Q3_K_M · tight 20.7 tok/s
- 04 Data Center GPU Max 1550 128 GB · needs 112.6 GB · Q3_K_M · tight 10.6 tok/s
- 05 Data Center GPU Max Subsystem 128 GB · needs 112.6 GB · Q3_K_M · tight 10.4 tok/s
- 06 Radeon Instinct MI300 128 GB · needs 112.6 GB · Q3_K_M · tight 25.5 tok/s
- 07 Radeon Instinct MI250 128 GB · needs 112.6 GB · Q3_K_M · tight 12.8 tok/s
- 08 Radeon Instinct MI250X 128 GB · needs 112.6 GB · Q3_K_M · tight 12.8 tok/s
- 09 H200 NVL 141 GB · needs 125.9 GB · IQ4_XS · tight 22.2 tok/s
- 10 H200 SXM 141 GB 141 GB · needs 125.9 GB · IQ4_XS · tight 22.2 tok/s
What the numbers mean
What you need to run it
Minimum card
Radeon Instinct MI250
Memory needed
112.6 GB
Fastest
34.2 tok/s
At 229B parameters, MiniMax-M2.1 is beyond what any single graphics card holds. Running it means either splitting it across several cards or renting hardware built for the job — 17 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 MI250. Its 128 GB is enough at Q3_K_M compression, giving roughly 12.8 tokens per second.
A B200 is the fastest we calculate for it: about 34.2 tokens per second, from 8,000 GB/s of memory bandwidth.
Background
MiniMax-M2.1 was published by MiniMax, in China, in December 2025. industry is the category the publisher falls under.
It works in Language, and is recorded as doing chat, Language modeling/generation, Code generation, Tool use, Agentic workflows.
The weights are published, so it can be downloaded and run on your own hardware indefinitely, offline, with no account attached.
Reading the throughput figures
The median result is around 12.8 tokens per second; 15 cards produce text faster than most people read it.
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
The reason it appears in this catalogue at all is discretionary.
Step by step
How to choose a GPU for MiniMax-M2.1
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
Look at what MiniMax-M2.1 actually needs — around 112.6 GB at Q3_K_M. No amount of processing power compensates for a card that cannot hold it.
-
02
Decide how long your conversations run
The conversation occupies memory too, and grows as it goes. Set the slider to the length you expect: at long context MiniMax-M2.1 can slip off a card that handles short questions easily.
-
03
Decide how much compression you will accept
Each card runs the least-compressed copy it can hold — Q3_K_M on the smallest card that fits. Setting a floor drops the cards that only manage MiniMax-M2.1 by squeezing it further than you would want.
-
04
Rank by throughput rather than spec sheet
Ranking by tokens per second for MiniMax-M2.1 follows memory bandwidth, not core counts, which is why the B200 tops it at 34.2 tok/s.
-
05
Read the fit column last
A tight fit runs MiniMax-M2.1 but leaves nothing spare for a longer conversation; comfortable has headroom. If you expect to grow the context, buy for comfortable.
-
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 MiniMax-M2.1 is settled.
Answers
MiniMax-M2.1 — common questions
Why does the quantisation differ between cards for MiniMax-M2.1?
Because capacity varies, so does how hard MiniMax-M2.1 has to be squeezed — 6 distinct levels appear in the table above. Set a minimum quality to compare at one.
How accurate are these MiniMax-M2.1 speed estimates?
They are calculated from specifications rather than measured, and each carries a range — 20–55 tok/s on the B200, 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-M2.1?
The smallest card in our catalogue that holds MiniMax-M2.1 is the Radeon Instinct MI250, with 128 GB of memory. It runs the model at Q3_K_M using about 112.6 GB, and produces roughly 12.8 tokens per second. 17 cards in total can run it.
How fast is MiniMax-M2.1 on a GPU?
It depends on the card. The quickest we calculate is a B200 at about 34.2 tokens per second; the slowest that still runs it manages considerably less. Reading speed is around ten tokens per second, and 15 of the cards that can run MiniMax-M2.1 clear that.
How much VRAM does MiniMax-M2.1 need?
About 112.6 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-M2.1 open source?
Its weights are published, so MiniMax-M2.1 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-M2.1 have?
MiniMax-M2.1 has 229B parameters. HF lists 229B params 10B active 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.
Who created MiniMax-M2.1?
MiniMax-M2.1 was published by MiniMax, based in China, categorised as industry.
When was MiniMax-M2.1 released?
MiniMax-M2.1 was published in December 2025.
What is MiniMax-M2.1 used for?
MiniMax-M2.1 works in Language, and is recorded as handling chat, Language modeling/generation, Code generation, Tool use, Agentic workflows. These are the areas it was designed around; they describe intent rather than a hard boundary.
Where can I download MiniMax-M2.1?
The weights for MiniMax-M2.1 are published, though we do not hold a repository link for it. This site calculates hardware requirements rather than hosting model files.
Can I run MiniMax-M2.1 if it does not fit in my GPU?
It can be split between the card and system memory, but MiniMax-M2.1 generates painfully slowly that way — the nearest miss we calculate is short by 52.8 GB. Nothing on this page assumes offloading.
Would two GPUs run MiniMax-M2.1 faster?
A second card roughly doubles the memory available but not the generation rate. With 17 cards already able to run MiniMax-M2.1 alone, the case for pairing is weak.
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.