MiniMax-M2 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 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?
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
- 27 October 2025
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language
- Task
- Code generation, System control, Search, 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
- 229B
- Training data
- tokens
"maintaining activations around 10B" safetensors: 229B params
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
MIT license https://huggingface.co/MiniMaxAI/MiniMax-M2
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
high performing open source model
Sources
Where this record came from and when it was last checked.
- Reference
- MiniMax M2 & Agent: Ingenious in Simplicity
- Last updated
- 11 February 2026
The extremes
The ten fastest GPUs that run MiniMax-M2
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
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
Hardware requirements in practice
Minimum card
Radeon Instinct MI250
Memory needed
112.6 GB
Fastest
34.2 tok/s
At 229B parameters, MiniMax-M2 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 entry point is the Radeon Instinct MI250: 128 GB of memory, Q3_K_M compression, roughly 12.8 tokens per second.
At the other end, a B200 generates roughly 34.2 tokens per second on it, on the strength of 8,000 GB/s of memory bandwidth.
Where it came from
MiniMax-M2 was published by MiniMax, in China, in October 2025. industry is the category the publisher falls under.
It works in Language, and is recorded as doing code generation, System control, Search, Language modeling/generation, Question answering.
The weights are published, so it can be downloaded and run on your own hardware indefinitely, offline, with no account attached. It is published under the MiniMaxAI organisation on Hugging Face.
Understanding the speeds
Across every card that can run it, the middle of the range is about 12.8 tokens per second, and 15 of them clear the ten tokens per second that roughly matches reading speed.
Every weight participates in every token here, so bandwidth is the whole story: the ranking below is effectively a ranking of memory throughput.
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.
What went into building it
The reason it appears in this catalogue at all is discretionary.
Step by step
How to choose a GPU for MiniMax-M2
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
The table lists every card that can hold MiniMax-M2 — around 112.6 GB at Q3_K_M. That figure, not the card's headline performance, is what decides whether it runs.
-
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-M2.
-
03
Set a quality floor
The quantisation column varies by card, because a bigger card holds a more accurate copy of MiniMax-M2 — Q3_K_M on the smallest card that fits. Set a floor to hold the comparison at one level.
-
04
Sort by speed
Ranking by tokens per second for MiniMax-M2 follows memory bandwidth, not core counts, which is why the B200 tops it at 34.2 tok/s.
-
05
Look at the headroom, not just the fit
Tight means MiniMax-M2 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
Each card page repeats this sweep for every model we hold. It answers what else the hardware is good for, beyond MiniMax-M2.
Answers
MiniMax-M2 — common questions
What is MiniMax-M2 used for?
MiniMax-M2 works in Language, and is recorded as handling code generation, System control, Search, Language modeling/generation, Question answering. 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.
Where can I download MiniMax-M2?
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.
Can I run MiniMax-M2 if it does not fit in my GPU?
It can be split between the card and system memory, but MiniMax-M2 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 faster?
A second card roughly doubles the memory available but not the generation rate. With 17 cards already able to run MiniMax-M2 alone, the case for pairing is weak.
Why does the quantisation differ between cards for MiniMax-M2?
Each card is shown running the least-compressed copy it can hold, and MiniMax-M2 appears at 6 different compression levels across the cards that fit it. Bigger cards get the more accurate version.
How accurate are these MiniMax-M2 speed estimates?
Every figure is derived from memory bandwidth and model size, not benchmarked. That is why each is published as a range such as 20–55 tok/s on the B200 rather than a single number.
What GPU do I need to run MiniMax-M2?
The smallest card in our catalogue that holds MiniMax-M2 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 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 clear that.
How much VRAM does MiniMax-M2 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 open source?
Its weights are published, so MiniMax-M2 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 have?
MiniMax-M2 has 229B parameters. "maintaining activations around 10B" safetensors: 229B params. 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?
MiniMax-M2 was published by MiniMax, based in China, categorised as industry.
When was MiniMax-M2 released?
MiniMax-M2 was published in October 2025.
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.