P1-235B-A22B TPS calculator

Open weights Shanghai AI Lab 235B parameters November 2025

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

17 cards that can run it

818 cards we hold specifications for

Smallest card that fits

Radeon Instinct MI250

128 GB · Q3_K_M · 69.1 tok/s

Fastest card

B200

143 tok/s · 180 GB

Which GPUs can run P1-235B-A22B?

Set the inputs, read the answer

Context length tokens

A longer conversation needs more memory, which can push this model off smaller cards.

Minimum quality

Hides cards that would only fit the model by compressing it below this point.

Search cards

Card memory

Also show cards that cannot run it

17 cards match

Calculating

					Needs	Quantisation	Fit
143 tok/s 86–229 · low confidence	B200 NVIDIA	180 GB	8,000 GB/s	Jan 2024	157.1 GB	Q5_K_M	Tight
138 tok/s 83–221 · low confidence	Radeon Instinct MI300 AMD	128 GB	6,550 GB/s	Jan 2023	102.4 GB	Q3_K_M	Tight
120 tok/s 72–192 · low confidence	H200 NVL NVIDIA	141 GB	4,890 GB/s	Nov 2024	116.0 GB	IQ4_XS	Tight
120 tok/s 72–192 · low confidence	H200 SXM 141 GB NVIDIA	141 GB	4,890 GB/s	Nov 2024	116.0 GB	IQ4_XS	Tight
112 tok/s 67–180 · low confidence	Radeon Instinct MI300A AMD	128 GB	5,325 GB/s	Dec 2023	102.4 GB	Q3_K_M	Tight
80.1 tok/s 48–128 · low confidence	B300 NVIDIA	288 GB	8,000 GB/s	Sep 2025	239.1 GB	Q8_0	Tight
74.3 tok/s 45–119 · low confidence	Radeon Instinct MI300X AMD	192 GB	5,325 GB/s	Dec 2023	157.1 GB	Q5_K_M	Tight
74.3 tok/s 45–119 · low confidence	Radeon Instinct MI308X AMD	192 GB	5,325 GB/s	Dec 2023	157.1 GB	Q5_K_M	Tight
69.1 tok/s 41–111 · low confidence	Radeon Instinct MI250 AMD	128 GB	3,280 GB/s	Nov 2021	102.4 GB	Q3_K_M	Tight
69.1 tok/s 41–111 · low confidence	Radeon Instinct MI250X AMD	128 GB	3,280 GB/s	Nov 2021	102.4 GB	Q3_K_M	Tight
68.1 tok/s 41–109 · low confidence	Radeon Instinct MI325X AMD	256 GB	6,000 GB/s	Oct 2024	184.4 GB	Q6_K	Comfortable
64.0 tok/s 38–102 · low confidence	Radeon Instinct MI350X AMD	288 GB	8,190 GB/s	Jan 2025	239.1 GB	Q8_0	Tight
64.0 tok/s 38–102 · low confidence	Radeon Instinct MI355X AMD	288 GB	8,190 GB/s	Jan 2025	239.1 GB	Q8_0	Tight
57.6 tok/s 35–92 · low confidence	Data Center GPU Max 1550 Intel	128 GB	3,280 GB/s	Jan 2023	102.4 GB	Q3_K_M	Tight
56.4 tok/s 34–90 · low confidence	Data Center GPU Max Subsystem Intel	128 GB	3,210 GB/s	Jan 2023	102.4 GB	Q3_K_M	Tight
7.4 tok/s 4–12 · low confidence	GB10 NVIDIA	128 GB	273 GB/s	Oct 2025	102.4 GB	Q3_K_M	Tight
7.4 tok/s 4–12 · low confidence	Jetson T5000 NVIDIA	128 GB	273 GB/s	Aug 2025	102.4 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: Shanghai AI Lab
Organisation type: Academia
Country: China
Published: 17 November 2025
Authors: Jiacheng Chen, Qianjia Cheng, Fangchen Yu, Haiyuan Wan, Yuchen Zhang, Shenghe Zheng, Junchi Yao, Qingyang Zhang, Haonan He, Yun Luo, Yufeng Zhao, Futing Wang, Li Sheng, Chengxing Xie, Yuxin Zuo, Yizhuo Li, Wenxauan Zeng, Yulun Wu, Rui Huang, Dongzhan Zhou, Kai Chen, Yu Qiao, Lei Bai, Yu Cheng, Ning Ding, Bowen Zhou, Peng Ye, Ganqu Cui

What it does

The problem areas the model was built for. A model can carry several of each.

Domain: Language
Task: Question answering
Base model: Qwen3-235B-A22B

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: 235B
Training data: tokens

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)

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: Likely

Sources

Where this record came from and when it was last checked.

Reference: P1: Mastering Physics Olympiads with Reinforcement Learning
Last updated: 8 April 2026

The extremes

The ten fastest GPUs that run P1-235B-A22B

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 P1-235B-A22B

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 MI250

Memory needed

102.4 GB

Fastest

143 tok/s

P1-235B-A22B reaches a parameter count of 235B. 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: 17.

The smallest card that holds it is Radeon Instinct MI250, with a memory capacity of 128 GB, running it at a compression of Q3_K_M and producing around 69.1 tokens per second.

At the other end sits B200, generating roughly 143 tokens per second on the strength of a memory bandwidth of 8,000 GB/s.

About this model

P1-235B-A22B was published by Shanghai AI Lab, in the country recorded as China, during November 2025. It comes out of an organisation categorised as academia.

It works in the domain of Language, and is recorded as performing the task of question answering.

Rather than being trained from scratch, it is derived from Qwen3-235B-A22B. That is why it shares the base model's general shape and size.

The weights being open is what puts this page in the calculator rather than only in the catalogue: it is a model you can actually hold.

How fast it runs, and why

Half the cards that hold it manage more than 69.1 tokens per second. Exceeding reading speed outright: 15 of them.

Mixture-of-experts routing is why the speeds here look high for the parameter count. Only a fraction is read per token, but the whole thing has to be loaded.

Memory here is estimated from size rather than computed from the architecture, which is not recorded for this model — the numbers are indicative rather than exact.

Training and provenance

The reason it appears in this catalogue at all: discretionary.

Step by step

How to choose a GPU for P1-235B-A22B

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 able to hold P1-235B-A22B, needing around 102.4 GB at a compression of Q3_K_M. Capacity is the gate — a card either holds it or it does not.
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 P1-235B-A22B.
03

Choose how far you will compress it

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.
04

Sort by speed

The speed ordering is effectively an ordering by memory bandwidth, for P1-235B-A22B. It will not match a gaming ordering, because generation is bound by memory bandwidth. The card topping the list is B200, at 143 tok/s.
05

Look at the headroom, not just the fit

Tight means it loads and works with no room to raise the context later, in the case of P1-235B-A22B. 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.
06

Check the card from the other side

Every card name links to its own page, which runs the same calculation across the whole model catalogue. A card is usually bought for more than one model, so it is worth a look before buying for P1-235B-A22B.

Answers

P1-235B-A22B — common questions

P1-235B-A22B— what GPU do I need to run it?

The smallest card in our catalogue that holds it is Radeon Instinct MI250, with a memory capacity of 128 GB. It runs the model at a compression of Q3_K_M using about 102.4 GB, and produces roughly 69.1 tokens per second. The number of cards able to run it in total: 17.

P1-235B-A22B— how fast is it on a GPU?

It depends on the card. The quickest we calculate is B200, at about 143 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: 15.

P1-235B-A22B— how much VRAM does it need?

It needs about 102.4 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.

P1-235B-A22B— 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.

P1-235B-A22B— how many parameters does it have?

It has a parameter count of 235B. MoE: P1-235B-A22B has 235B total, 22B active parameters. Lightweight variant P1-30B-A3B has 30B total, 3B active. (https://arxiv.org/abs/2511.13612). 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.

P1-235B-A22B— who created it?

It was published by Shanghai AI Lab, based in China, an organisation categorised as academia.

P1-235B-A22B— when was it released?

It was published in November 2025.

P1-235B-A22B— what is it used for?

It works in the domain of Language, and is recorded as handling the task of question answering. These are the areas it was designed around; they describe intent rather than a hard boundary.

P1-235B-A22B— 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.

P1-235B-A22B— 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 43.3 GB. Every figure here assumes the whole model is resident on the card.

P1-235B-A22B— would two GPUs run it faster?

Two cards buy memory rather than speed, which matters only if one card cannot hold it. The number that can: 17. So a second card is rarely the answer here.

P1-235B-A22B— why does the quantisation differ between cards?

Because capacity varies, so does how hard it has to be squeezed. The number of distinct levels in the table above: 5. Bigger cards get the more accurate version, and the quality floor above pins the comparison to one level.

P1-235B-A22B— how accurate are these speed estimates?

They are calculated from specifications rather than measured, and each carries a range. One example: 86–229 tok/s on B200. The same model and card vary by thirty to fifty per cent depending on the inference software and its version.

Source

Original publication

Record last updated 8 April 2026

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