XVERSE-65B-2 TPS calculator

Open weights XVERSE Technology,Shenzhen Yuanxiang Technology 65B parameters December 2023

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

61 cards that can run it

818 cards we hold specifications for

Smallest card that fits

A100 PCIe 40 GB

40 GB · Q3_K_M · 27.4 tok/s

Fastest card

B200

52.1 tok/s · 180 GB

Which GPUs can run XVERSE-65B-2?

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

61 cards match

Calculating

					Needs	Quantisation	Fit
52.1 tok/s 31–83 · low confidence	B200 NVIDIA	180 GB	8,000 GB/s	Jan 2024	70.3 GB	Q8_0	Comfortable
52.1 tok/s 31–83 · low confidence	B300 NVIDIA	288 GB	8,000 GB/s	Sep 2025	70.3 GB	Q8_0	Comfortable
41.6 tok/s 25–67 · low confidence	Radeon Instinct MI350X AMD	288 GB	8,190 GB/s	Jan 2025	70.3 GB	Q8_0	Comfortable
41.6 tok/s 25–67 · low confidence	Radeon Instinct MI355X AMD	288 GB	8,190 GB/s	Jan 2025	70.3 GB	Q8_0	Comfortable
33.3 tok/s 20–53 · low confidence	Radeon Instinct MI300 AMD	128 GB	6,550 GB/s	Jan 2023	70.3 GB	Q8_0	Comfortable
31.9 tok/s 19–51 · low confidence	H200 NVL NVIDIA	141 GB	4,890 GB/s	Nov 2024	70.3 GB	Q8_0	Comfortable
31.9 tok/s 19–51 · low confidence	H200 SXM 141 GB NVIDIA	141 GB	4,890 GB/s	Nov 2024	70.3 GB	Q8_0	Comfortable
30.5 tok/s 18–49 · low confidence	Radeon Instinct MI325X AMD	256 GB	6,000 GB/s	Oct 2024	70.3 GB	Q8_0	Comfortable
28.1 tok/s 17–45 · low confidence	GRID A100B NVIDIA	48 GB	1,870 GB/s	May 2020	40.0 GB	Q4_K_M	Tight
27.4 tok/s 16–44 · low confidence	A100 PCIe 40 GB NVIDIA	40 GB	1,560 GB/s	Jun 2020	32.5 GB	Q3_K_M	Tight
27.4 tok/s 16–44 · low confidence	A100 SXM4 40 GB NVIDIA	40 GB	1,560 GB/s	May 2020	32.5 GB	Q3_K_M	Tight
27.4 tok/s 16–44 · low confidence	A800 PCIe 40 GB NVIDIA	40 GB	1,560 GB/s	Nov 2022	32.5 GB	Q3_K_M	Tight
27.1 tok/s 16–43 · low confidence	Radeon Instinct MI300A AMD	128 GB	5,325 GB/s	Dec 2023	70.3 GB	Q8_0	Comfortable
27.1 tok/s 16–43 · low confidence	Radeon Instinct MI300X AMD	192 GB	5,325 GB/s	Dec 2023	70.3 GB	Q8_0	Comfortable
27.1 tok/s 16–43 · low confidence	Radeon Instinct MI308X AMD	192 GB	5,325 GB/s	Dec 2023	70.3 GB	Q8_0	Comfortable
25.7 tok/s 15–41 · low confidence	H100 NVL 94 GB NVIDIA	94 GB	3,940 GB/s	Mar 2023	70.3 GB	Q8_0	Comfortable
21.9 tok/s 13–35 · low confidence	H100 PCIe 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	70.3 GB	Q8_0	Comfortable
21.9 tok/s 13–35 · low confidence	H100 SXM5 80 GB NVIDIA	80 GB	3,360 GB/s	Oct 2022	70.3 GB	Q8_0	Tight
21.9 tok/s 13–35 · low confidence	H100 SXM5 94 GB NVIDIA	94 GB	3,360 GB/s	Mar 2023	70.3 GB	Q8_0	Comfortable
21.9 tok/s 13–35 · low confidence	H100 SXM5 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	70.3 GB	Q8_0	Comfortable
21.9 tok/s 13–35 · low confidence	H800 SXM5 NVIDIA	80 GB	3,360 GB/s	Mar 2023	70.3 GB	Q8_0	Tight
20.2 tok/s 12–32 · low confidence	RTX PRO 5000 Blackwell NVIDIA	48 GB	1,340 GB/s	Mar 2025	40.0 GB	Q4_K_M	Tight
19.1 tok/s 11–31 · low confidence	H100 SXM5 64 GB NVIDIA	64 GB	2,020 GB/s	Mar 2023	55.2 GB	Q6_K	Tight
16.7 tok/s 10–27 · low confidence	Radeon Instinct MI250 AMD	128 GB	3,280 GB/s	Nov 2021	70.3 GB	Q8_0	Comfortable
16.7 tok/s 10–27 · low confidence	Radeon Instinct MI250X AMD	128 GB	3,280 GB/s	Nov 2021	70.3 GB	Q8_0	Comfortable

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: XVERSE Technology,Shenzhen Yuanxiang Technology
Organisation type: Industry,Industry
Country: China
Published: 8 December 2023

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

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: 65B
Training data: 2,600,000,000,000 tokens

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: 1.2 × 10²⁴ FLOP
How it was established: Operation counting

The training run

What it physically took to train: which chips, how many, for how long, and what that drew from the wall.

Wall-clock time: 4,096 hours (170.7 days)
Data centre: There is no paper to reference, no information about hardware used for training found in media.

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: Open source

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: Confident

Sources

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

Last updated: 28 November 2025

The extremes

The ten fastest GPUs that run XVERSE-65B-2

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 XVERSE-65B-2

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

A100 PCIe 40 GB

Memory needed

32.5 GB

Fastest

52.1 tok/s

XVERSE-65B-2 reaches a parameter count of 65B. That puts it above consumer hardware, into the range where a card is bought for this purpose rather than repurposed for it. The number of cards we track that can hold it: 61.

The least hardware that works is A100 PCIe 40 GB, with a memory capacity of 40 GB, running it at a compression of Q3_K_M and producing around 27.4 tokens per second.

At the other end sits B200, generating roughly 52.1 tokens per second on the strength of a memory bandwidth of 8,000 GB/s.

Where it came from

XVERSE-65B-2 was published by XVERSE Technology,Shenzhen Yuanxiang Technology, in the country recorded as China, during December 2023. The publishing organisation is categorised as industry,Industry.

It works in the domain of Language, and is recorded as performing the task of chat, Language modeling/generation.

Because its weights were released, nothing about running it depends on a provider staying available — it is yours once downloaded.

Understanding the speeds

Across every card that can run it, the middle of the range sits at 13.3 tokens per second. Producing text faster than most people read it: 56 of them.

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.

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

The training run consumed about 1.2 × 10²⁴ FLOP. That figure measures what producing the model cost, and has no bearing on how fast it answers.

The training set ran to roughly 2,600,000,000,000 tokens of text.

Step by step

How to choose a GPU for XVERSE-65B-2

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

Start from what it actually needs, which is the requirement of XVERSE-65B-2, needing around 32.5 GB at a compression of Q3_K_M. That figure, not the headline performance of a card, is what decides whether it runs.
02

Match the context to your actual use

The conversation occupies memory too, and grows as it goes. Set the slider to the length you expect, because at long context a card that handles short questions easily can be dropped by XVERSE-65B-2.
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.
04

Sort by speed

Ranking by tokens per second follows memory bandwidth rather than core counts, for XVERSE-65B-2. It will not match a gaming ordering, because generation is bound by memory bandwidth. The card topping the list is B200, at 52.1 tok/s.
05

Read the fit column last

A tight fit runs, but leaves nothing spare for a longer conversation, in the case of XVERSE-65B-2. 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

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 XVERSE-65B-2.

Answers

XVERSE-65B-2 — common questions

XVERSE-65B-2— how accurate are these speed estimates?

Every figure is derived from memory bandwidth and model size, not benchmarked, which is why each is published as a range rather than a single number. One example: 31–83 tok/s on B200. The same model and card vary by thirty to fifty per cent depending on the inference software and its version.

XVERSE-65B-2— what GPU do I need to run it?

The smallest card in our catalogue that holds it is A100 PCIe 40 GB, with a memory capacity of 40 GB. It runs the model at a compression of Q3_K_M using about 32.5 GB, and produces roughly 27.4 tokens per second. The number of cards able to run it in total: 61.

XVERSE-65B-2— how fast is it on a GPU?

It depends on the card. The quickest we calculate is B200, at about 52.1 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: 56.

XVERSE-65B-2— how much VRAM does it need?

It needs about 32.5 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.

XVERSE-65B-2— 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.

XVERSE-65B-2— how many parameters does it have?

It has a parameter count of 65B. Based on the name. Exact count unknown but may be listed on Hugging Face. 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.

XVERSE-65B-2— who created it?

It was published by XVERSE Technology,Shenzhen Yuanxiang Technology, based in China, an organisation categorised as industry,Industry.

XVERSE-65B-2— when was it released?

It was published in December 2023. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.

XVERSE-65B-2— what is it used for?

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

XVERSE-65B-2— 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.

XVERSE-65B-2— how much compute was used to train it?

Training consumed around 1.2 × 10²⁴ FLOP. 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.

XVERSE-65B-2— can I run it if it does not fit in my GPU?

It can be split between the card and system memory, but it generates painfully slowly that way. The nearest miss we calculate falls short by 11.2 GB. Every figure here assumes the whole model is resident on the card.

XVERSE-65B-2— 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: 61. So a second card is rarely the answer here.

XVERSE-65B-2— 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: 4. Bigger cards get the more accurate version, and the quality floor above pins the comparison to one level.

Source

Original publication

Record last updated 28 November 2025

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