Eagle 2 TPS calculator

Open weights NVIDIA,Nanjing University,Tsinghua University,Hong Kong Polytechnic University,Johns Hopkins University,New York University (NYU) 8.9B parameters January 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

582 cards that can run it

818 cards we hold specifications for

Smallest card that fits

Quadro 6000

6 GB · Q3_K_M · 15.6 tok/s

Fastest card

B200

379 tok/s · 180 GB

Which GPUs can run Eagle 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

582 cards match

Calculating

					Needs	Quantisation	Fit
379 tok/s 228–607 · low confidence	B200 NVIDIA	180 GB	8,000 GB/s	Jan 2024	10.3 GB	Q8_0	Comfortable
379 tok/s 228–607 · low confidence	B300 NVIDIA	288 GB	8,000 GB/s	Sep 2025	10.3 GB	Q8_0	Comfortable
303 tok/s 182–485 · low confidence	Radeon Instinct MI350X AMD	288 GB	8,190 GB/s	Jan 2025	10.3 GB	Q8_0	Comfortable
303 tok/s 182–485 · low confidence	Radeon Instinct MI355X AMD	288 GB	8,190 GB/s	Jan 2025	10.3 GB	Q8_0	Comfortable
242 tok/s 145–388 · low confidence	Radeon Instinct MI300 AMD	128 GB	6,550 GB/s	Jan 2023	10.3 GB	Q8_0	Comfortable
232 tok/s 139–371 · low confidence	H200 NVL NVIDIA	141 GB	4,890 GB/s	Nov 2024	10.3 GB	Q8_0	Comfortable
232 tok/s 139–371 · low confidence	H200 SXM 141 GB NVIDIA	141 GB	4,890 GB/s	Nov 2024	10.3 GB	Q8_0	Comfortable
222 tok/s 133–355 · low confidence	Radeon Instinct MI325X AMD	256 GB	6,000 GB/s	Oct 2024	10.3 GB	Q8_0	Comfortable
197 tok/s 118–315 · low confidence	Radeon Instinct MI300A AMD	128 GB	5,325 GB/s	Dec 2023	10.3 GB	Q8_0	Comfortable
197 tok/s 118–315 · low confidence	Radeon Instinct MI300X AMD	192 GB	5,325 GB/s	Dec 2023	10.3 GB	Q8_0	Comfortable
197 tok/s 118–315 · low confidence	Radeon Instinct MI308X AMD	192 GB	5,325 GB/s	Dec 2023	10.3 GB	Q8_0	Comfortable
187 tok/s 112–299 · low confidence	H100 NVL 94 GB NVIDIA	94 GB	3,940 GB/s	Mar 2023	10.3 GB	Q8_0	Comfortable
159 tok/s 96–255 · low confidence	H100 PCIe 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	10.3 GB	Q8_0	Comfortable
159 tok/s 96–255 · low confidence	H100 SXM5 80 GB NVIDIA	80 GB	3,360 GB/s	Oct 2022	10.3 GB	Q8_0	Comfortable
159 tok/s 96–255 · low confidence	H100 SXM5 94 GB NVIDIA	94 GB	3,360 GB/s	Mar 2023	10.3 GB	Q8_0	Comfortable
159 tok/s 96–255 · low confidence	H100 SXM5 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	10.3 GB	Q8_0	Comfortable
159 tok/s 96–255 · low confidence	H800 SXM5 NVIDIA	80 GB	3,360 GB/s	Mar 2023	10.3 GB	Q8_0	Comfortable
126 tok/s 76–202 · low confidence	CMP 170HX 8 GB NVIDIA	8 GB	1,490 GB/s	Sep 2021	7.1 GB	Q5_K_M	Tight
121 tok/s 73–194 · low confidence	Radeon Instinct MI250 AMD	128 GB	3,280 GB/s	Nov 2021	10.3 GB	Q8_0	Comfortable
121 tok/s 73–194 · low confidence	Radeon Instinct MI250X AMD	128 GB	3,280 GB/s	Nov 2021	10.3 GB	Q8_0	Comfortable
108 tok/s 65–172 · low confidence	CMP 170HX 10 GB NVIDIA	10 GB	1,560 GB/s	Sep 2021	8.2 GB	Q6_K	Tight
101 tok/s 61–162 · low confidence	Data Center GPU Max 1550 Intel	128 GB	3,280 GB/s	Jan 2023	10.3 GB	Q8_0	Comfortable
99.0 tok/s 59–158 · low confidence	Data Center GPU Max Subsystem Intel	128 GB	3,210 GB/s	Jan 2023	10.3 GB	Q8_0	Comfortable
96.8 tok/s 58–155 · low confidence	A100 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Nov 2020	10.3 GB	Q8_0	Comfortable
96.8 tok/s 58–155 · low confidence	A100X NVIDIA	80 GB	2,040 GB/s	Jun 2021	10.3 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: NVIDIA,Nanjing University,Tsinghua University,Hong Kong Polytechnic University,Johns Hopkins University,New York University (NYU)
Organisation type: Industry,Academia,Academia,Academia,Academia,Academia
Country: United States of America, China, Hong Kong
Published: 20 January 2025
Authors: Zhiqi Li, Guo Chen, Shilong Liu, Shihao Wang, Vibashan VS, Yishen Ji, Shiyi Lan, Hao Zhang, Yilin Zhao, Subhashree Radhakrishnan, Nadine Chang, Karan Sapra, Amala Sanjay Deshmukh, Tuomas Rintamaki, Matthieu Le, Ilia Karmanov, Lukas Voegtle, Philipp Fischer, De-An Huang, Timo Roman, Tong Lu, Jose M. Alvarez, Bryan Catanzaro, Jan Kautz, Andrew Tao, Guilin Liu, Zhiding Yu

What it does

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

Domain: Vision, Robotics, Language
Base model: Qwen2.5-7B

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: 8.9B
Training data: 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: 4.7 × 10²² FLOP

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 H100 SXM5 80GB
Chips used: 256
Wall-clock time: 131 hours
Power draw: 352.1 kW

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 (non-commercial)
Training code: Unreleased
Hugging Face: nvidia

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: SOTA improvement
Record confidence: Confident
Citations: 57

Sources

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

Reference: Eagle 2: Building Post-Training Data Strategies from Scratch for Frontier Vision-Language Models
Last updated: 25 May 2026

The extremes

The ten fastest GPUs that run Eagle 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 Eagle 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

Hardware requirements in practice

Minimum card

Quadro 6000

Memory needed

5.1 GB

Fastest

379 tok/s

Eagle 2 is small enough at 8.9B parameters that hardware is rarely the obstacle — 582 of the cards we track can run it, including cards several years old.

The entry point is the Quadro 6000: 6 GB of memory, Q3_K_M compression, roughly 15.6 tokens per second.

At the other end, a B200 generates roughly 379 tokens per second on it, on the strength of 8,000 GB/s of memory bandwidth.

What this model is

Eagle 2 was published by NVIDIA,Nanjing University,Tsinghua University,Hong Kong Polytechnic University,Johns Hopkins University,New York University (NYU), in United States of America, in January 2025. industry,Academia,Academia,Academia,Academia,Academia is the category the publisher falls under.

It works in Vision, Robotics, Language.

Its starting point was Qwen2.5-7B — most models at this scale are adapted from an existing base rather than built from nothing.

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. It is published under the nvidia organisation on Hugging Face.

What decides the speed

Half the cards that hold it manage more than 21.3 tokens per second, and 541 exceed reading speed outright.

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.

How it was trained

Producing it required around 4.7 × 10²² FLOP of arithmetic, on NVIDIA H100 SXM5 80GB, which is a statement about the training budget rather than about inference.

The reason it appears in this catalogue at all is sOTA improvement.

Step by step

How to choose a GPU for Eagle 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

Every card here has been checked against Eagle 2 — around 5.1 GB at Q3_K_M. Capacity is the gate — a card either holds it or it does not.
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: at long context Eagle 2 can slip off a card that handles short questions easily.
03

Decide how much compression you will accept

Compression is what makes Eagle 2 fit smaller cards, at some cost in accuracy — Q3_K_M on the smallest card that fits. A minimum quality removes the ones that go too far.
04

Rank by throughput rather than spec sheet

Sort by speed to see how cards rank for Eagle 2. It will not match a gaming ordering — generation is bound by memory bandwidth, which is why the B200 tops it at 379 tok/s.
05

Look at the headroom, not just the fit

A tight fit runs Eagle 2 but leaves nothing spare for a longer conversation; comfortable has headroom. If you expect to grow the context, buy for comfortable.
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 Eagle 2.

Answers

Eagle 2 — common questions

Can I run Eagle 2 on a 8 GB GPU?

Yes. A CMP 170HX 8 GB with 8 GB runs it at Q5_K_M, using about 7.1 GB and generating roughly 126 tokens per second — a tight fit.

Can I run Eagle 2 on a 12 GB GPU?

Yes. A GeForce RTX 3080 Ti with 12 GB runs it at Q8_0, using about 10.3 GB and generating roughly 43.3 tokens per second — a tight fit.

Can I run Eagle 2 on a 16 GB GPU?

Yes. A Tesla V100 SXM2 16 GB with 16 GB runs it at Q8_0, using about 10.3 GB and generating roughly 53.6 tokens per second — a comfortable fit.

Can I run Eagle 2 on a 24 GB GPU?

Yes. A GeForce RTX 5090 D V2 with 24 GB runs it at Q8_0, using about 10.3 GB and generating roughly 63.6 tokens per second — a comfortable fit.

Is Eagle 2 open source?

Its weights are published, so Eagle 2 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 Eagle 2 have?

Eagle 2 has 8.9B parameters. Table 4 https://huggingface.co/nvidia/Eagle2-9B "8.93B 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 Eagle 2?

Eagle 2 was published by NVIDIA,Nanjing University,Tsinghua University,Hong Kong Polytechnic University,Johns Hopkins University,New York University (NYU), based in United States of America, categorised as industry,Academia,Academia,Academia,Academia,Academia.

When was Eagle 2 released?

Eagle 2 was published in January 2025.

What is Eagle 2 used for?

Eagle 2 works in Vision, Robotics, Language. 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 Eagle 2?

Its weights are published under the nvidia 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 Eagle 2?

Around 4.7 × 10²² FLOP, on NVIDIA H100 SXM5 80GB. 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 Eagle 2 if it does not fit in my GPU?

Only by offloading, which is usually a false economy: the part in system memory drags the whole thing down — the nearest miss we calculate is short by 1.6 GB. Our figures for Eagle 2 assume it is fully resident.

Would two GPUs run Eagle 2 faster?

Two cards buy memory rather than speed. That matters for Eagle 2 only if one card cannot hold it — 582 can, so a second adds little.

Why does the quantisation differ between cards for Eagle 2?

Because capacity varies, so does how hard Eagle 2 has to be squeezed — 4 distinct levels appear in the table above. Set a minimum quality to compare at one.

How accurate are these Eagle 2 speed estimates?

These are estimates with real error bars. The fastest result here, 228–607 tok/s on the B200, could reasonably land anywhere in its published range depending on which runtime you use.

What GPU do I need to run Eagle 2?

The smallest card in our catalogue that holds Eagle 2 is the Quadro 6000, with 6 GB of memory. It runs the model at Q3_K_M using about 5.1 GB, and produces roughly 15.6 tokens per second. 582 cards in total can run it.

How fast is Eagle 2 on a GPU?

It depends on the card. The quickest we calculate is a B200 at about 379 tokens per second; the slowest that still runs it manages considerably less. Reading speed is around ten tokens per second, and 541 of the cards that can run Eagle 2 clear that.

How much VRAM does Eagle 2 need?

About 5.1 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.

Source

Original publication

Record last updated 25 May 2026

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Looking at it from the other side?

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