Gemma 3 QAT 27B 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
Xeon Phi 7120P
16 GB · Q3_K_M · 9.7 tok/s
Fastest card
B200
125 tok/s · 180 GB
Which GPUs can run Gemma 3 QAT 27B?
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.
241 cards match
Calculating| Needs | Quantisation | Fit | |||||
|---|---|---|---|---|---|---|---|
|
125
tok/s
107–151 |
B200 NVIDIA | 180 GB | 8,000 GB/s | Jan 2024 | 29.0 GB | Q8_0 | Comfortable |
|
125
tok/s
107–151 |
B300 NVIDIA | 288 GB | 8,000 GB/s | Sep 2025 | 29.0 GB | Q8_0 | Comfortable |
|
100
tok/s
60–160 · low confidence |
Radeon Instinct MI350X AMD | 288 GB | 8,190 GB/s | Jan 2025 | 29.0 GB | Q8_0 | Comfortable |
|
100
tok/s
60–160 · low confidence |
Radeon Instinct MI355X AMD | 288 GB | 8,190 GB/s | Jan 2025 | 29.0 GB | Q8_0 | Comfortable |
|
80.1
tok/s
48–128 · low confidence |
Radeon Instinct MI300 AMD | 128 GB | 6,550 GB/s | Jan 2023 | 29.0 GB | Q8_0 | Comfortable |
|
76.7
tok/s
65–92 |
H200 NVL NVIDIA | 141 GB | 4,890 GB/s | Nov 2024 | 29.0 GB | Q8_0 | Comfortable |
|
76.7
tok/s
65–92 |
H200 SXM 141 GB NVIDIA | 141 GB | 4,890 GB/s | Nov 2024 | 29.0 GB | Q8_0 | Comfortable |
|
73.4
tok/s
44–117 · low confidence |
Radeon Instinct MI325X AMD | 256 GB | 6,000 GB/s | Oct 2024 | 29.0 GB | Q8_0 | Comfortable |
|
65.2
tok/s
39–104 · low confidence |
Radeon Instinct MI300A AMD | 128 GB | 5,325 GB/s | Dec 2023 | 29.0 GB | Q8_0 | Comfortable |
|
65.2
tok/s
39–104 · low confidence |
Radeon Instinct MI300X AMD | 192 GB | 5,325 GB/s | Dec 2023 | 29.0 GB | Q8_0 | Comfortable |
|
65.2
tok/s
39–104 · low confidence |
Radeon Instinct MI308X AMD | 192 GB | 5,325 GB/s | Dec 2023 | 29.0 GB | Q8_0 | Comfortable |
|
61.8
tok/s
53–74 |
H100 NVL 94 GB NVIDIA | 94 GB | 3,940 GB/s | Mar 2023 | 29.0 GB | Q8_0 | Comfortable |
|
52.7
tok/s
45–63 |
H100 PCIe 96 GB NVIDIA | 96 GB | 3,360 GB/s | Mar 2023 | 29.0 GB | Q8_0 | Comfortable |
|
52.7
tok/s
45–63 |
H100 SXM5 80 GB NVIDIA | 80 GB | 3,360 GB/s | Oct 2022 | 29.0 GB | Q8_0 | Comfortable |
|
52.7
tok/s
45–63 |
H100 SXM5 94 GB NVIDIA | 94 GB | 3,360 GB/s | Mar 2023 | 29.0 GB | Q8_0 | Comfortable |
|
52.7
tok/s
45–63 |
H100 SXM5 96 GB NVIDIA | 96 GB | 3,360 GB/s | Mar 2023 | 29.0 GB | Q8_0 | Comfortable |
|
52.7
tok/s
45–63 |
H800 SXM5 NVIDIA | 80 GB | 3,360 GB/s | Mar 2023 | 29.0 GB | Q8_0 | Comfortable |
|
47.8
tok/s
41–57 |
Tesla V100 SXM2 16 GB NVIDIA | 16 GB | 1,130 GB/s | Nov 2019 | 13.3 GB | Q3_K_M | Tight |
|
42.6
tok/s
36–51 |
DRIVE A100 PROD NVIDIA | 32 GB | 1,870 GB/s | May 2020 | 22.7 GB | Q6_K | Comfortable |
|
42.6
tok/s
36–51 |
GRID A100A NVIDIA | 32 GB | 1,870 GB/s | May 2020 | 22.7 GB | Q6_K | Comfortable |
|
40.8
tok/s
35–49 |
GeForce RTX 5090 NVIDIA | 32 GB | 1,790 GB/s | Jan 2025 | 22.7 GB | Q6_K | Comfortable |
|
40.8
tok/s
35–49 |
GeForce RTX 5090 D NVIDIA | 32 GB | 1,790 GB/s | Jan 2025 | 22.7 GB | Q6_K | Comfortable |
|
40.6
tok/s
35–49 |
GeForce RTX 5080 NVIDIA | 16 GB | 960 GB/s | Jan 2025 | 13.3 GB | Q3_K_M | Tight |
|
40.1
tok/s
24–64 · low confidence |
Radeon Instinct MI250 AMD | 128 GB | 3,280 GB/s | Nov 2021 | 29.0 GB | Q8_0 | Comfortable |
|
40.1
tok/s
24–64 · low confidence |
Radeon Instinct MI250X AMD | 128 GB | 3,280 GB/s | Nov 2021 | 29.0 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
- Google DeepMind
- Organisation type
- Industry
- Country
- United States of America
- Published
- 18 April 2025
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language, Vision, Multimodal
- Task
- Language modeling/generation, Question answering, Translation, Chat, Quantitative reasoning, Visual question answering, Code generation
- Base model
- Gemma 3 27B
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
- 27B
- Training data
- tokens
27B
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
- Hugging Face
Gemma license https://huggingface.co/google/gemma-3-27b-it-qat-q4_0-gguf
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Confident
Sources
Where this record came from and when it was last checked.
- Reference
- Gemma 3 QAT Models: Bringing state-of-the-Art AI to consumer GPUs
- Last updated
- 28 November 2025
The extremes
The ten fastest GPUs for Gemma 3 QAT 27B
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 B300 288 GB · 8,000 GB/s · Q8_0 125 tok/s
- 02 B200 180 GB · 8,000 GB/s · Q8_0 125 tok/s
- 03 Radeon Instinct MI350X 288 GB · 8,190 GB/s · Q8_0 100 tok/s
- 04 Radeon Instinct MI355X 288 GB · 8,190 GB/s · Q8_0 100 tok/s
- 05 Radeon Instinct MI300 128 GB · 6,550 GB/s · Q8_0 80.1 tok/s
- 06 H200 NVL 141 GB · 4,890 GB/s · Q8_0 76.7 tok/s
- 07 H200 SXM 141 GB 141 GB · 4,890 GB/s · Q8_0 76.7 tok/s
- 08 Radeon Instinct MI325X 256 GB · 6,000 GB/s · Q8_0 73.4 tok/s
- 09 Radeon Instinct MI300A 128 GB · 5,325 GB/s · Q8_0 65.2 tok/s
- 10 Radeon Instinct MI300X 192 GB · 5,325 GB/s · Q8_0 65.2 tok/s
The smallest GPUs that still run Gemma 3 QAT 27B
The cheapest route in, by memory capacity. A tight fit runs the model but leaves nothing spare for a longer conversation.
- 01 Ryzen AI Z2 Extreme GPU 16 GB · needs 13.3 GB · Q3_K_M · tight 8.5 tok/s
- 02 Radeon RX 7700 16 GB · needs 13.3 GB · Q3_K_M · tight 20.5 tok/s
- 03 Arc Pro B50 16 GB · needs 13.3 GB · Q3_K_M · tight 6.2 tok/s
- 04 RTX PRO 2000 Blackwell 16 GB · needs 13.3 GB · Q3_K_M · tight 12.2 tok/s
- 05 Ryzen Z2 Extreme GPU 16 GB · needs 13.3 GB · Q3_K_M · tight 4.2 tok/s
- 06 Radeon RX 9060 XT 16 GB 16 GB · needs 13.3 GB · Q3_K_M · tight 10.6 tok/s
- 07 GeForce RTX 5060 Ti 16 GB 16 GB · needs 13.3 GB · Q3_K_M · tight 19.0 tok/s
- 08 GeForce RTX 5080 Mobile 16 GB · needs 13.3 GB · Q3_K_M · tight 37.9 tok/s
- 09 Radeon RX 9070 16 GB · needs 13.3 GB · Q3_K_M · tight 21.3 tok/s
- 10 Radeon RX 9070 XT 16 GB · needs 13.3 GB · Q3_K_M · tight 21.3 tok/s
What the numbers mean
The hardware side
Minimum card
Xeon Phi 7120P
Memory needed
13.3 GB
Fastest
125 tok/s
With 27B parameters, Gemma 3 QAT 27B lands in the range a serious desktop card can handle once the weights are compressed. 241 of the cards we track can run it.
At the low end, a Xeon Phi 7120P handles it — 16 GB, at Q3_K_M, for about 9.7 tokens per second.
The quickest result comes from a B200 at around 125 tokens per second — its 8,000 GB/s of bandwidth is what buys that.
Where it came from
Gemma 3 QAT 27B was published by Google DeepMind, in United States of America, in April 2025. industry is the category the publisher falls under.
It works in Language, Vision, Multimodal, and is recorded as doing language modeling/generation, Question answering, Translation, Chat, Quantitative reasoning, Visual question answering, Code generation.
It builds on Gemma 3 27B, which is why it shares that model's general shape and size.
Published weights mean the model runs on your machine rather than someone else's, which is what makes the hardware question below answerable at all. It is published under the google organisation on Hugging Face.
Understanding the speeds
Half the cards that hold it manage more than 19.0 tokens per second, and 196 exceed reading speed outright.
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.
Because the architecture is recorded, the memory column is derived rather than estimated.
Step by step
How to choose a GPU for Gemma 3 QAT 27B
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
Look at what Gemma 3 QAT 27B actually needs — around 13.3 GB at Q3_K_M. No amount of processing power compensates for a card that cannot hold it.
-
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 Gemma 3 QAT 27B.
-
03
Choose how far you will compress it
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 Gemma 3 QAT 27B by squeezing it further than you would want.
-
04
Compare tokens per second, not specifications
The speed ordering for Gemma 3 QAT 27B is effectively an ordering by memory bandwidth, which is why the B200 tops it at 125 tok/s.
-
05
Check the fit verdict before buying
Tight means Gemma 3 QAT 27B 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
See what else that card runs
Every card name links to its own page, which runs the same calculation across the whole model catalogue. Worth a look before buying for Gemma 3 QAT 27B alone — a card is usually bought for more than one model.
Answers
Gemma 3 QAT 27B — common questions
What GPU do I need to run Gemma 3 QAT 27B?
The smallest card in our catalogue that holds Gemma 3 QAT 27B is the Xeon Phi 7120P, with 16 GB of memory. It runs the model at Q3_K_M using about 13.3 GB, and produces roughly 9.7 tokens per second. 241 cards in total can run it.
How fast is Gemma 3 QAT 27B on a GPU?
It depends on the card. The quickest we calculate is a B200 at about 125 tokens per second; the slowest that still runs it manages considerably less. Reading speed is around ten tokens per second, and 196 of the cards that can run Gemma 3 QAT 27B clear that.
How much VRAM does Gemma 3 QAT 27B need?
About 13.3 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.
Can I run Gemma 3 QAT 27B on a 16 GB GPU?
Yes. A Tesla V100 SXM2 16 GB with 16 GB runs it at Q3_K_M, using about 13.3 GB and generating roughly 47.8 tokens per second — a tight fit.
Can I run Gemma 3 QAT 27B on a 24 GB GPU?
Yes. A GeForce RTX 5090 D V2 with 24 GB runs it at Q5_K_M, using about 19.5 GB and generating roughly 37.5 tokens per second — a tight fit.
Is Gemma 3 QAT 27B open source?
Its weights are published, so Gemma 3 QAT 27B 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 Gemma 3 QAT 27B have?
Gemma 3 QAT 27B has 27B parameters. 27B. 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 Gemma 3 QAT 27B?
Gemma 3 QAT 27B was published by Google DeepMind, based in United States of America, categorised as industry.
When was Gemma 3 QAT 27B released?
Gemma 3 QAT 27B was published in April 2025.
What is Gemma 3 QAT 27B used for?
Gemma 3 QAT 27B works in Language, Vision, Multimodal, and is recorded as handling language modeling/generation, Question answering, Translation, Chat, Quantitative reasoning, Visual question answering, Code generation. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
Where can I download Gemma 3 QAT 27B?
Its weights are published under the google organisation on Hugging Face. We do not host model files — this site calculates what hardware is needed to run them.
Can I run Gemma 3 QAT 27B 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 5.6 GB. Our figures for Gemma 3 QAT 27B assume it is fully resident.
Would two GPUs run Gemma 3 QAT 27B faster?
Two cards buy memory rather than speed. That matters for Gemma 3 QAT 27B only if one card cannot hold it — 241 can, so a second adds little.
Why does the quantisation differ between cards for Gemma 3 QAT 27B?
A larger card holds a more accurate copy. Across the cards that run Gemma 3 QAT 27B, 5 compression levels are used; the floor control above pins it to one.
How accurate are these Gemma 3 QAT 27B 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 107–151 tok/s on the B200 rather than a single number.
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.