Calculate the TPS of the Radeon RX 560X Mobile on local AI models

AMD 4 GB GDDR5 93 GB/s April 2018

Every model in our catalogue assessed against this card at the context length and minimum quality you choose. Speed is an estimate for a single request, calculated from this card's memory bandwidth and the size of each model once compressed.

Calculated for this card

97 models it can run

679 models in our catalogue altogether

Largest model it holds

DeciLM 6B

5.7B · Q3_K_M · 14.5 tok/s

Fastest model

Gemma 3 QAT 1B

30.7 tok/s · 1B

Which AI models can run on a Radeon RX 560X Mobile?

Set the inputs, read the answer

More context means more memory for the conversation cache. Speed is for a fresh conversation and does not change with this setting.

Hides models that would only fit by being compressed below this point.

97 models match

Calculating
Quantisation Fit
30.7 tok/s

18–49 · low confidence

Gemma 3 1B 1B Mar 2025 1.8 GB 33k tokens Q8_0 Comfortable
30.7 tok/s

18–49 · low confidence

Gemma 3 QAT 1B 1B Apr 2025 1.8 GB 33k tokens Q8_0 Comfortable
30.7 tok/s

18–49 · low confidence

HGRN 1B (WT 103) ≈ 1B Nov 2023 1.8 GB 131k tokens ? Q8_0 Comfortable
30.7 tok/s

18–49 · low confidence

LLama 3..2 Typhoon 2 1B ≈ 1B Dec 2024 1.8 GB 131k tokens ? Q8_0 Comfortable
30.7 tok/s

18–49 · low confidence

OLMo-1B ≈ 1B Feb 2024 1.8 GB 131k tokens ? Q8_0 Comfortable
30.7 tok/s

18–49 · low confidence

Pythia-1b ≈ 1B Apr 2023 1.8 GB 131k tokens ? Q8_0 Comfortable
28.4 tok/s

17–45 · low confidence

OpenELM-1.1B ≈ 1.1B May 2024 1.9 GB 131k tokens ? Q8_0 Comfortable
27.9 tok/s

17–45 · low confidence

DeciCoder-1B ≈ 1.1B Aug 2023 1.9 GB 131k tokens ? Q8_0 Comfortable
27.9 tok/s

17–45 · low confidence

SantaCoder ≈ 1.1B Jan 2023 1.9 GB 131k tokens ? Q8_0 Comfortable
27.9 tok/s

17–45 · low confidence

TinyLlama-1.1B (1T token checkpoint) ≈ 1.1B Oct 2023 1.9 GB 131k tokens ? Q8_0 Comfortable
27.9 tok/s

17–45 · low confidence

TinyLlama-1.1B (3T token checkpoint) ≈ 1.1B Oct 2023 1.9 GB 131k tokens ? Q8_0 Comfortable
25.6 tok/s

15–41 · low confidence

EXAONE 4.0 (1.2B) ≈ 1.2B Jul 2025 2.0 GB 131k tokens ? Q8_0 Comfortable
25.6 tok/s

15–41 · low confidence

MinerU2.5 ≈ 1.2B Sep 2025 2.0 GB 131k tokens ? Q8_0 Comfortable
25.6 tok/s

15–41 · low confidence

Pleias 1.0 1.2B ≈ 1.2B Dec 2024 2.0 GB 131k tokens ? Q8_0 Comfortable
25.6 tok/s

15–41 · low confidence

Pleias-RAG-1B ≈ 1.2B Apr 2025 2.0 GB 131k tokens ? Q8_0 Comfortable
24.9 tok/s

15–40 · low confidence

Llama 3.2 1B 1.2B Sep 2024 2.2 GB 54k tokens Q8_0 Comfortable
24.6 tok/s

15–39 · low confidence

MiniCPM-1.2B ≈ 1.2B Jun 2024 2.0 GB 131k tokens ? Q8_0 Comfortable
23.6 tok/s

14–38 · low confidence

DeepSeek Coder 1.3B ≈ 1.3B Jan 2024 2.1 GB 131k tokens ? Q8_0 Comfortable
23.6 tok/s

14–38 · low confidence

DeepSeek-VL-1.3B ≈ 1.3B Mar 2024 2.1 GB 131k tokens ? Q8_0 Comfortable
23.6 tok/s

14–38 · low confidence

DigiRL ≈ 1.3B Jun 2024 2.1 GB 131k tokens ? Q8_0 Comfortable
23.6 tok/s

14–38 · low confidence

GLA Transformer 1.3B ≈ 1.3B Aug 2024 2.1 GB 131k tokens ? Q8_0 Comfortable
23.6 tok/s

14–38 · low confidence

Janus 1.3B ≈ 1.3B Oct 2024 2.1 GB 131k tokens ? Q8_0 Comfortable
23.6 tok/s

14–38 · low confidence

Kosmos-2.5 ≈ 1.3B Aug 2024 2.1 GB 131k tokens ? Q8_0 Comfortable
23.6 tok/s

14–38 · low confidence

Otter ≈ 1.3B May 2023 2.1 GB 131k tokens ? Q8_0 Comfortable
23.6 tok/s

14–38 · low confidence

Phi-1 ≈ 1.3B Oct 2023 2.1 GB 131k tokens ? 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

Radeon RX 560X Mobile full specification

Everything on record for this board, ordered by how much it bears on running a language model rather than by how a spec sheet would list it. Memory comes first because it decides the outcome; the rest is context.

Memory

The two specifications that decide what this card can run and how quickly. Capacity sets which models fit; bandwidth sets how many tokens per second they produce once they do.

Memory size
4 GB
Memory bandwidth
93 GB/s
Memory type
GDDR5
Memory bus width
128 bit
Memory clock
1.45 GHz

The chip

Which processor is on the board and how it was manufactured. A smaller process size generally means more performance for the same power.

Graphics processor
Polaris 21
Architecture
GCN 4.0
Generation
Polaris Mobile(RX M500X)
Foundry
GlobalFoundries
Process size
14 nm
Transistors
3 billion
Transistor density
24,400 K/mm²
Die size
123 mm²
Package
BGA-769
Released
11 April 2018

Clock speeds

How fast the processor runs. Worth far less here than on a gaming benchmark: generating text is limited by memory bandwidth, so a higher clock barely moves the result.

Base clock
1.28 GHz
Boost clock
1.28 GHz

Processing units

What the chip contains. These drive graphics performance and matter mainly for processing a long prompt rather than for producing the answer.

Shading units
1,024
Texture mapping units
64
Render output units
16
L1 cache
16 KB
L2 cache
1 MB

Theoretical performance

Peak arithmetic rates published for the board. These are ceilings that no real workload reaches, and generating text reaches a small fraction of them because it is limited by memory rather than arithmetic.

Half precision (FP16)
2.6 TFLOPS
Single precision (FP32)
2.6 TFLOPS
Double precision (FP64)
163.2 GFLOPS
Pixel rate
20 GPixel/s
Texture rate
82 GTexel/s

The board

What it takes to physically install and power the card — the practical constraints that decide whether it fits the machine you already own.

Power draw (TDP)
65 W
Power connectors
None
Bus interface
MXM-B (3.0)
Slot width
MXM Module

Software support

Which graphics and compute interfaces the card supports. CUDA compute capability is the one that bears on inference: below 7.0 there are no tensor cores, and modern inference software falls back to slower code paths.

DirectX
12.0
OpenGL
4.6
Vulkan
1.3
OpenCL
2.1
Shader model
6.7

Listings

Where to buy a Radeon RX 560X Mobile

No vendor is currently listing this card. Listings come from vendors who publish them here directly — browse the vendor directory to see who is selling what.

What the numbers mean

Memory: the specification that decides everything

Memory

4 GB

Bandwidth

93 GB/s

Largest model

DeciLM 6B

At 4 GB of GDDR5 the Radeon RX 560X Mobile is limited to the smaller end of the catalogue. About 3.6 GB is actually available to a runtime, and a model has to fit entirely inside it before generating anything at all.

At 93 GB/s across a 128-bit bus, bandwidth is this card's real constraint. Every token requires reading the entire model out of memory, so a large model will feel slow here even when it fits.

The figure is the memory clock — 1.45 GHz here — multiplied by the bus width. It is why core counts predict generation speed so poorly.

Put together, the largest model that fits is DeciLM 6B at 5.7B, running Q3_K_M and producing around 14.5 tokens per second.

The chip and how it was built

The Radeon RX 560X Mobile is built on the Polaris 21 graphics processor, using AMD's GCN 4.0 architecture, as part of the Polaris Mobile(RX M500X) generation.

The chip is manufactured by GlobalFoundries, on a 14 nm process, with a die measuring 123 mm², holding 3 billion transistors. A smaller process generally means more performance for the same power, though for language models it matters far less than the memory subsystem.

It was released in April 2018, roughly 8 years ago. Inference software support tends to follow hardware by a year or two, so a card of this age generally has mature, well-optimised code paths available to it.

Compute throughput, and why it matters less than it looks

FP16

2.6 TFLOPS

FP64

163.2 GFLOPS

On paper the Radeon RX 560X Mobile reaches 2.6 TFLOPS at half precision and 2.6 TFLOPS at single precision. These are peak figures no real workload sustains, and generating text reaches only a small fraction of them — decoding is limited by memory rather than arithmetic, which is why a card can look enormously powerful here and still produce tokens at an ordinary rate.

Double-precision throughput is 163.2 GFLOPS. It has no bearing on running a language model — no inference runtime uses it — but it separates datacentre parts from consumer ones, since the latter deliberately restrict it.

Clocks run from 1.28 GHz at base to 1.28 GHz boosted. Worth far less here than on a gaming benchmark: raising the clock speeds up the arithmetic, and the arithmetic is not what generation is waiting on.

Cache and processing units

The Radeon RX 560X Mobile has 16 KB of L1 cache, backed by 1 MB of L2. Cache absorbs a share of the memory traffic that would otherwise hit the main bus, which is the one place on this page where a number other than bandwidth quietly affects generation speed — a large L2 lets more of the working set stay close to the cores.

There are 1,024 shading units, 64 texture mapping units, and 16 render output units. These drive graphics workloads and contribute to prompt processing, but they sit idle for much of the time a model spends generating a reply.

Power, size and installation

Power draw

65 W

The Radeon RX 560X Mobile is rated at 65 W. Running a language model keeps a card busy in bursts rather than continuously — it draws hard while generating and idles between requests — so sustained draw over a working day is usually well below the rated figure.

The board occupies a mxm module. Worth checking against the case and power supply already in the machine, since the largest cards need considerably more of both than a typical desktop provides.

It connects over MXM-B (3.0). The interface governs how quickly a model is loaded from disk into the card, not how fast it runs once there, so a narrower link costs a few seconds at startup and nothing thereafter.

The extremes

The largest AI models that run on a Radeon RX 560X Mobile

The biggest open-weight models that fit on this card, newest first. Each is shown at the best compression the card can hold.

  1. 01 Qwen3.5-4B 4B · Q5_K_M · Feb 2026 13.7 tok/s
  2. 02 Voxtral Mini 4.7B · Q4_K_M · Jul 2025 15.1 tok/s
  3. 03 Typhoon 2.1 Gemma 4B 4B · Q4_K_M · May 2025 17.7 tok/s
  4. 04 Qwen3-4B 4B · Q3_K_M · Apr 2025 20.7 tok/s
  5. 05 Gemma 3 QAT 4B 4B · Q4_K_M · Apr 2025 17.7 tok/s
  6. 06 Gemma 3 4B 4B · Q4_K_M · Mar 2025 17.7 tok/s
  7. 07 Phi-4-Multimodal 5.6B · Q3_K_M · Mar 2025 14.8 tok/s
  8. 08 Minitron 4B 4.2B · Q4_K_M · Nov 2024 16.9 tok/s
  9. 09 XVERSE-MoE-A4.2B 4.2B · Q4_K_M · Apr 2024 16.9 tok/s
  10. 10 DeciLM 6B 5.7B · Q3_K_M · Sep 2023 14.5 tok/s

The fastest AI models on a Radeon RX 560X Mobile

Where this card produces tokens quickest. Smaller models dominate here, because generating each token means reading the whole model out of memory once.

  1. 01 Gemma 3 QAT 1B 1B · Q8_0 · 1.8 GB 30.7 tok/s
  2. 02 Gemma 3 1B 1B · Q8_0 · 1.8 GB 30.7 tok/s
  3. 03 LLama 3..2 Typhoon 2 1B 1B · Q8_0 · 1.8 GB 30.7 tok/s
  4. 04 OLMo-1B 1B · Q8_0 · 1.8 GB 30.7 tok/s
  5. 05 HGRN 1B (WT 103) 1B · Q8_0 · 1.8 GB 30.7 tok/s
  6. 06 Pythia-1b 1B · Q8_0 · 1.8 GB 30.7 tok/s
  7. 07 OpenELM-1.1B 1.1B · Q8_0 · 1.9 GB 28.4 tok/s
  8. 08 TinyLlama-1.1B (1T token checkpoint) 1.1B · Q8_0 · 1.9 GB 27.9 tok/s
  9. 09 TinyLlama-1.1B (3T token checkpoint) 1.1B · Q8_0 · 1.9 GB 27.9 tok/s
  10. 10 DeciCoder-1B 1.1B · Q8_0 · 1.9 GB 27.9 tok/s

Step by step

How to work out the tokens per second of a Radeon RX 560X Mobile

You do not have to calculate anything by hand — the gputps.com calculator on this page has already worked it out for every model this card can hold. Reading off the answer takes six steps.

  1. 01

    Start with the model, not the specification

    Every one of the 97 models this Radeon RX 560X Mobile runs is in the table above. Search narrows it by name or by size.

  2. 02

    Set the context length you will actually use

    Longer conversations cost memory on top of the weights. With 4 GB to work in, that is frequently the difference between a model fitting and not.

  3. 03

    Choose how far you will compress

    Compression is what lets bigger models fit. The quality control drops any model that needs more of it than you are willing to give.

  4. 04

    Take the range as the answer

    Each speed is an estimate for a single conversation, with a range beneath it — 30.7 tok/s on Gemma 3 QAT 1B at the top end here. The same card and model vary by thirty to fifty per cent between inference runtimes.

  5. 05

    Check the memory column before committing

    The fit column separates models that just fit from those with room to spare — worth checking against the card's 4 GB before settling on one.

  6. 06

    Open the model to compare cards

    Every model name in the table links to its own page, which runs the same calculation across every card we hold. That is where you see whether the Radeon RX 560X Mobile is the right buy for it or merely a card that fits.

Answers

Radeon RX 560X Mobile — common questions

01

Can a Radeon RX 560X Mobile run a model that does not fit in its memory?

Offloading past the card's 4 GB is possible and usually a false economy: the system-memory portion is slow enough to dominate the result.

02

Would two Radeon RX 560X Mobile cards be twice as fast?

Pairing Radeon RX 560X Mobile cards buys headroom rather than pace: 8 GB of combined memory, at roughly the same generation speed as one.

03

What AI models can a Radeon RX 560X Mobile run?

97 of the 679 open-weight language models we track fit on a Radeon RX 560X Mobile and can be run locally on it. The table on this page lists every one, with the memory it needs, the quantisation it runs at and an estimated generation speed.

04

What is the largest AI model a Radeon RX 560X Mobile can run?

The largest model in our catalogue that fits on a Radeon RX 560X Mobile is DeciLM 6B at 5.7B parameters, compressed to Q3_K_M. It generates roughly 14.5 tokens per second and needs about 3.5 GB of the card's memory.

05

How many tokens per second does a Radeon RX 560X Mobile produce?

It depends on the model. On a Radeon RX 560X Mobile the fastest model we track is Gemma 3 QAT 1B at about 30.7 tokens per second, while larger models run proportionally slower because each token requires reading the whole model out of memory once. Speeds are estimates for a single conversation at a time.

06

How much memory does a Radeon RX 560X Mobile have?

A Radeon RX 560X Mobile has 4 GB of GDDR5 memory. Around a tenth of that is reserved by the inference runtime and the driver, leaving roughly 3.6 GB available for a model and its conversation.

07

What is the memory bandwidth of a Radeon RX 560X Mobile?

The Radeon RX 560X Mobile has 93 GB/s of memory bandwidth, across a 128-bit memory bus. This is the single best predictor of how fast it generates text, because producing each token means reading the entire model out of memory once.

08

What type of memory does a Radeon RX 560X Mobile use?

It uses GDDR5 clocked at 1.45 GHz. HBM types are found on datacentre accelerators and carry far more bandwidth than the GDDR used on desktop cards, which is why they generate tokens considerably faster at the same capacity.

09

Who makes the Radeon RX 560X Mobile?

The Radeon RX 560X Mobile is a AMD product, with the chip manufactured by GlobalFoundries, on a 14 nm process.

10

When was the Radeon RX 560X Mobile released?

The Radeon RX 560X Mobile was released in April 2018.

11

How much power does a Radeon RX 560X Mobile use?

The Radeon RX 560X Mobile has a rated board power of 65 W. Generating text draws hard in bursts and idles between requests, so average consumption over a working session is normally well below the rated figure.

12

How much cache does a Radeon RX 560X Mobile have?

The Radeon RX 560X Mobile has 16 KB of L1 cache, and 1 MB of L2 cache. Cache absorbs part of the memory traffic that would otherwise reach the main bus, so a larger L2 gives a modest lift to generation speed beyond what bandwidth alone predicts.

13

What are the TFLOPS of a Radeon RX 560X Mobile?

The Radeon RX 560X Mobile is rated at 2.6 TFLOPS at half precision and 2.6 TFLOPS at single precision. These are peak arithmetic ceilings rather than achievable rates, and text generation reaches only a small fraction of them because it is limited by memory bandwidth instead.

14

Does the Radeon RX 560X Mobile support CUDA?

No. CUDA is NVIDIA-only, and the Radeon RX 560X Mobile is a AMD card. It runs language models through ROCm, Vulkan or Metal depending on the software, which are less mature than the CUDA path — our estimates apply a penalty for that.

15

What bus interface does the Radeon RX 560X Mobile use?

It uses MXM-B (3.0). This governs how fast a model is loaded onto the card rather than how fast it runs once loaded, so it costs a few seconds at startup and nothing during generation.

16

Is the Radeon RX 560X Mobile good for running local AI models?

Its memory limits it to smaller models though its bandwidth means generation will feel slow on larger models. In total it runs 97 of the models we track. Whether that is enough depends entirely on which model you want — the table above answers that directly.

The other direction

Looking at it from the other side?

This page starts from the hardware. If you already know which model you want and need to know what it takes to run it, start from the model instead.

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