Calculate the TPS of the Radeon RX Vega 56 Mobile on local AI models
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
721 models in our catalogue altogether
Largest model it holds
Baichuan 1-13B
13.3B · Q3_K_M · 27.5 tok/s
Fastest model
Gemma 3 QAT 1B
135 tok/s · 1B
Which AI models can run on a Radeon RX Vega 56 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.
351 models match
Calculating| Quantisation | Fit | ||||||
|---|---|---|---|---|---|---|---|
|
135
tok/s
81–217 · low confidence |
Gemma 3 1B | 1B | Mar 2025 | 1.8 GB | 33k tokens | Q8_0 | Comfortable |
|
135
tok/s
81–217 · low confidence |
Gemma 3 QAT 1B | 1B | Apr 2025 | 1.8 GB | 33k tokens | Q8_0 | Comfortable |
|
135
tok/s
81–217 · low confidence |
HGRN 1B (WT 103) ≈ | 1B | Nov 2023 | 1.8 GB | 131k tokens ? | Q8_0 | Comfortable |
|
135
tok/s
81–217 · low confidence |
LLama 3..2 Typhoon 2 1B ≈ | 1B | Dec 2024 | 1.8 GB | 131k tokens ? | Q8_0 | Comfortable |
|
135
tok/s
81–217 · low confidence |
OLMo-1B ≈ | 1B | Feb 2024 | 1.8 GB | 131k tokens ? | Q8_0 | Comfortable |
|
135
tok/s
81–217 · low confidence |
Pythia-1b ≈ | 1B | Apr 2023 | 1.8 GB | 131k tokens ? | Q8_0 | Comfortable |
|
125
tok/s
75–200 · low confidence |
OpenELM-1.1B ≈ | 1.1B | May 2024 | 1.9 GB | 131k tokens ? | Q8_0 | Comfortable |
|
123
tok/s
74–197 · low confidence |
DeciCoder-1B ≈ | 1.1B | Aug 2023 | 1.9 GB | 131k tokens ? | Q8_0 | Comfortable |
|
123
tok/s
74–197 · low confidence |
SantaCoder ≈ | 1.1B | Jan 2023 | 1.9 GB | 131k tokens ? | Q8_0 | Comfortable |
|
123
tok/s
74–197 · low confidence |
TinyLlama-1.1B (1T token checkpoint) ≈ | 1.1B | Oct 2023 | 1.9 GB | 131k tokens ? | Q8_0 | Comfortable |
|
123
tok/s
74–197 · low confidence |
TinyLlama-1.1B (3T token checkpoint) ≈ | 1.1B | Oct 2023 | 1.9 GB | 131k tokens ? | Q8_0 | Comfortable |
|
113
tok/s
68–180 · low confidence |
EXAONE 4.0 (1.2B) ≈ | 1.2B | Jul 2025 | 2.0 GB | 131k tokens ? | Q8_0 | Comfortable |
|
113
tok/s
68–180 · low confidence |
LFM2-1.2B ≈ | 1.2B | Jul 2025 | 2.0 GB | 131k tokens ? | Q8_0 | Comfortable |
|
113
tok/s
68–180 · low confidence |
MinerU2.5 ≈ | 1.2B | Sep 2025 | 2.0 GB | 131k tokens ? | Q8_0 | Comfortable |
|
113
tok/s
68–180 · low confidence |
Pleias 1.0 1.2B ≈ | 1.2B | Dec 2024 | 2.0 GB | 131k tokens ? | Q8_0 | Comfortable |
|
113
tok/s
68–180 · low confidence |
Pleias-RAG-1B ≈ | 1.2B | Apr 2025 | 2.0 GB | 131k tokens ? | Q8_0 | Comfortable |
|
110
tok/s
66–176 · low confidence |
Llama 3.2 1B | 1.2B | Sep 2024 | 2.2 GB | 131k tokens | Q8_0 | Comfortable |
|
108
tok/s
65–174 · low confidence |
MiniCPM-1.2B ≈ | 1.2B | Jun 2024 | 2.0 GB | 131k tokens ? | Q8_0 | Comfortable |
|
104
tok/s
62–167 · low confidence |
DeepSeek Coder 1.3B ≈ | 1.3B | Jan 2024 | 2.1 GB | 131k tokens ? | Q8_0 | Comfortable |
|
104
tok/s
62–167 · low confidence |
DeepSeek-VL-1.3B ≈ | 1.3B | Mar 2024 | 2.1 GB | 131k tokens ? | Q8_0 | Comfortable |
|
104
tok/s
62–167 · low confidence |
DigiRL ≈ | 1.3B | Jun 2024 | 2.1 GB | 131k tokens ? | Q8_0 | Comfortable |
|
104
tok/s
62–167 · low confidence |
GLA Transformer 1.3B ≈ | 1.3B | Aug 2024 | 2.1 GB | 131k tokens ? | Q8_0 | Comfortable |
|
104
tok/s
62–167 · low confidence |
Janus 1.3B ≈ | 1.3B | Oct 2024 | 2.1 GB | 131k tokens ? | Q8_0 | Comfortable |
|
104
tok/s
62–167 · low confidence |
Kosmos-2.5 ≈ | 1.3B | Aug 2024 | 2.1 GB | 131k tokens ? | Q8_0 | Comfortable |
|
104
tok/s
62–167 · low confidence |
Otter ≈ | 1.3B | May 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 Vega 56 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
- 8 GB
- Memory bandwidth
- 410 GB/s
- Memory type
- HBM2
- Memory bus width
- 2,048 bit
- Memory clock
- 800 MHz
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
- Vega 10
- Architecture
- GCN 5.0
- Generation
- Polaris Mobile(Vega)
- Foundry
- GlobalFoundries
- Process size
- 14 nm
- Transistors
- 12.5 billion
- Transistor density
- 25,300 K/mm²
- Die size
- 495 mm²
- Package
- BGA-2013
- Released
- 1 June 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
- 1.14 GHz
- Boost clock
- 1.3 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
- 3,584
- Texture mapping units
- 224
- Render output units
- 64
- L1 cache
- 16 KB
- L2 cache
- 4 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)
- 18.7 TFLOPS
- Single precision (FP32)
- 9.3 TFLOPS
- Double precision (FP64)
- 582.8 GFLOPS
- Pixel rate
- 83 GPixel/s
- Texture rate
- 291 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)
- 120 W
- Power connectors
- None
- Bus interface
- PCIe 3.0 x16
- Slot width
- MXM Module
- Dimensions
- 105 mm
- Display outputs
- 1x HDMI 2.0b, 3x DisplayPort 1.4a
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
- 12.1
- OpenGL
- 4.6
- Vulkan
- 1.3
- OpenCL
- 2.1
- Shader model
- 6.7
Listings
Where to buy a Radeon RX Vega 56 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
Capacity and bandwidth
Memory
8 GB
Bandwidth
410 GB/s
Largest model
Baichuan 1-13B
Radeon RX Vega 56 Mobile carries only 8 GB of HBM2. That limits it to the smaller end of the catalogue, and a model has to fit entirely inside before it generates anything at all. A runtime actually gets about 7.2 GB.
Memory bandwidth reaches 410 GB/s across a bus of 2,048 bits. That is the number governing generation speed — arithmetic per byte read is small enough that the bus, not the cores, is what everything waits on.
The figure is the bus width multiplied by a memory clock of 800 MHz. Both halves matter, and neither is visible in a gaming benchmark.
The biggest thing it holds is Baichuan 1-13B, 13.3B, compressed to Q3_K_M and generating around 27.5 tokens per second.
The chip and how it was built
Radeon RX Vega 56 Mobile is built on the graphics processor Vega 10, using the architecture GCN 5.0 from AMD, as part of the generation Polaris Mobile(Vega).
The chip is manufactured by GlobalFoundries, on a process of 14 nm, with a die measuring 495 mm², holding 12.5 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 June 2018, roughly 8.2868242945331 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
18.7 TFLOPS
FP64
582.8 GFLOPS
On paper Radeon RX Vega 56 Mobile reaches 18.7 TFLOPS at half precision, and 9.3 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 reaches 582.8 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 a base of 1.14 GHz to a boost of 1.3 GHz. 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
Radeon RX Vega 56 Mobile has an L1 cache of 16 KB, backed by an L2 cache of 4 MB. 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 3,584 shading units, 224 texture mapping units, and 64 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
120 W
Radeon RX Vega 56 Mobile is rated at 120 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 mxm module, measuring 105 mm long. 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 PCIe 3.0 x16. 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 Vega 56 Mobile
The biggest open-weight models that fit on this card, newest first. Each is shown at the best compression the card can hold.
The fastest AI models on a Radeon RX Vega 56 Mobile
Where this card produces tokens quickest. Smaller models dominate here, because generating each token means reading the whole model out of memory once.
Step by step
How to work out the tokens per second of a Radeon RX Vega 56 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.
-
01
Find the model in the table
The table lists 351 models this card can run. Search by name, or by size — typing 27b matches on the parameter count even when the name never states it.
-
02
Decide how long your conversations run
Longer conversations cost memory on top of the weights. Against 8 GB so the setting is worth getting right.
-
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.
-
04
Take the range as the answer
Speeds come with error bars for a reason. The best case here is 135 tok/s on Gemma 3 QAT 1B. The same card and model vary by thirty to fifty per cent between inference runtimes.
-
05
Read the fit verdict last
A tight fit runs but leaves no room to raise the context later; comfortable has headroom. The memory column shows what each model needs against an available 8 GB.
-
06
Open the model to compare cards
Following a model through to its own page lists all the hardware that can run it, so you can see how it compares against Radeon RX Vega 56 Mobile.
Answers
Radeon RX Vega 56 Mobile — common questions
Radeon RX Vega 56 Mobile— what is its memory bandwidth?
Memory bandwidth reaches 410 GB/s across a bus of 2,048 bits. 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.
Radeon RX Vega 56 Mobile— what type of memory does it use?
It uses HBM2 clocked at 800 MHz. 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.
Radeon RX Vega 56 Mobile— who makes it?
This is a product of AMD, with the chip manufactured by GlobalFoundries, on a process of 14 nm.
Radeon RX Vega 56 Mobile— when was it released?
It was released in June 2018.
Radeon RX Vega 56 Mobile— how much power does it use?
Rated board power is 120 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.
Radeon RX Vega 56 Mobile— how much cache does it have?
The L1 cache is 16 KB, and the L2 cache is 4 MB. 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.
Radeon RX Vega 56 Mobile— what are its TFLOPS?
It is rated at 18.7 TFLOPS at half precision and 9.3 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.
Radeon RX Vega 56 Mobile— does it support CUDA?
No. CUDA is NVIDIA-only, and this is a card from AMD. 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.
Radeon RX Vega 56 Mobile— what bus interface does it use?
It uses PCIe 3.0 x16. 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.
Radeon RX Vega 56 Mobile— is it good for running local AI models?
Its memory limits it to smaller models and its bandwidth gives usable, if unspectacular, generation speeds. In total it runs 351 of the models we track. Whether that is enough depends entirely on which model you want — the table above answers that directly.
Radeon RX Vega 56 Mobile— can it run a model that does not fit in its memory?
Offloading past the card's 8 GB drags the whole thing down, and none of the figures on this page assume it.
Would two Radeon RX Vega 56 Mobile cards be twice as fast?
No. A second card doubles the memory to 16 GB which lets you hold models neither could hold alone, but generation does not split that way. These figures describe one card.
Radeon RX Vega 56 Mobile— which AI models can it run?
351 of the 721 open-weight language models we track fit on this card and can be run locally. The table on this page lists every one, with the memory it needs, the quantisation it runs at and an estimated generation speed.
Radeon RX Vega 56 Mobile— what is the largest AI model it can run?
The largest model in our catalogue that fits is Baichuan 1-13B at 13.3B parameters, compressed to Q3_K_M. It generates roughly 27.5 tokens per second and needs about 7.2 GB of the card's memory.
Radeon RX Vega 56 Mobile— how many tokens per second does it produce?
It depends on the model. The fastest model we track here is Gemma 3 QAT 1B at about 135 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.
Radeon RX Vega 56 Mobile— can it run 7B models?
Yes. For example it runs Gemma 4 E4B at Q5_K_M, using about 7.0 GB of memory and generating around 53.7 tokens per second.
Radeon RX Vega 56 Mobile— can it run 13B models?
Yes. For example it runs Gemma 4 12B at Q3_K_M, using about 6.5 GB of memory and generating around 30.6 tokens per second.
Radeon RX Vega 56 Mobile— how much memory does it have?
This card has 8 GB of HBM2. Around a tenth is reserved by the inference runtime and the driver, leaving roughly 7.2 GB available for a model and its conversation.
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.