Calculate the TPS of the Radeon Pro W6800X 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
679 models in our catalogue altogether
Largest model it holds
Phi-3.5-MoE
60.8B · Q3_K_M · 41.7 tok/s
Fastest model
Gemma 3 QAT 1B
169 tok/s · 1B
Which AI models can run on a Radeon Pro W6800X?
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.
513 models match
Calculating| Quantisation | Fit | ||||||
|---|---|---|---|---|---|---|---|
|
169
tok/s
101–271 · low confidence |
Gemma 3 1B | 1B | Mar 2025 | 1.8 GB | 33k tokens | Q8_0 | Comfortable |
|
169
tok/s
101–271 · low confidence |
Gemma 3 QAT 1B | 1B | Apr 2025 | 1.8 GB | 33k tokens | Q8_0 | Comfortable |
|
169
tok/s
101–271 · low confidence |
HGRN 1B (WT 103) ≈ | 1B | Nov 2023 | 1.8 GB | 131k tokens ? | Q8_0 | Comfortable |
|
169
tok/s
101–271 · low confidence |
LLama 3..2 Typhoon 2 1B ≈ | 1B | Dec 2024 | 1.8 GB | 131k tokens ? | Q8_0 | Comfortable |
|
169
tok/s
101–271 · low confidence |
OLMo-1B ≈ | 1B | Feb 2024 | 1.8 GB | 131k tokens ? | Q8_0 | Comfortable |
|
169
tok/s
101–271 · low confidence |
Pythia-1b ≈ | 1B | Apr 2023 | 1.8 GB | 131k tokens ? | Q8_0 | Comfortable |
|
157
tok/s
94–251 · low confidence |
OpenELM-1.1B ≈ | 1.1B | May 2024 | 1.9 GB | 131k tokens ? | Q8_0 | Comfortable |
|
154
tok/s
92–246 · low confidence |
DeciCoder-1B ≈ | 1.1B | Aug 2023 | 1.9 GB | 131k tokens ? | Q8_0 | Comfortable |
|
154
tok/s
92–246 · low confidence |
SantaCoder ≈ | 1.1B | Jan 2023 | 1.9 GB | 131k tokens ? | Q8_0 | Comfortable |
|
154
tok/s
92–246 · low confidence |
TinyLlama-1.1B (1T token checkpoint) ≈ | 1.1B | Oct 2023 | 1.9 GB | 131k tokens ? | Q8_0 | Comfortable |
|
154
tok/s
92–246 · low confidence |
TinyLlama-1.1B (3T token checkpoint) ≈ | 1.1B | Oct 2023 | 1.9 GB | 131k tokens ? | Q8_0 | Comfortable |
|
141
tok/s
85–226 · low confidence |
EXAONE 4.0 (1.2B) ≈ | 1.2B | Jul 2025 | 2.0 GB | 131k tokens ? | Q8_0 | Comfortable |
|
141
tok/s
85–226 · low confidence |
MinerU2.5 ≈ | 1.2B | Sep 2025 | 2.0 GB | 131k tokens ? | Q8_0 | Comfortable |
|
141
tok/s
85–226 · low confidence |
Pleias 1.0 1.2B ≈ | 1.2B | Dec 2024 | 2.0 GB | 131k tokens ? | Q8_0 | Comfortable |
|
141
tok/s
85–226 · low confidence |
Pleias-RAG-1B ≈ | 1.2B | Apr 2025 | 2.0 GB | 131k tokens ? | Q8_0 | Comfortable |
|
138
tok/s
83–220 · low confidence |
Llama 3.2 1B | 1.2B | Sep 2024 | 2.2 GB | 131k tokens | Q8_0 | Comfortable |
|
136
tok/s
81–217 · low confidence |
MiniCPM-1.2B ≈ | 1.2B | Jun 2024 | 2.0 GB | 131k tokens ? | Q8_0 | Comfortable |
|
130
tok/s
78–208 · low confidence |
DeepSeek Coder 1.3B ≈ | 1.3B | Jan 2024 | 2.1 GB | 131k tokens ? | Q8_0 | Comfortable |
|
130
tok/s
78–208 · low confidence |
DeepSeek-VL-1.3B ≈ | 1.3B | Mar 2024 | 2.1 GB | 131k tokens ? | Q8_0 | Comfortable |
|
130
tok/s
78–208 · low confidence |
DigiRL ≈ | 1.3B | Jun 2024 | 2.1 GB | 131k tokens ? | Q8_0 | Comfortable |
|
130
tok/s
78–208 · low confidence |
GLA Transformer 1.3B ≈ | 1.3B | Aug 2024 | 2.1 GB | 131k tokens ? | Q8_0 | Comfortable |
|
130
tok/s
78–208 · low confidence |
Janus 1.3B ≈ | 1.3B | Oct 2024 | 2.1 GB | 131k tokens ? | Q8_0 | Comfortable |
|
130
tok/s
78–208 · low confidence |
Kosmos-2.5 ≈ | 1.3B | Aug 2024 | 2.1 GB | 131k tokens ? | Q8_0 | Comfortable |
|
130
tok/s
78–208 · low confidence |
Otter ≈ | 1.3B | May 2023 | 2.1 GB | 131k tokens ? | Q8_0 | Comfortable |
|
130
tok/s
78–208 · 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 Pro W6800X 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
- 32 GB
- Memory bandwidth
- 512 GB/s
- Memory type
- GDDR6
- Memory bus width
- 256 bit
- Memory clock
- 2 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
- Navi 21
- Architecture
- RDNA 2.0
- Generation
- Radeon Pro Mac(Navi II Series)
- Foundry
- TSMC
- Process size
- 7 nm
- Transistors
- 26.8 billion
- Transistor density
- 51,500 K/mm²
- Die size
- 520 mm²
- Package
- BGA-2425
- Released
- 3 August 2021
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
- 1.8 GHz
- Boost clock
- 2.09 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
- 3,840
- Texture mapping units
- 240
- Render output units
- 96
- Ray tracing cores
- 60
- L1 cache
- 128 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)
- 32.1 TFLOPS
- Single precision (FP32)
- 16 TFLOPS
- Double precision (FP64)
- 1 TFLOPS
- Pixel rate
- 200 GPixel/s
- Texture rate
- 501 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)
- 200 W
- Suggested power supply
- 550 W
- Power connectors
- Apple MPX
- Bus interface
- Apple MPX
- Slot width
- Quad-slot
- Dimensions
- 267 mm
- Display outputs
- 1x HDMI 2.1, 4x Thunderbolt
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
- 12.2
- OpenGL
- 4.6
- Vulkan
- 1.4
- OpenCL
- 2.1
- Shader model
- 6.8
Listings
Where to buy a Radeon Pro W6800X
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
What the memory subsystem means for AI
Memory
32 GB
Bandwidth
512 GB/s
Largest model
Phi-3.5-MoE
Radeon Pro W6800X carries 32 GB of GDDR6. That covers the mid-sized models most people actually run. Once the runtime and driver reserve their working space, roughly this much is left: 28.8 GB.
Memory bandwidth reaches 512 GB/s across a bus of 256 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 2 GHz. Widening the bus and raising the clock are the two levers a manufacturer has, which is why a card with unremarkable cores can still generate quickly.
In practice that combination tops out at Phi-3.5-MoE, 60.8B, compressed to Q3_K_M and generating around 41.7 tokens per second.
The chip and how it was built
Radeon Pro W6800X is built on the graphics processor Navi 21, using the architecture RDNA 2.0 from AMD, as part of the generation Radeon Pro Mac(Navi II Series).
The chip is manufactured by TSMC, on a process of 7 nm, with a die measuring 520 mm², holding 26.8 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 August 2021, roughly 4.9943656924399 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
32.1 TFLOPS
FP64
1 TFLOPS
On paper Radeon Pro W6800X reaches 32.1 TFLOPS at half precision, and 16 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 1 TFLOPS. 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 1.8 GHz to a boost of 2.09 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 Pro W6800X has an L1 cache of 128 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 3,840 shading units, 240 texture mapping units, and 96 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
200 W
Radeon Pro W6800X is rated at 200 W, and the suggested system power supply is 550 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 quad-slot, measuring 267 mm long, and needs Apple MPX. 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 Apple MPX. 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 Pro W6800X
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 Pro W6800X
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 Pro W6800X
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 513 models the card handles. The search box takes a name or a size such as 27b, which matches on parameter count.
-
02
Match the context to your work
Set the context to your real working length. Short questions cost almost nothing, but a long document can consume a large share of 32 GB that is frequently the difference between a model fitting and not.
-
03
Pin the comparison to one quality level
Each model is shown at the best compression this card can hold. A minimum quality hides the ones that only fit by being squeezed further than you would accept.
-
04
Read the speed and the range
The figures are calculated, not measured. The fastest result on this card is 169 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
Tight means it works today; comfortable means it still works when the conversation grows. Compare what each model needs against an available 32 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 Pro W6800X.
Answers
Radeon Pro W6800X — common questions
Would two Radeon Pro W6800X cards be twice as fast?
Pairing them buys headroom rather than pace: 64 GB to work with rather than twice the tokens per second — every figure here is for a single card.
Radeon Pro W6800X— which AI models can it run?
513 of the 679 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 Pro W6800X— what is the largest AI model it can run?
The largest model in our catalogue that fits is Phi-3.5-MoE at 60.8B parameters, compressed to Q3_K_M. It generates roughly 41.7 tokens per second and needs about 27.2 GB of the card's memory.
Radeon Pro W6800X— 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 169 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 Pro W6800X— can it run 7B models?
Yes. For example it runs Multi-Token Prediction 7B at Q8_0, using about 7.9 GB of memory and generating around 25.2 tokens per second.
Radeon Pro W6800X— can it run 13B models?
Yes. For example it runs DeepSeekMoE-16B at Q8_0, using about 17.5 GB of memory and generating around 58.7 tokens per second.
Radeon Pro W6800X— can it run 30B models?
Yes. For example it runs ERNIE-4.5-VL-28B-A3B at Q6_K, using about 22.7 GB of memory and generating around 48.8 tokens per second.
Radeon Pro W6800X— how much memory does it have?
This card has 32 GB of GDDR6. Around a tenth is reserved by the inference runtime and the driver, leaving roughly 28.8 GB available for a model and its conversation.
Radeon Pro W6800X— what is its memory bandwidth?
Memory bandwidth reaches 512 GB/s across a bus of 256 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 Pro W6800X— what type of memory does it use?
It uses GDDR6 clocked at 2 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.
Radeon Pro W6800X— who makes it?
This is a product of AMD, with the chip manufactured by TSMC, on a process of 7 nm.
Radeon Pro W6800X— when was it released?
It was released in August 2021.
Radeon Pro W6800X— how much power does it use?
Rated board power is 200 W, and the suggested system power supply is 550 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 Pro W6800X— how much cache does it have?
The L1 cache is 128 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 Pro W6800X— what are its TFLOPS?
It is rated at 32.1 TFLOPS at half precision and 16 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 Pro W6800X— 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 Pro W6800X— what bus interface does it use?
It uses Apple MPX. 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 Pro W6800X— is it good for running local AI models?
Its memory comfortably covers the mid-sized models most people run locally and its bandwidth gives usable, if unspectacular, generation speeds. In total it runs 513 of the models we track. Whether that is enough depends entirely on which model you want — the table above answers that directly.
Radeon Pro W6800X— can it run a model that does not fit in its memory?
Only partly. Layers beyond the card's 32 GB sit in system memory and run at a fraction of the speed, so a mostly-offloaded model is rarely worth using. Every figure here assumes it is fully resident on the card.
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.