Calculate the TPS of the Radeon RX 7900 XTX 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
Largest model it holds
Mixtral 8x7B
46.7B · Q3_K_M · 66.3 tok/s
Fastest model
Gemma 3 QAT 1B
317 tok/s · 1B
What AI models can a Radeon RX 7900 XTX run?
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.
502 models match
Calculating| Quantisation | Fit | ||||||
|---|---|---|---|---|---|---|---|
|
317
tok/s
190–507 · low confidence |
Gemma 3 1B | 1B | Mar 2025 | 1.8 GB | 33k tokens | Q8_0 | Comfortable |
|
317
tok/s
190–507 · low confidence |
Gemma 3 QAT 1B | 1B | Apr 2025 | 1.8 GB | 33k tokens | Q8_0 | Comfortable |
|
317
tok/s
190–507 · low confidence |
HGRN 1B (WT 103) ≈ | 1B | Nov 2023 | 1.8 GB | 131k tokens ? | Q8_0 | Comfortable |
|
317
tok/s
190–507 · low confidence |
LLama 3..2 Typhoon 2 1B ≈ | 1B | Dec 2024 | 1.8 GB | 131k tokens ? | Q8_0 | Comfortable |
|
317
tok/s
190–507 · low confidence |
OLMo-1B ≈ | 1B | Feb 2024 | 1.8 GB | 131k tokens ? | Q8_0 | Comfortable |
|
317
tok/s
190–507 · low confidence |
Pythia-1b ≈ | 1B | Apr 2023 | 1.8 GB | 131k tokens ? | Q8_0 | Comfortable |
|
294
tok/s
176–470 · low confidence |
OpenELM-1.1B ≈ | 1.1B | May 2024 | 1.9 GB | 131k tokens ? | Q8_0 | Comfortable |
|
288
tok/s
173–461 · low confidence |
DeciCoder-1B ≈ | 1.1B | Aug 2023 | 1.9 GB | 131k tokens ? | Q8_0 | Comfortable |
|
288
tok/s
173–461 · low confidence |
SantaCoder ≈ | 1.1B | Jan 2023 | 1.9 GB | 131k tokens ? | Q8_0 | Comfortable |
|
288
tok/s
173–461 · low confidence |
TinyLlama-1.1B (1T token checkpoint) ≈ | 1.1B | Oct 2023 | 1.9 GB | 131k tokens ? | Q8_0 | Comfortable |
|
288
tok/s
173–461 · low confidence |
TinyLlama-1.1B (3T token checkpoint) ≈ | 1.1B | Oct 2023 | 1.9 GB | 131k tokens ? | Q8_0 | Comfortable |
|
264
tok/s
159–423 · low confidence |
EXAONE 4.0 (1.2B) ≈ | 1.2B | Jul 2025 | 2.0 GB | 131k tokens ? | Q8_0 | Comfortable |
|
264
tok/s
159–423 · low confidence |
MinerU2.5 ≈ | 1.2B | Sep 2025 | 2.0 GB | 131k tokens ? | Q8_0 | Comfortable |
|
264
tok/s
159–423 · low confidence |
Pleias 1.0 1.2B ≈ | 1.2B | Dec 2024 | 2.0 GB | 131k tokens ? | Q8_0 | Comfortable |
|
264
tok/s
159–423 · low confidence |
Pleias-RAG-1B ≈ | 1.2B | Apr 2025 | 2.0 GB | 131k tokens ? | Q8_0 | Comfortable |
|
258
tok/s
155–413 · low confidence |
Llama 3.2 1B | 1.2B | Sep 2024 | 2.2 GB | 131k tokens | Q8_0 | Comfortable |
|
254
tok/s
153–407 · low confidence |
MiniCPM-1.2B ≈ | 1.2B | Jun 2024 | 2.0 GB | 131k tokens ? | Q8_0 | Comfortable |
|
244
tok/s
146–390 · low confidence |
DeepSeek Coder 1.3B ≈ | 1.3B | Jan 2024 | 2.1 GB | 131k tokens ? | Q8_0 | Comfortable |
|
244
tok/s
146–390 · low confidence |
DeepSeek-VL-1.3B ≈ | 1.3B | Mar 2024 | 2.1 GB | 131k tokens ? | Q8_0 | Comfortable |
|
244
tok/s
146–390 · low confidence |
DigiRL ≈ | 1.3B | Jun 2024 | 2.1 GB | 131k tokens ? | Q8_0 | Comfortable |
|
244
tok/s
146–390 · low confidence |
GLA Transformer 1.3B ≈ | 1.3B | Aug 2024 | 2.1 GB | 131k tokens ? | Q8_0 | Comfortable |
|
244
tok/s
146–390 · low confidence |
Janus 1.3B ≈ | 1.3B | Oct 2024 | 2.1 GB | 131k tokens ? | Q8_0 | Comfortable |
|
244
tok/s
146–390 · low confidence |
Kosmos-2.5 ≈ | 1.3B | Aug 2024 | 2.1 GB | 131k tokens ? | Q8_0 | Comfortable |
|
244
tok/s
146–390 · low confidence |
Otter ≈ | 1.3B | May 2023 | 2.1 GB | 131k tokens ? | Q8_0 | Comfortable |
|
244
tok/s
146–390 · 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 7900 XTX 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
- 24 GB
- Memory bandwidth
- 960 GB/s
- Memory type
- GDDR6
- Memory bus width
- 384 bit
- Memory clock
- 2.5 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 31
- Architecture
- RDNA 3.0
- Generation
- Navi III(RX 7000)
- Foundry
- TSMC
- Process size
- 5 nm
- Transistors
- 57.7 billion
- Transistor density
- 109,100 K/mm²
- Die size
- 529 mm²
- Package
- MCM
- Released
- 3 November 2022
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
- 1.93 GHz
- Boost clock
- 2.5 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
- 6,144
- Texture mapping units
- 384
- Render output units
- 192
- Ray tracing cores
- 96
- L1 cache
- 250 KB
- L2 cache
- 6 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)
- 122.8 TFLOPS
- Single precision (FP32)
- 61.4 TFLOPS
- Double precision (FP64)
- 1.9 TFLOPS
- Pixel rate
- 480 GPixel/s
- Texture rate
- 959 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)
- 355 W
- Suggested power supply
- 750 W
- Power connectors
- 2x 8-pin
- Bus interface
- PCIe 4.0 x16
- Slot width
- Dual-slot
- Dimensions
- 287 mm × 51 mm
- Display outputs
- 1x HDMI 2.1a, 2x DisplayPort 2.1, 1x USB Type-C
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
- 2.2
- Shader model
- 6.8
Listings
Where to buy a Radeon RX 7900 XTX
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
24 GB
Bandwidth
960 GB/s
Largest model
Mixtral 8x7B
The Radeon RX 7900 XTX carries 24 GB of GDDR6, which covers the mid-sized models most people actually run — about 21.6 GB of it after the runtime and driver reserve their working space.
Bandwidth is 960 GB/s across a 384-bit bus. Generating a token means reading every weight once, so that figure sets the pace more than any other number here, and at this level text arrives faster than most people read.
Bandwidth is clock times bus width, and this card clocks its memory at 2.5 GHz. Both halves matter, and neither is visible in a gaming benchmark.
The practical ceiling is Mixtral 8x7B at 46.7B, held at Q3_K_M and running at roughly 66.3 tokens per second.
The chip and how it was built
The Radeon RX 7900 XTX is built on the Navi 31 graphics processor, using AMD's RDNA 3.0 architecture, as part of the Navi III(RX 7000) generation.
The chip is manufactured by TSMC, on a 5 nm process, with a die measuring 529 mm², holding 57.7 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 November 2022, roughly 3 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
122.8 TFLOPS
FP64
1.9 TFLOPS
On paper the Radeon RX 7900 XTX reaches 122.8 TFLOPS at half precision and 61.4 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 1.9 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 1.93 GHz at base to 2.5 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 7900 XTX has 250 KB of L1 cache, backed by 6 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 6,144 shading units, 384 texture mapping units, and 192 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
355 W
The Radeon RX 7900 XTX is rated at 355 W, with a 750 W power supply suggested for the whole system. 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 dual-slot, measuring 287 mm long, and needs 2x 8-pin. 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 4.0 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 a Radeon RX 7900 XTX can run
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 7900 XTX
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 7900 XTX
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
Start with the model, not the specification
All 502 models the Radeon RX 7900 XTX handles are already listed. The search box takes a name or a size such as 27b, which matches on parameter count.
-
02
Match the context to your work
Longer conversations cost memory on top of the weights. With 24 GB to work in, that is frequently the difference between a model fitting and not.
-
03
Choose how far you will compress
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
Take the range as the answer
Each speed is an estimate for a single conversation, with a range beneath it — 317 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.
-
05
Check the memory column before committing
Compare what each model needs with the 24 GB this card provides. Tight means it works today; comfortable means it still works when the conversation grows.
-
06
Cross-check against other hardware
Following a model through to its own page lists all the hardware that can run it, so you can see where the Radeon RX 7900 XTX sits against the alternatives.
Answers
Radeon RX 7900 XTX — common questions
Does the Radeon RX 7900 XTX support CUDA?
No. CUDA is NVIDIA-only, and the Radeon RX 7900 XTX 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.
What bus interface does the Radeon RX 7900 XTX use?
It uses PCIe 4.0 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.
Is the Radeon RX 7900 XTX good for running local AI models?
Its memory comfortably covers the mid-sized models most people run locally and its bandwidth is high enough to generate text faster than most people read. In total it runs 502 of the models we track. Whether that is enough depends entirely on which model you want — the table above answers that directly.
Can a Radeon RX 7900 XTX run a model that does not fit in its memory?
Only partly. Layers beyond the 24 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.
Would two Radeon RX 7900 XTX cards be twice as fast?
Capacity adds, throughput does not. Two of them give you 48 GB to work with rather than twice the tokens per second — every figure here is for a single Radeon RX 7900 XTX.
What AI models can a Radeon RX 7900 XTX run?
502 of the 679 open-weight language models we track fit on a Radeon RX 7900 XTX 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.
What is the largest AI model a Radeon RX 7900 XTX can run?
The largest model in our catalogue that fits on a Radeon RX 7900 XTX is Mixtral 8x7B at 46.7B parameters, compressed to Q3_K_M. It generates roughly 66.3 tokens per second and needs about 21.0 GB of the card's memory.
How many tokens per second does a Radeon RX 7900 XTX produce?
It depends on the model. On a Radeon RX 7900 XTX the fastest model we track is Gemma 3 QAT 1B at about 317 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.
Can a Radeon RX 7900 XTX run a 7B model?
Yes. For example a Radeon RX 7900 XTX runs Multi-Token Prediction 7B at Q8_0, using about 7.9 GB of memory and generating around 47.3 tokens per second.
Can a Radeon RX 7900 XTX run a 13B model?
Yes. For example a Radeon RX 7900 XTX runs DeepSeekMoE-16B at Q8_0, using about 17.5 GB of memory and generating around 110 tokens per second.
Can a Radeon RX 7900 XTX run a 30B model?
Yes. For example a Radeon RX 7900 XTX runs Nemotron 3-Nano-30B-A3B at Q4_K_M, using about 18.1 GB of memory and generating around 129 tokens per second.
How much memory does a Radeon RX 7900 XTX have?
A Radeon RX 7900 XTX has 24 GB of GDDR6 memory. Around a tenth of that is reserved by the inference runtime and the driver, leaving roughly 21.6 GB available for a model and its conversation.
What is the memory bandwidth of a Radeon RX 7900 XTX?
The Radeon RX 7900 XTX has 960 GB/s of memory bandwidth, across a 384-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.
What type of memory does a Radeon RX 7900 XTX use?
It uses GDDR6 clocked at 2.5 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.
Who makes the Radeon RX 7900 XTX?
The Radeon RX 7900 XTX is a AMD product, with the chip manufactured by TSMC, on a 5 nm process.
When was the Radeon RX 7900 XTX released?
The Radeon RX 7900 XTX was released in November 2022.
How much power does a Radeon RX 7900 XTX use?
The Radeon RX 7900 XTX has a rated board power of 355 W, and a 750 W system power supply is suggested. Generating text draws hard in bursts and idles between requests, so average consumption over a working session is normally well below the rated figure.
How much cache does a Radeon RX 7900 XTX have?
The Radeon RX 7900 XTX has 250 KB of L1 cache, and 6 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.
What are the TFLOPS of a Radeon RX 7900 XTX?
The Radeon RX 7900 XTX is rated at 122.8 TFLOPS at half precision and 61.4 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.
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.