Calculate the TPS of the GeForce RTX 4080 SUPER 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
Nemotron 3-Nano-30B-A3B
31.6B · Q3_K_M · 148 tok/s
Fastest model
Gemma 3 QAT 1B
312 tok/s · 1B
What AI models can a GeForce RTX 4080 SUPER 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.
432 models match
Calculating| Quantisation | Fit | ||||||
|---|---|---|---|---|---|---|---|
|
312
tok/s
265–374 |
Gemma 3 1B | 1B | Mar 2025 | 1.8 GB | 33k tokens | Q8_0 | Comfortable |
|
312
tok/s
265–374 |
Gemma 3 QAT 1B | 1B | Apr 2025 | 1.8 GB | 33k tokens | Q8_0 | Comfortable |
|
312
tok/s
187–499 · low confidence |
HGRN 1B (WT 103) ≈ | 1B | Nov 2023 | 1.8 GB | 131k tokens ? | Q8_0 | Comfortable |
|
312
tok/s
187–499 · low confidence |
LLama 3..2 Typhoon 2 1B ≈ | 1B | Dec 2024 | 1.8 GB | 131k tokens ? | Q8_0 | Comfortable |
|
312
tok/s
187–499 · low confidence |
OLMo-1B ≈ | 1B | Feb 2024 | 1.8 GB | 131k tokens ? | Q8_0 | Comfortable |
|
312
tok/s
187–499 · low confidence |
Pythia-1b ≈ | 1B | Apr 2023 | 1.8 GB | 131k tokens ? | Q8_0 | Comfortable |
|
289
tok/s
173–462 · low confidence |
OpenELM-1.1B ≈ | 1.1B | May 2024 | 1.9 GB | 131k tokens ? | Q8_0 | Comfortable |
|
284
tok/s
170–454 · low confidence |
DeciCoder-1B ≈ | 1.1B | Aug 2023 | 1.9 GB | 131k tokens ? | Q8_0 | Comfortable |
|
284
tok/s
170–454 · low confidence |
SantaCoder ≈ | 1.1B | Jan 2023 | 1.9 GB | 131k tokens ? | Q8_0 | Comfortable |
|
284
tok/s
170–454 · low confidence |
TinyLlama-1.1B (1T token checkpoint) ≈ | 1.1B | Oct 2023 | 1.9 GB | 131k tokens ? | Q8_0 | Comfortable |
|
284
tok/s
170–454 · low confidence |
TinyLlama-1.1B (3T token checkpoint) ≈ | 1.1B | Oct 2023 | 1.9 GB | 131k tokens ? | Q8_0 | Comfortable |
|
260
tok/s
156–416 · low confidence |
EXAONE 4.0 (1.2B) ≈ | 1.2B | Jul 2025 | 2.0 GB | 131k tokens ? | Q8_0 | Comfortable |
|
260
tok/s
156–416 · low confidence |
MinerU2.5 ≈ | 1.2B | Sep 2025 | 2.0 GB | 131k tokens ? | Q8_0 | Comfortable |
|
260
tok/s
156–416 · low confidence |
Pleias 1.0 1.2B ≈ | 1.2B | Dec 2024 | 2.0 GB | 131k tokens ? | Q8_0 | Comfortable |
|
260
tok/s
156–416 · low confidence |
Pleias-RAG-1B ≈ | 1.2B | Apr 2025 | 2.0 GB | 131k tokens ? | Q8_0 | Comfortable |
|
254
tok/s
216–304 |
Llama 3.2 1B | 1.2B | Sep 2024 | 2.2 GB | 131k tokens | Q8_0 | Comfortable |
|
250
tok/s
150–400 · low confidence |
MiniCPM-1.2B ≈ | 1.2B | Jun 2024 | 2.0 GB | 131k tokens ? | Q8_0 | Comfortable |
|
240
tok/s
144–384 · low confidence |
DeepSeek Coder 1.3B ≈ | 1.3B | Jan 2024 | 2.1 GB | 131k tokens ? | Q8_0 | Comfortable |
|
240
tok/s
144–384 · low confidence |
DeepSeek-VL-1.3B ≈ | 1.3B | Mar 2024 | 2.1 GB | 131k tokens ? | Q8_0 | Comfortable |
|
240
tok/s
144–384 · low confidence |
DigiRL ≈ | 1.3B | Jun 2024 | 2.1 GB | 131k tokens ? | Q8_0 | Comfortable |
|
240
tok/s
144–384 · low confidence |
GLA Transformer 1.3B ≈ | 1.3B | Aug 2024 | 2.1 GB | 131k tokens ? | Q8_0 | Comfortable |
|
240
tok/s
144–384 · low confidence |
Janus 1.3B ≈ | 1.3B | Oct 2024 | 2.1 GB | 131k tokens ? | Q8_0 | Comfortable |
|
240
tok/s
144–384 · low confidence |
Kosmos-2.5 ≈ | 1.3B | Aug 2024 | 2.1 GB | 131k tokens ? | Q8_0 | Comfortable |
|
240
tok/s
144–384 · low confidence |
Otter ≈ | 1.3B | May 2023 | 2.1 GB | 131k tokens ? | Q8_0 | Comfortable |
|
240
tok/s
144–384 · 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
GeForce RTX 4080 SUPER 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
- 16 GB
- Memory bandwidth
- 736 GB/s
- Memory type
- GDDR6X
- Memory bus width
- 256 bit
- Memory clock
- 1.44 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
- AD103
- Architecture
- Ada Lovelace
- Generation
- GeForce 40
- Foundry
- TSMC
- Process size
- 5 nm
- Transistors
- 45.9 billion
- Transistor density
- 121,100 K/mm²
- Die size
- 379 mm²
- Released
- 8 January 2024
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
- 2.3 GHz
- Boost clock
- 2.55 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
- 10,240
- Texture mapping units
- 320
- Render output units
- 112
- Streaming multiprocessors
- 80
- Tensor cores
- 320
- Ray tracing cores
- 80
- L1 cache
- 128 KB
- L2 cache
- 64 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)
- 52.2 TFLOPS
- Single precision (FP32)
- 52.2 TFLOPS
- Double precision (FP64)
- 816 GFLOPS
- Pixel rate
- 286 GPixel/s
- Texture rate
- 816 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)
- 320 W
- Suggested power supply
- 700 W
- Power connectors
- 1x 16-pin
- Bus interface
- PCIe 4.0 x16
- Slot width
- Triple-slot
- Dimensions
- 310 mm × 61 mm
- Display outputs
- 1x HDMI 2.1, 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.
- CUDA compute capability
- 8.9
- DirectX
- 12.2
- OpenGL
- 4.6
- Vulkan
- 1.4
- OpenCL
- 3.0
- Shader model
- 6.8
Listings
Where to buy a GeForce RTX 4080 SUPER
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
16 GB
Bandwidth
736 GB/s
Largest model
Nemotron 3-Nano-30B-A3B
16 GB of GDDR6X puts the GeForce RTX 4080 SUPER comfortably into small and mid-sized models, with roughly 14.4 GB usable once the driver overhead is taken out. The largest models are out of reach without splitting them.
The memory bus moves 736 GB/s across a 256-bit bus. That is the number that governs generation speed — arithmetic per byte read is small enough that the bus, not the cores, is what everything waits on.
The figure is the memory clock — 1.44 GHz here — multiplied by the bus width. It is why core counts predict generation speed so poorly.
In practice that combination tops out at Nemotron 3-Nano-30B-A3B — 31.6B, compressed to Q3_K_M, generating around 148 tokens per second.
The chip and how it was built
The GeForce RTX 4080 SUPER is built on the AD103 graphics processor, using NVIDIA's Ada Lovelace architecture, as part of the GeForce 40 generation.
The chip is manufactured by TSMC, on a 5 nm process, with a die measuring 379 mm², holding 45.9 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 January 2024, roughly 2 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
52.2 TFLOPS
FP64
816 GFLOPS
Tensor cores
320
On paper the GeForce RTX 4080 SUPER reaches 52.2 TFLOPS at half precision and 52.2 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 816 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.
The card carries 320 tensor cores across 80 streaming multiprocessors. These accelerate the matrix arithmetic at the heart of a transformer, and they are what make prompt processing — reading a long document before answering — dramatically faster than it would otherwise be.
Clocks run from 2.3 GHz at base to 2.55 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 GeForce RTX 4080 SUPER has 128 KB of L1 cache, backed by 64 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 10,240 shading units, 320 texture mapping units, and 112 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
320 W
The GeForce RTX 4080 SUPER is rated at 320 W, with a 700 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 triple-slot, measuring 310 mm long, and needs 1x 16-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 GeForce RTX 4080 SUPER 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 GeForce RTX 4080 SUPER
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 GeForce RTX 4080 SUPER
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
Search for the model you want
All 432 models the GeForce RTX 4080 SUPER handles are already listed. The search box takes a name or a size such as 27b, which matches on parameter count.
-
02
Decide how long your conversations run
Set the context to your real working length. Short questions cost almost nothing; a long document can consume a large share of the card's 16 GB.
-
03
Pin the comparison to one quality level
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
Look at the range, not just the number
Speeds come with error bars for a reason. The best case here is 312 tok/s on Gemma 3 QAT 1B, and which inference software you use moves that by thirty to fifty per cent.
-
05
Check the headroom before you decide
The fit column separates models that just fit from those with room to spare — worth checking against the card's 16 GB before settling on one.
-
06
Check the same model from the other side
Each model page repeats this calculation for the whole catalogue. Worth a look before deciding: it shows what else runs the same model, and how the GeForce RTX 4080 SUPER compares.
Answers
GeForce RTX 4080 SUPER — common questions
What bus interface does the GeForce RTX 4080 SUPER 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 GeForce RTX 4080 SUPER good for running local AI models?
Its memory covers small and mid-sized models, though the largest are out of reach and its bandwidth gives usable, if unspectacular, generation speeds. In total it runs 432 of the models we track. Whether that is enough depends entirely on which model you want — the table above answers that directly.
Can a GeForce RTX 4080 SUPER run a model that does not fit in its memory?
Offloading past the card's 16 GB is possible and usually a false economy: the system-memory portion is slow enough to dominate the result.
Would two GeForce RTX 4080 SUPER cards be twice as fast?
Capacity adds, throughput does not. Two of them give you 32 GB to work with rather than twice the tokens per second — every figure here is for a single GeForce RTX 4080 SUPER.
What AI models can a GeForce RTX 4080 SUPER run?
432 of the 679 open-weight language models we track fit on a GeForce RTX 4080 SUPER 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 GeForce RTX 4080 SUPER can run?
The largest model in our catalogue that fits on a GeForce RTX 4080 SUPER is Nemotron 3-Nano-30B-A3B at 31.6B parameters, compressed to Q3_K_M. It generates roughly 148 tokens per second and needs about 14.4 GB of the card's memory.
How many tokens per second does a GeForce RTX 4080 SUPER produce?
It depends on the model. On a GeForce RTX 4080 SUPER the fastest model we track is Gemma 3 QAT 1B at about 312 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 GeForce RTX 4080 SUPER run a 7B model?
Yes. For example a GeForce RTX 4080 SUPER runs Multi-Token Prediction 7B at Q8_0, using about 7.9 GB of memory and generating around 46.5 tokens per second.
Can a GeForce RTX 4080 SUPER run a 13B model?
Yes. For example a GeForce RTX 4080 SUPER runs DeepSeekMoE-16B at Q6_K, using about 13.7 GB of memory and generating around 157 tokens per second.
Can a GeForce RTX 4080 SUPER run a 30B model?
Yes. For example a GeForce RTX 4080 SUPER runs ERNIE-4.5-VL-28B-A3B at Q3_K_M, using about 12.9 GB of memory and generating around 167 tokens per second.
How much memory does a GeForce RTX 4080 SUPER have?
A GeForce RTX 4080 SUPER has 16 GB of GDDR6X memory. Around a tenth of that is reserved by the inference runtime and the driver, leaving roughly 14.4 GB available for a model and its conversation.
What is the memory bandwidth of a GeForce RTX 4080 SUPER?
The GeForce RTX 4080 SUPER has 736 GB/s of memory bandwidth, across a 256-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 GeForce RTX 4080 SUPER use?
It uses GDDR6X clocked at 1.44 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 GeForce RTX 4080 SUPER?
The GeForce RTX 4080 SUPER is a NVIDIA product, with the chip manufactured by TSMC, on a 5 nm process.
When was the GeForce RTX 4080 SUPER released?
The GeForce RTX 4080 SUPER was released in January 2024.
How much power does a GeForce RTX 4080 SUPER use?
The GeForce RTX 4080 SUPER has a rated board power of 320 W, and a 700 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 GeForce RTX 4080 SUPER have?
The GeForce RTX 4080 SUPER has 128 KB of L1 cache, and 64 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 GeForce RTX 4080 SUPER?
The GeForce RTX 4080 SUPER is rated at 52.2 TFLOPS at half precision and 52.2 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.
How many tensor cores does a GeForce RTX 4080 SUPER have?
The GeForce RTX 4080 SUPER has 320 tensor cores across 80 streaming multiprocessors. They accelerate the matrix arithmetic a transformer is built from, which mainly speeds up processing a long prompt rather than producing the reply.
Does the GeForce RTX 4080 SUPER support CUDA?
Yes. The GeForce RTX 4080 SUPER reports CUDA compute capability 8.9. Capability 7.0 and above has tensor cores, which modern inference software uses; below that it falls back to slower code paths for quantised models.
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.