Calculate the TPS of the Quadro RTX 4000 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
Baichuan 1-13B
13.3B · Q3_K_M · 35.8 tok/s
Fastest model
Gemma 3 QAT 1B
176 tok/s · 1B
Which AI models can run on a Quadro RTX 4000?
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.
337 models match
Calculating| Quantisation | Fit | ||||||
|---|---|---|---|---|---|---|---|
|
176
tok/s
150–211 |
Gemma 3 1B | 1B | Mar 2025 | 1.8 GB | 33k tokens | Q8_0 | Comfortable |
|
176
tok/s
150–211 |
Gemma 3 QAT 1B | 1B | Apr 2025 | 1.8 GB | 33k tokens | Q8_0 | Comfortable |
|
176
tok/s
106–282 · low confidence |
HGRN 1B (WT 103) ≈ | 1B | Nov 2023 | 1.8 GB | 131k tokens ? | Q8_0 | Comfortable |
|
176
tok/s
106–282 · low confidence |
LLama 3..2 Typhoon 2 1B ≈ | 1B | Dec 2024 | 1.8 GB | 131k tokens ? | Q8_0 | Comfortable |
|
176
tok/s
106–282 · low confidence |
OLMo-1B ≈ | 1B | Feb 2024 | 1.8 GB | 131k tokens ? | Q8_0 | Comfortable |
|
176
tok/s
106–282 · low confidence |
Pythia-1b ≈ | 1B | Apr 2023 | 1.8 GB | 131k tokens ? | Q8_0 | Comfortable |
|
163
tok/s
98–261 · low confidence |
OpenELM-1.1B ≈ | 1.1B | May 2024 | 1.9 GB | 131k tokens ? | Q8_0 | Comfortable |
|
160
tok/s
96–256 · low confidence |
DeciCoder-1B ≈ | 1.1B | Aug 2023 | 1.9 GB | 131k tokens ? | Q8_0 | Comfortable |
|
160
tok/s
96–256 · low confidence |
SantaCoder ≈ | 1.1B | Jan 2023 | 1.9 GB | 131k tokens ? | Q8_0 | Comfortable |
|
160
tok/s
96–256 · low confidence |
TinyLlama-1.1B (1T token checkpoint) ≈ | 1.1B | Oct 2023 | 1.9 GB | 131k tokens ? | Q8_0 | Comfortable |
|
160
tok/s
96–256 · low confidence |
TinyLlama-1.1B (3T token checkpoint) ≈ | 1.1B | Oct 2023 | 1.9 GB | 131k tokens ? | Q8_0 | Comfortable |
|
147
tok/s
88–235 · low confidence |
EXAONE 4.0 (1.2B) ≈ | 1.2B | Jul 2025 | 2.0 GB | 131k tokens ? | Q8_0 | Comfortable |
|
147
tok/s
88–235 · low confidence |
MinerU2.5 ≈ | 1.2B | Sep 2025 | 2.0 GB | 131k tokens ? | Q8_0 | Comfortable |
|
147
tok/s
88–235 · low confidence |
Pleias 1.0 1.2B ≈ | 1.2B | Dec 2024 | 2.0 GB | 131k tokens ? | Q8_0 | Comfortable |
|
147
tok/s
88–235 · low confidence |
Pleias-RAG-1B ≈ | 1.2B | Apr 2025 | 2.0 GB | 131k tokens ? | Q8_0 | Comfortable |
|
143
tok/s
122–172 |
Llama 3.2 1B | 1.2B | Sep 2024 | 2.2 GB | 131k tokens | Q8_0 | Comfortable |
|
141
tok/s
85–226 · low confidence |
MiniCPM-1.2B ≈ | 1.2B | Jun 2024 | 2.0 GB | 131k tokens ? | Q8_0 | Comfortable |
|
136
tok/s
81–217 · low confidence |
DeepSeek Coder 1.3B ≈ | 1.3B | Jan 2024 | 2.1 GB | 131k tokens ? | Q8_0 | Comfortable |
|
136
tok/s
81–217 · low confidence |
DeepSeek-VL-1.3B ≈ | 1.3B | Mar 2024 | 2.1 GB | 131k tokens ? | Q8_0 | Comfortable |
|
136
tok/s
81–217 · low confidence |
DigiRL ≈ | 1.3B | Jun 2024 | 2.1 GB | 131k tokens ? | Q8_0 | Comfortable |
|
136
tok/s
81–217 · low confidence |
GLA Transformer 1.3B ≈ | 1.3B | Aug 2024 | 2.1 GB | 131k tokens ? | Q8_0 | Comfortable |
|
136
tok/s
81–217 · low confidence |
Janus 1.3B ≈ | 1.3B | Oct 2024 | 2.1 GB | 131k tokens ? | Q8_0 | Comfortable |
|
136
tok/s
81–217 · low confidence |
Kosmos-2.5 ≈ | 1.3B | Aug 2024 | 2.1 GB | 131k tokens ? | Q8_0 | Comfortable |
|
136
tok/s
81–217 · low confidence |
Otter ≈ | 1.3B | May 2023 | 2.1 GB | 131k tokens ? | Q8_0 | Comfortable |
|
136
tok/s
81–217 · 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
Quadro RTX 4000 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
- 416 GB/s
- Memory type
- GDDR6
- Memory bus width
- 256 bit
- Memory clock
- 1.63 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
- TU104
- Architecture
- Turing
- Generation
- Quadro Turing(Tx000)
- Foundry
- TSMC
- Process size
- 12 nm
- Transistors
- 13.6 billion
- Transistor density
- 25,000 K/mm²
- Die size
- 545 mm²
- Package
- BGA-2228
- Released
- 13 November 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
- 1.01 GHz
- Boost clock
- 1.55 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
- 2,304
- Texture mapping units
- 144
- Render output units
- 64
- Streaming multiprocessors
- 36
- Tensor cores
- 288
- Ray tracing cores
- 36
- L1 cache
- 64 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)
- 14.2 TFLOPS
- Single precision (FP32)
- 7.1 TFLOPS
- Double precision (FP64)
- 222.5 GFLOPS
- Pixel rate
- 99 GPixel/s
- Texture rate
- 223 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)
- 160 W
- Suggested power supply
- 450 W
- Power connectors
- 1x 8-pin
- Bus interface
- PCIe 3.0 x16
- Slot width
- Single-slot
- Dimensions
- 241 mm
- Display outputs
- 3x DisplayPort 1.4a, 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.
- CUDA compute capability
- 7.5
- DirectX
- 12.2
- OpenGL
- 4.6
- Vulkan
- 1.4
- OpenCL
- 3.0
- Shader model
- 6.8
Listings
Where to buy a Quadro RTX 4000
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
8 GB
Bandwidth
416 GB/s
Largest model
Baichuan 1-13B
At 8 GB of GDDR6 the Quadro RTX 4000 is limited to the smaller end of the catalogue. About 7.2 GB is actually available to a runtime, and a model has to fit entirely inside it before generating anything at all.
The memory bus moves 416 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.
That comes from a 1.63 GHz memory clock across the bus width above. 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 Baichuan 1-13B — 13.3B, compressed to Q3_K_M, generating around 35.8 tokens per second.
The chip and how it was built
The Quadro RTX 4000 is built on the TU104 graphics processor, using NVIDIA's Turing architecture, as part of the Quadro Turing(Tx000) generation.
The chip is manufactured by TSMC, on a 12 nm process, with a die measuring 545 mm², holding 13.6 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 2018, roughly 7 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
14.2 TFLOPS
FP64
222.5 GFLOPS
Tensor cores
288
On paper the Quadro RTX 4000 reaches 14.2 TFLOPS at half precision and 7.1 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 222.5 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 288 tensor cores across 36 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 1.01 GHz at base to 1.55 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 Quadro RTX 4000 has 64 KB of L1 cache, backed by 4 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 2,304 shading units, 144 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
160 W
The Quadro RTX 4000 is rated at 160 W, with a 450 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 single-slot, measuring 241 mm long, and needs 1x 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 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 Quadro RTX 4000
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 Quadro RTX 4000
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 Quadro RTX 4000
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
Every one of the 337 models this Quadro RTX 4000 runs is in the table above. Search narrows it by name or by size.
-
02
Set the context length you will actually use
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 8 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
Take the range as the answer
Speeds come with error bars for a reason. The best case here is 176 tok/s on Gemma 3 QAT 1B, and which inference software you use moves that by thirty to fifty per cent.
-
05
Read the fit verdict last
The fit column separates models that just fit from those with room to spare — worth checking against the card's 8 GB before settling on one.
-
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 Quadro RTX 4000 is the right buy for it or merely a card that fits.
Answers
Quadro RTX 4000 — common questions
What AI models can a Quadro RTX 4000 run?
337 of the 679 open-weight language models we track fit on a Quadro RTX 4000 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 Quadro RTX 4000 can run?
The largest model in our catalogue that fits on a Quadro RTX 4000 is Baichuan 1-13B at 13.3B parameters, compressed to Q3_K_M. It generates roughly 35.8 tokens per second and needs about 7.2 GB of the card's memory.
How many tokens per second does a Quadro RTX 4000 produce?
It depends on the model. On a Quadro RTX 4000 the fastest model we track is Gemma 3 QAT 1B at about 176 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 Quadro RTX 4000 run a 7B model?
Yes. For example a Quadro RTX 4000 runs MetaMath 7B (LLaMa finetune) at Q4_K_M, using about 6.5 GB of memory and generating around 58.1 tokens per second.
Can a Quadro RTX 4000 run a 13B model?
Yes. For example a Quadro RTX 4000 runs Gemma 4 12B at Q3_K_M, using about 6.5 GB of memory and generating around 39.8 tokens per second.
How much memory does a Quadro RTX 4000 have?
A Quadro RTX 4000 has 8 GB of GDDR6 memory. Around a tenth of that is reserved by the inference runtime and the driver, leaving roughly 7.2 GB available for a model and its conversation.
What is the memory bandwidth of a Quadro RTX 4000?
The Quadro RTX 4000 has 416 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 Quadro RTX 4000 use?
It uses GDDR6 clocked at 1.63 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 Quadro RTX 4000?
The Quadro RTX 4000 is a NVIDIA product, with the chip manufactured by TSMC, on a 12 nm process.
When was the Quadro RTX 4000 released?
The Quadro RTX 4000 was released in November 2018.
How much power does a Quadro RTX 4000 use?
The Quadro RTX 4000 has a rated board power of 160 W, and a 450 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 Quadro RTX 4000 have?
The Quadro RTX 4000 has 64 KB of L1 cache, and 4 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 Quadro RTX 4000?
The Quadro RTX 4000 is rated at 14.2 TFLOPS at half precision and 7.1 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 Quadro RTX 4000 have?
The Quadro RTX 4000 has 288 tensor cores across 36 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 Quadro RTX 4000 support CUDA?
Yes. The Quadro RTX 4000 reports CUDA compute capability 7.5. 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.
What bus interface does the Quadro RTX 4000 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.
Is the Quadro RTX 4000 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 337 of the models we track. Whether that is enough depends entirely on which model you want — the table above answers that directly.
Can a Quadro RTX 4000 run a model that does not fit in its memory?
Only partly. Layers beyond the 8 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 Quadro RTX 4000 cards be twice as fast?
Pairing Quadro RTX 4000 cards buys headroom rather than pace: 16 GB of combined memory, at roughly the same generation speed as one.
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.