Calculate the TPS of the Quadro RTX 8000 Passive 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
Qwen3-Coder-Next
80B · IQ4_XS · 45.1 tok/s
Fastest model
Gemma 3 QAT 1B
264 tok/s · 1B
Which AI models can run on a Quadro RTX 8000 Passive?
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.
607 models match
Calculating| Quantisation | Fit | ||||||
|---|---|---|---|---|---|---|---|
|
264
tok/s
225–317 |
Gemma 3 1B | 1B | Mar 2025 | 1.8 GB | 33k tokens | Q8_0 | Comfortable |
|
264
tok/s
225–317 |
Gemma 3 QAT 1B | 1B | Apr 2025 | 1.8 GB | 33k tokens | Q8_0 | Comfortable |
|
264
tok/s
159–423 · low confidence |
HGRN 1B (WT 103) ≈ | 1B | Nov 2023 | 1.8 GB | 131k tokens ? | Q8_0 | Comfortable |
|
264
tok/s
159–423 · low confidence |
LLama 3..2 Typhoon 2 1B ≈ | 1B | Dec 2024 | 1.8 GB | 131k tokens ? | Q8_0 | Comfortable |
|
264
tok/s
159–423 · low confidence |
OLMo-1B ≈ | 1B | Feb 2024 | 1.8 GB | 131k tokens ? | Q8_0 | Comfortable |
|
264
tok/s
159–423 · low confidence |
Pythia-1b ≈ | 1B | Apr 2023 | 1.8 GB | 131k tokens ? | Q8_0 | Comfortable |
|
245
tok/s
147–392 · low confidence |
OpenELM-1.1B ≈ | 1.1B | May 2024 | 1.9 GB | 131k tokens ? | Q8_0 | Comfortable |
|
240
tok/s
144–384 · low confidence |
DeciCoder-1B ≈ | 1.1B | Aug 2023 | 1.9 GB | 131k tokens ? | Q8_0 | Comfortable |
|
240
tok/s
144–384 · low confidence |
SantaCoder ≈ | 1.1B | Jan 2023 | 1.9 GB | 131k tokens ? | Q8_0 | Comfortable |
|
240
tok/s
144–384 · low confidence |
TinyLlama-1.1B (1T token checkpoint) ≈ | 1.1B | Oct 2023 | 1.9 GB | 131k tokens ? | Q8_0 | Comfortable |
|
240
tok/s
144–384 · low confidence |
TinyLlama-1.1B (3T token checkpoint) ≈ | 1.1B | Oct 2023 | 1.9 GB | 131k tokens ? | Q8_0 | Comfortable |
|
220
tok/s
132–352 · low confidence |
EXAONE 4.0 (1.2B) ≈ | 1.2B | Jul 2025 | 2.0 GB | 131k tokens ? | Q8_0 | Comfortable |
|
220
tok/s
132–352 · low confidence |
LFM2-1.2B ≈ | 1.2B | Jul 2025 | 2.0 GB | 131k tokens ? | Q8_0 | Comfortable |
|
220
tok/s
132–352 · low confidence |
MinerU2.5 ≈ | 1.2B | Sep 2025 | 2.0 GB | 131k tokens ? | Q8_0 | Comfortable |
|
220
tok/s
132–352 · low confidence |
Pleias 1.0 1.2B ≈ | 1.2B | Dec 2024 | 2.0 GB | 131k tokens ? | Q8_0 | Comfortable |
|
220
tok/s
132–352 · low confidence |
Pleias-RAG-1B ≈ | 1.2B | Apr 2025 | 2.0 GB | 131k tokens ? | Q8_0 | Comfortable |
|
215
tok/s
183–258 |
Llama 3.2 1B | 1.2B | Sep 2024 | 2.2 GB | 131k tokens | Q8_0 | Comfortable |
|
212
tok/s
127–339 · low confidence |
MiniCPM-1.2B ≈ | 1.2B | Jun 2024 | 2.0 GB | 131k tokens ? | Q8_0 | Comfortable |
|
203
tok/s
122–325 · low confidence |
DeepSeek Coder 1.3B ≈ | 1.3B | Jan 2024 | 2.1 GB | 131k tokens ? | Q8_0 | Comfortable |
|
203
tok/s
122–325 · low confidence |
DeepSeek-VL-1.3B ≈ | 1.3B | Mar 2024 | 2.1 GB | 131k tokens ? | Q8_0 | Comfortable |
|
203
tok/s
122–325 · low confidence |
DigiRL ≈ | 1.3B | Jun 2024 | 2.1 GB | 131k tokens ? | Q8_0 | Comfortable |
|
203
tok/s
122–325 · low confidence |
GLA Transformer 1.3B ≈ | 1.3B | Aug 2024 | 2.1 GB | 131k tokens ? | Q8_0 | Comfortable |
|
203
tok/s
122–325 · low confidence |
Janus 1.3B ≈ | 1.3B | Oct 2024 | 2.1 GB | 131k tokens ? | Q8_0 | Comfortable |
|
203
tok/s
122–325 · low confidence |
Kosmos-2.5 ≈ | 1.3B | Aug 2024 | 2.1 GB | 131k tokens ? | Q8_0 | Comfortable |
|
203
tok/s
122–325 · 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
Quadro RTX 8000 Passive 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
- 48 GB
- Memory bandwidth
- 624 GB/s
- Memory type
- GDDR6
- Memory bus width
- 384 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
- TU102
- Architecture
- Turing
- Generation
- Quadro Turing(Tx000)
- Foundry
- TSMC
- Process size
- 12 nm
- Transistors
- 18.6 billion
- Transistor density
- 24,700 K/mm²
- Die size
- 754 mm²
- Released
- 13 August 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
- 1.23 GHz
- Boost clock
- 1.62 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
- 4,608
- Texture mapping units
- 288
- Render output units
- 96
- Streaming multiprocessors
- 72
- Tensor cores
- 576
- Ray tracing cores
- 72
- L1 cache
- 64 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)
- 29.9 TFLOPS
- Single precision (FP32)
- 14.9 TFLOPS
- Double precision (FP64)
- 466.6 GFLOPS
- Pixel rate
- 156 GPixel/s
- Texture rate
- 467 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)
- 260 W
- Suggested power supply
- 600 W
- Power connectors
- 1x 6-pin + 1x 8-pin
- Bus interface
- PCIe 3.0 x16
- Slot width
- Dual-slot
- Dimensions
- 267 mm
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 8000 Passive
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
48 GB
Bandwidth
624 GB/s
Largest model
Qwen3-Coder-Next
Quadro RTX 8000 Passive carries 48 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: 43.2 GB.
Memory bandwidth reaches 624 GB/s across a bus of 384 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.
Bandwidth is clock times bus width, and this card clocks its memory at 1.63 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.
Put together, the largest model that fits is Qwen3-Coder-Next, 80B, compressed to IQ4_XS and generating around 45.1 tokens per second.
The chip and how it was built
Quadro RTX 8000 Passive is built on the graphics processor TU102, using the architecture Turing from NVIDIA, as part of the generation Quadro Turing(Tx000).
The chip is manufactured by TSMC, on a process of 12 nm, with a die measuring 754 mm², holding 18.6 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 August 2018, roughly 8.0870337932937 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
29.9 TFLOPS
FP64
466.6 GFLOPS
Tensor cores
576
On paper Quadro RTX 8000 Passive reaches 29.9 TFLOPS at half precision, and 14.9 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 466.6 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 576 tensor cores across 72 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 a base of 1.23 GHz to a boost of 1.62 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
Quadro RTX 8000 Passive has an L1 cache of 64 KB, backed by an L2 cache of 6 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 4,608 shading units, 288 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
260 W
Quadro RTX 8000 Passive is rated at 260 W, and the suggested system power supply is 600 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 dual-slot, measuring 267 mm long, and needs 1x 6-pin + 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 8000 Passive
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 8000 Passive
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 8000 Passive
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
The table lists 607 models the card handles. The search box takes a name or a size such as 27b, which matches on parameter count.
-
02
Set the context length you will actually use
Set the context to your real working length. Short questions cost almost nothing, but a long document can consume a large share of 48 GB it is often what pushes a large model over the edge.
-
03
Set a minimum quality if you need one
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
Each speed is an estimate for a single conversation, with a range beneath it. The top end here is 264 tok/s on Gemma 3 QAT 1B. The same card and model vary by thirty to fifty per cent between inference runtimes.
-
05
Check the headroom before you decide
Tight means it works today; comfortable means it still works when the conversation grows. Compare what each model needs against an available 48 GB.
-
06
Cross-check against other hardware
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 right buy is Quadro RTX 8000 Passive.
Answers
Quadro RTX 8000 Passive — common questions
Quadro RTX 8000 Passive— who makes it?
This is a product of NVIDIA, with the chip manufactured by TSMC, on a process of 12 nm.
Quadro RTX 8000 Passive— when was it released?
It was released in August 2018.
Quadro RTX 8000 Passive— how much power does it use?
Rated board power is 260 W, and the suggested system power supply is 600 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.
Quadro RTX 8000 Passive— how much cache does it have?
The L1 cache is 64 KB, and the L2 cache is 6 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.
Quadro RTX 8000 Passive— what are its TFLOPS?
It is rated at 29.9 TFLOPS at half precision and 14.9 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.
Quadro RTX 8000 Passive— how many tensor cores does it have?
It has 576 tensor cores across 72 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.
Quadro RTX 8000 Passive— does it support CUDA?
Yes. It 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.
Quadro RTX 8000 Passive— 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.
Quadro RTX 8000 Passive— is it good for running local AI models?
Its memory is large enough for models most desktop hardware cannot touch and its bandwidth gives usable, if unspectacular, generation speeds. In total it runs 607 of the models we track. Whether that is enough depends entirely on which model you want — the table above answers that directly.
Quadro RTX 8000 Passive— can it run a model that does not fit in its memory?
Only partly. Layers beyond the card's 48 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 8000 Passive cards be twice as fast?
No. A second card doubles the memory to 96 GB of combined memory, at roughly the same generation speed as one.
Quadro RTX 8000 Passive— which AI models can it run?
607 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.
Quadro RTX 8000 Passive— what is the largest AI model it can run?
The largest model in our catalogue that fits is Qwen3-Coder-Next at 80B parameters, compressed to IQ4_XS. It generates roughly 45.1 tokens per second and needs about 38.8 GB of the card's memory.
Quadro RTX 8000 Passive— 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 264 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.
Quadro RTX 8000 Passive— can it run 7B models?
Yes. For example it runs Gemma 4 E4B at Q8_0, using about 9.8 GB of memory and generating around 58.7 tokens per second.
Quadro RTX 8000 Passive— 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 91.8 tokens per second.
Quadro RTX 8000 Passive— can it run 30B models?
Yes. For example it runs ERNIE-4.5-VL-28B-A3B at Q8_0, using about 29.2 GB of memory and generating around 52.4 tokens per second.
Quadro RTX 8000 Passive— can it run 70B models?
Yes. For example it runs Qwen3-Coder-Next at IQ4_XS, using about 38.8 GB of memory and generating around 45.1 tokens per second.
Quadro RTX 8000 Passive— how much memory does it have?
This card has 48 GB of GDDR6. Around a tenth is reserved by the inference runtime and the driver, leaving roughly 43.2 GB available for a model and its conversation.
Quadro RTX 8000 Passive— what is its memory bandwidth?
Memory bandwidth reaches 624 GB/s across a bus of 384 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.
Quadro RTX 8000 Passive— what type of memory does it 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.
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.