Calculate the TPS of the Quadro RTX 3000 X2 Mobile 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
Qwen-VL
9.6B · Q3_K_M · 40.0 tok/s
Fastest model
Gemma 3 QAT 1B
142 tok/s · 1B
Which AI models can run on a Quadro RTX 3000 X2 Mobile?
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.
266 models match
Calculating| Quantisation | Fit | ||||||
|---|---|---|---|---|---|---|---|
|
142
tok/s
121–171 |
Gemma 3 1B | 1B | Mar 2025 | 1.8 GB | 33k tokens | Q8_0 | Comfortable |
|
142
tok/s
121–171 |
Gemma 3 QAT 1B | 1B | Apr 2025 | 1.8 GB | 33k tokens | Q8_0 | Comfortable |
|
142
tok/s
85–228 · low confidence |
HGRN 1B (WT 103) ≈ | 1B | Nov 2023 | 1.8 GB | 131k tokens ? | Q8_0 | Comfortable |
|
142
tok/s
85–228 · low confidence |
LLama 3..2 Typhoon 2 1B ≈ | 1B | Dec 2024 | 1.8 GB | 131k tokens ? | Q8_0 | Comfortable |
|
142
tok/s
85–228 · low confidence |
OLMo-1B ≈ | 1B | Feb 2024 | 1.8 GB | 131k tokens ? | Q8_0 | Comfortable |
|
142
tok/s
85–228 · low confidence |
Pythia-1b ≈ | 1B | Apr 2023 | 1.8 GB | 131k tokens ? | Q8_0 | Comfortable |
|
132
tok/s
79–211 · low confidence |
OpenELM-1.1B ≈ | 1.1B | May 2024 | 1.9 GB | 131k tokens ? | Q8_0 | Comfortable |
|
129
tok/s
78–207 · low confidence |
DeciCoder-1B ≈ | 1.1B | Aug 2023 | 1.9 GB | 131k tokens ? | Q8_0 | Comfortable |
|
129
tok/s
78–207 · low confidence |
SantaCoder ≈ | 1.1B | Jan 2023 | 1.9 GB | 131k tokens ? | Q8_0 | Comfortable |
|
129
tok/s
78–207 · low confidence |
TinyLlama-1.1B (1T token checkpoint) ≈ | 1.1B | Oct 2023 | 1.9 GB | 131k tokens ? | Q8_0 | Comfortable |
|
129
tok/s
78–207 · low confidence |
TinyLlama-1.1B (3T token checkpoint) ≈ | 1.1B | Oct 2023 | 1.9 GB | 131k tokens ? | Q8_0 | Comfortable |
|
119
tok/s
71–190 · low confidence |
EXAONE 4.0 (1.2B) ≈ | 1.2B | Jul 2025 | 2.0 GB | 131k tokens ? | Q8_0 | Comfortable |
|
119
tok/s
71–190 · low confidence |
MinerU2.5 ≈ | 1.2B | Sep 2025 | 2.0 GB | 131k tokens ? | Q8_0 | Comfortable |
|
119
tok/s
71–190 · low confidence |
Pleias 1.0 1.2B ≈ | 1.2B | Dec 2024 | 2.0 GB | 131k tokens ? | Q8_0 | Comfortable |
|
119
tok/s
71–190 · low confidence |
Pleias-RAG-1B ≈ | 1.2B | Apr 2025 | 2.0 GB | 131k tokens ? | Q8_0 | Comfortable |
|
116
tok/s
98–139 |
Llama 3.2 1B | 1.2B | Sep 2024 | 2.2 GB | 113k tokens | Q8_0 | Comfortable |
|
114
tok/s
68–183 · low confidence |
MiniCPM-1.2B ≈ | 1.2B | Jun 2024 | 2.0 GB | 131k tokens ? | Q8_0 | Comfortable |
|
109
tok/s
66–175 · low confidence |
DeepSeek Coder 1.3B ≈ | 1.3B | Jan 2024 | 2.1 GB | 131k tokens ? | Q8_0 | Comfortable |
|
109
tok/s
66–175 · low confidence |
DeepSeek-VL-1.3B ≈ | 1.3B | Mar 2024 | 2.1 GB | 131k tokens ? | Q8_0 | Comfortable |
|
109
tok/s
66–175 · low confidence |
DigiRL ≈ | 1.3B | Jun 2024 | 2.1 GB | 131k tokens ? | Q8_0 | Comfortable |
|
109
tok/s
66–175 · low confidence |
GLA Transformer 1.3B ≈ | 1.3B | Aug 2024 | 2.1 GB | 131k tokens ? | Q8_0 | Comfortable |
|
109
tok/s
66–175 · low confidence |
Janus 1.3B ≈ | 1.3B | Oct 2024 | 2.1 GB | 131k tokens ? | Q8_0 | Comfortable |
|
109
tok/s
66–175 · low confidence |
Kosmos-2.5 ≈ | 1.3B | Aug 2024 | 2.1 GB | 131k tokens ? | Q8_0 | Comfortable |
|
109
tok/s
66–175 · low confidence |
Otter ≈ | 1.3B | May 2023 | 2.1 GB | 131k tokens ? | Q8_0 | Comfortable |
|
109
tok/s
66–175 · 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 3000 X2 Mobile 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
- 6 GB
- Memory bandwidth
- 336 GB/s
- Memory type
- GDDR6
- Memory bus width
- 192 bit
- Memory clock
- 1.75 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
- TU106
- Architecture
- Turing
- Generation
- Quadro Turing-M(Tx000)
- Foundry
- TSMC
- Process size
- 12 nm
- Transistors
- 10.8 billion
- Transistor density
- 24,300 K/mm²
- Die size
- 445 mm²
- Package
- BGA-2228
- Released
- 27 May 2019
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
- 945 MHz
- Boost clock
- 1.38 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
- 1,920
- Texture mapping units
- 120
- Render output units
- 64
- Streaming multiprocessors
- 30
- Tensor cores
- 240
- Ray tracing cores
- 30
- 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)
- 10.6 TFLOPS
- Single precision (FP32)
- 5.3 TFLOPS
- Double precision (FP64)
- 165.6 GFLOPS
- Pixel rate
- 88 GPixel/s
- Texture rate
- 166 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
- Power connectors
- None
- Bus interface
- PCIe 3.0 x16
- Slot width
- MXM Module
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 3000 X2 Mobile
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
6 GB
Bandwidth
336 GB/s
Largest model
Qwen-VL
At 6 GB of GDDR6 the Quadro RTX 3000 X2 Mobile is limited to the smaller end of the catalogue. About 5.4 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 336 GB/s across a 192-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.
Bandwidth is clock times bus width, and this card clocks its memory at 1.75 GHz. Both halves matter, and neither is visible in a gaming benchmark.
In practice that combination tops out at Qwen-VL — 9.6B, compressed to Q3_K_M, generating around 40.0 tokens per second.
The chip and how it was built
The Quadro RTX 3000 X2 Mobile is built on the TU106 graphics processor, using NVIDIA's Turing architecture, as part of the Quadro Turing-M(Tx000) generation.
The chip is manufactured by TSMC, on a 12 nm process, with a die measuring 445 mm², holding 10.8 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 May 2019, 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
10.6 TFLOPS
FP64
165.6 GFLOPS
Tensor cores
240
On paper the Quadro RTX 3000 X2 Mobile reaches 10.6 TFLOPS at half precision and 5.3 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 165.6 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 240 tensor cores across 30 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 945 MHz at base to 1.38 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 3000 X2 Mobile 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 1,920 shading units, 120 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 3000 X2 Mobile is rated at 160 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 a mxm module. 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 3000 X2 Mobile
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 3000 X2 Mobile
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 3000 X2 Mobile
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 266 models this Quadro RTX 3000 X2 Mobile runs is in the table above. Search narrows it by name or by size.
-
02
Match the context to your work
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 6 GB.
-
03
Set a minimum quality if you need one
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
The figures are calculated, not measured. 142 tok/s on Gemma 3 QAT 1B is the fastest result on this card, and like every row it carries a range that reflects how much the runtime matters.
-
05
Check the memory column before committing
A tight fit runs but leaves no room to raise the context later; comfortable has headroom. The memory column shows what each model needs against the 6 GB available.
-
06
Open the model to compare cards
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 Quadro RTX 3000 X2 Mobile compares.
Answers
Quadro RTX 3000 X2 Mobile — common questions
What are the TFLOPS of a Quadro RTX 3000 X2 Mobile?
The Quadro RTX 3000 X2 Mobile is rated at 10.6 TFLOPS at half precision and 5.3 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 3000 X2 Mobile have?
The Quadro RTX 3000 X2 Mobile has 240 tensor cores across 30 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 3000 X2 Mobile support CUDA?
Yes. The Quadro RTX 3000 X2 Mobile 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 3000 X2 Mobile 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 3000 X2 Mobile good for running local AI models?
Its memory limits it to smaller models though its bandwidth means generation will feel slow on larger models. In total it runs 266 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 3000 X2 Mobile run a model that does not fit in its memory?
Offloading past the card's 6 GB is possible and usually a false economy: the system-memory portion is slow enough to dominate the result.
Would two Quadro RTX 3000 X2 Mobile cards be twice as fast?
Pairing Quadro RTX 3000 X2 Mobile cards buys headroom rather than pace: 12 GB of combined memory, at roughly the same generation speed as one.
What AI models can a Quadro RTX 3000 X2 Mobile run?
266 of the 679 open-weight language models we track fit on a Quadro RTX 3000 X2 Mobile 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 3000 X2 Mobile can run?
The largest model in our catalogue that fits on a Quadro RTX 3000 X2 Mobile is Qwen-VL at 9.6B parameters, compressed to Q3_K_M. It generates roughly 40.0 tokens per second and needs about 5.4 GB of the card's memory.
How many tokens per second does a Quadro RTX 3000 X2 Mobile produce?
It depends on the model. On a Quadro RTX 3000 X2 Mobile the fastest model we track is Gemma 3 QAT 1B at about 142 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 3000 X2 Mobile run a 7B model?
Yes. For example a Quadro RTX 3000 X2 Mobile runs MetaMath 7B (Mistral finetune) at IQ4_XS, using about 5.1 GB of memory and generating around 49.9 tokens per second.
How much memory does a Quadro RTX 3000 X2 Mobile have?
A Quadro RTX 3000 X2 Mobile has 6 GB of GDDR6 memory. Around a tenth of that is reserved by the inference runtime and the driver, leaving roughly 5.4 GB available for a model and its conversation.
What is the memory bandwidth of a Quadro RTX 3000 X2 Mobile?
The Quadro RTX 3000 X2 Mobile has 336 GB/s of memory bandwidth, across a 192-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 3000 X2 Mobile use?
It uses GDDR6 clocked at 1.75 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 3000 X2 Mobile?
The Quadro RTX 3000 X2 Mobile is a NVIDIA product, with the chip manufactured by TSMC, on a 12 nm process.
When was the Quadro RTX 3000 X2 Mobile released?
The Quadro RTX 3000 X2 Mobile was released in May 2019.
How much power does a Quadro RTX 3000 X2 Mobile use?
The Quadro RTX 3000 X2 Mobile has a rated board power of 160 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.
How much cache does a Quadro RTX 3000 X2 Mobile have?
The Quadro RTX 3000 X2 Mobile 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.
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.