Calculate the TPS of the RTX A3000 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 · 31.4 tok/s
Fastest model
Gemma 3 QAT 1B
112 tok/s · 1B
Which AI models can run on a RTX A3000 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 | ||||||
|---|---|---|---|---|---|---|---|
|
112
tok/s
95–134 |
Gemma 3 1B | 1B | Mar 2025 | 1.8 GB | 33k tokens | Q8_0 | Comfortable |
|
112
tok/s
95–134 |
Gemma 3 QAT 1B | 1B | Apr 2025 | 1.8 GB | 33k tokens | Q8_0 | Comfortable |
|
112
tok/s
67–179 · low confidence |
HGRN 1B (WT 103) ≈ | 1B | Nov 2023 | 1.8 GB | 131k tokens ? | Q8_0 | Comfortable |
|
112
tok/s
67–179 · low confidence |
LLama 3..2 Typhoon 2 1B ≈ | 1B | Dec 2024 | 1.8 GB | 131k tokens ? | Q8_0 | Comfortable |
|
112
tok/s
67–179 · low confidence |
OLMo-1B ≈ | 1B | Feb 2024 | 1.8 GB | 131k tokens ? | Q8_0 | Comfortable |
|
112
tok/s
67–179 · low confidence |
Pythia-1b ≈ | 1B | Apr 2023 | 1.8 GB | 131k tokens ? | Q8_0 | Comfortable |
|
104
tok/s
62–166 · low confidence |
OpenELM-1.1B ≈ | 1.1B | May 2024 | 1.9 GB | 131k tokens ? | Q8_0 | Comfortable |
|
102
tok/s
61–163 · low confidence |
DeciCoder-1B ≈ | 1.1B | Aug 2023 | 1.9 GB | 131k tokens ? | Q8_0 | Comfortable |
|
102
tok/s
61–163 · low confidence |
SantaCoder ≈ | 1.1B | Jan 2023 | 1.9 GB | 131k tokens ? | Q8_0 | Comfortable |
|
102
tok/s
61–163 · low confidence |
TinyLlama-1.1B (1T token checkpoint) ≈ | 1.1B | Oct 2023 | 1.9 GB | 131k tokens ? | Q8_0 | Comfortable |
|
102
tok/s
61–163 · low confidence |
TinyLlama-1.1B (3T token checkpoint) ≈ | 1.1B | Oct 2023 | 1.9 GB | 131k tokens ? | Q8_0 | Comfortable |
|
93.2
tok/s
56–149 · low confidence |
EXAONE 4.0 (1.2B) ≈ | 1.2B | Jul 2025 | 2.0 GB | 131k tokens ? | Q8_0 | Comfortable |
|
93.2
tok/s
56–149 · low confidence |
MinerU2.5 ≈ | 1.2B | Sep 2025 | 2.0 GB | 131k tokens ? | Q8_0 | Comfortable |
|
93.2
tok/s
56–149 · low confidence |
Pleias 1.0 1.2B ≈ | 1.2B | Dec 2024 | 2.0 GB | 131k tokens ? | Q8_0 | Comfortable |
|
93.2
tok/s
56–149 · low confidence |
Pleias-RAG-1B ≈ | 1.2B | Apr 2025 | 2.0 GB | 131k tokens ? | Q8_0 | Comfortable |
|
90.9
tok/s
77–109 |
Llama 3.2 1B | 1.2B | Sep 2024 | 2.2 GB | 113k tokens | Q8_0 | Comfortable |
|
89.6
tok/s
54–143 · low confidence |
MiniCPM-1.2B ≈ | 1.2B | Jun 2024 | 2.0 GB | 131k tokens ? | Q8_0 | Comfortable |
|
86.0
tok/s
52–138 · low confidence |
DeepSeek Coder 1.3B ≈ | 1.3B | Jan 2024 | 2.1 GB | 131k tokens ? | Q8_0 | Comfortable |
|
86.0
tok/s
52–138 · low confidence |
DeepSeek-VL-1.3B ≈ | 1.3B | Mar 2024 | 2.1 GB | 131k tokens ? | Q8_0 | Comfortable |
|
86.0
tok/s
52–138 · low confidence |
DigiRL ≈ | 1.3B | Jun 2024 | 2.1 GB | 131k tokens ? | Q8_0 | Comfortable |
|
86.0
tok/s
52–138 · low confidence |
GLA Transformer 1.3B ≈ | 1.3B | Aug 2024 | 2.1 GB | 131k tokens ? | Q8_0 | Comfortable |
|
86.0
tok/s
52–138 · low confidence |
Janus 1.3B ≈ | 1.3B | Oct 2024 | 2.1 GB | 131k tokens ? | Q8_0 | Comfortable |
|
86.0
tok/s
52–138 · low confidence |
Kosmos-2.5 ≈ | 1.3B | Aug 2024 | 2.1 GB | 131k tokens ? | Q8_0 | Comfortable |
|
86.0
tok/s
52–138 · low confidence |
Otter ≈ | 1.3B | May 2023 | 2.1 GB | 131k tokens ? | Q8_0 | Comfortable |
|
86.0
tok/s
52–138 · 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
RTX A3000 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
- 264 GB/s
- Memory type
- GDDR6
- Memory bus width
- 192 bit
- Memory clock
- 1.38 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
- GA104
- Architecture
- Ampere
- Generation
- Ampere-MW(Ax000)
- Foundry
- Samsung
- Process size
- 8 nm
- Transistors
- 17.4 billion
- Transistor density
- 44,400 K/mm²
- Die size
- 392 mm²
- Package
- BGA-2713
- Released
- 12 April 2021
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
- 600 MHz
- Boost clock
- 1.23 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
- 4,096
- Texture mapping units
- 128
- Render output units
- 64
- Streaming multiprocessors
- 32
- Tensor cores
- 128
- Ray tracing cores
- 32
- L1 cache
- 128 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)
- 10.1 TFLOPS
- Single precision (FP32)
- 10.1 TFLOPS
- Double precision (FP64)
- 157.4 GFLOPS
- Pixel rate
- 79 GPixel/s
- Texture rate
- 157 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)
- 70 W
- Power connectors
- None
- Bus interface
- PCIe 4.0 x16
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
- 8.6
- DirectX
- 12.2
- OpenGL
- 4.6
- Vulkan
- 1.4
- OpenCL
- 3.0
- Shader model
- 6.8
Listings
Where to buy a RTX A3000 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
264 GB/s
Largest model
Qwen-VL
At 6 GB of GDDR6 the RTX A3000 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.
At 264 GB/s across a 192-bit bus, bandwidth is this card's real constraint. Every token requires reading the entire model out of memory, so a large model will feel slow here even when it fits.
Bandwidth is clock times bus width, and this card clocks its memory at 1.38 GHz. Both halves matter, and neither is visible in a gaming benchmark.
The biggest thing it holds is Qwen-VL (9.6B) at Q3_K_M compression, for about 31.4 tokens per second.
The chip and how it was built
The RTX A3000 Mobile is built on the GA104 graphics processor, using NVIDIA's Ampere architecture, as part of the Ampere-MW(Ax000) generation.
The chip is manufactured by Samsung, on a 8 nm process, with a die measuring 392 mm², holding 17.4 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 April 2021, roughly 5 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
10.1 TFLOPS
FP64
157.4 GFLOPS
Tensor cores
128
On paper the RTX A3000 Mobile reaches 10.1 TFLOPS at half precision and 10.1 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 157.4 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 128 tensor cores across 32 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 600 MHz at base to 1.23 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 RTX A3000 Mobile has 128 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 4,096 shading units, 128 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
70 W
The RTX A3000 Mobile is rated at 70 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.
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 that run on a RTX A3000 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 RTX A3000 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 RTX A3000 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
Search for the model you want
Every one of the 266 models this RTX A3000 Mobile runs is in the table above. Search narrows it by name or by size.
-
02
Set the context length you will actually use
Drag the slider to the conversation length you plan to work at. The cache grows with the conversation, and on 6 GB it is often what pushes a large model over the edge.
-
03
Pin the comparison to one quality level
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
Read the speed and the range
Speeds come with error bars for a reason. The best case here is 112 tok/s on Gemma 3 QAT 1B, and which inference software you use moves that by thirty to fifty per cent.
-
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
Check the same model from the other side
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 RTX A3000 Mobile is the right buy for it or merely a card that fits.
Answers
RTX A3000 Mobile — common questions
What is the largest AI model a RTX A3000 Mobile can run?
The largest model in our catalogue that fits on a RTX A3000 Mobile is Qwen-VL at 9.6B parameters, compressed to Q3_K_M. It generates roughly 31.4 tokens per second and needs about 5.4 GB of the card's memory.
How many tokens per second does a RTX A3000 Mobile produce?
It depends on the model. On a RTX A3000 Mobile the fastest model we track is Gemma 3 QAT 1B at about 112 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 RTX A3000 Mobile run a 7B model?
Yes. For example a RTX A3000 Mobile runs MetaMath 7B (Mistral finetune) at IQ4_XS, using about 5.1 GB of memory and generating around 39.2 tokens per second.
How much memory does a RTX A3000 Mobile have?
A RTX A3000 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 RTX A3000 Mobile?
The RTX A3000 Mobile has 264 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 RTX A3000 Mobile use?
It uses GDDR6 clocked at 1.38 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 RTX A3000 Mobile?
The RTX A3000 Mobile is a NVIDIA product, with the chip manufactured by Samsung, on a 8 nm process.
When was the RTX A3000 Mobile released?
The RTX A3000 Mobile was released in April 2021.
How much power does a RTX A3000 Mobile use?
The RTX A3000 Mobile has a rated board power of 70 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 RTX A3000 Mobile have?
The RTX A3000 Mobile has 128 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 RTX A3000 Mobile?
The RTX A3000 Mobile is rated at 10.1 TFLOPS at half precision and 10.1 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 RTX A3000 Mobile have?
The RTX A3000 Mobile has 128 tensor cores across 32 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 RTX A3000 Mobile support CUDA?
Yes. The RTX A3000 Mobile reports CUDA compute capability 8.6. 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 RTX A3000 Mobile 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 RTX A3000 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 RTX A3000 Mobile run a model that does not fit in its memory?
Only partly. Layers beyond the 6 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 RTX A3000 Mobile cards be twice as fast?
Pairing RTX A3000 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 RTX A3000 Mobile run?
266 of the 679 open-weight language models we track fit on a RTX A3000 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.
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.