Calculate the TPS of the A10M 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
Qwen3-Omni-30B-A3B
35.3B · IQ4_XS · 81.9 tok/s
Fastest model
Gemma 3 QAT 1B
212 tok/s · 1B
What AI models can a A10M 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.
495 models match
Calculating| Quantisation | Fit | ||||||
|---|---|---|---|---|---|---|---|
|
212
tok/s
180–254 |
Gemma 3 1B | 1B | Mar 2025 | 1.8 GB | 33k tokens | Q8_0 | Comfortable |
|
212
tok/s
180–254 |
Gemma 3 QAT 1B | 1B | Apr 2025 | 1.8 GB | 33k tokens | Q8_0 | Comfortable |
|
212
tok/s
127–339 · low confidence |
HGRN 1B (WT 103) ≈ | 1B | Nov 2023 | 1.8 GB | 131k tokens ? | Q8_0 | Comfortable |
|
212
tok/s
127–339 · low confidence |
LLama 3..2 Typhoon 2 1B ≈ | 1B | Dec 2024 | 1.8 GB | 131k tokens ? | Q8_0 | Comfortable |
|
212
tok/s
127–339 · low confidence |
OLMo-1B ≈ | 1B | Feb 2024 | 1.8 GB | 131k tokens ? | Q8_0 | Comfortable |
|
212
tok/s
127–339 · low confidence |
Pythia-1b ≈ | 1B | Apr 2023 | 1.8 GB | 131k tokens ? | Q8_0 | Comfortable |
|
196
tok/s
118–314 · low confidence |
OpenELM-1.1B ≈ | 1.1B | May 2024 | 1.9 GB | 131k tokens ? | Q8_0 | Comfortable |
|
193
tok/s
116–308 · low confidence |
DeciCoder-1B ≈ | 1.1B | Aug 2023 | 1.9 GB | 131k tokens ? | Q8_0 | Comfortable |
|
193
tok/s
116–308 · low confidence |
SantaCoder ≈ | 1.1B | Jan 2023 | 1.9 GB | 131k tokens ? | Q8_0 | Comfortable |
|
193
tok/s
116–308 · low confidence |
TinyLlama-1.1B (1T token checkpoint) ≈ | 1.1B | Oct 2023 | 1.9 GB | 131k tokens ? | Q8_0 | Comfortable |
|
193
tok/s
116–308 · low confidence |
TinyLlama-1.1B (3T token checkpoint) ≈ | 1.1B | Oct 2023 | 1.9 GB | 131k tokens ? | Q8_0 | Comfortable |
|
177
tok/s
106–282 · low confidence |
EXAONE 4.0 (1.2B) ≈ | 1.2B | Jul 2025 | 2.0 GB | 131k tokens ? | Q8_0 | Comfortable |
|
177
tok/s
106–282 · low confidence |
MinerU2.5 ≈ | 1.2B | Sep 2025 | 2.0 GB | 131k tokens ? | Q8_0 | Comfortable |
|
177
tok/s
106–282 · low confidence |
Pleias 1.0 1.2B ≈ | 1.2B | Dec 2024 | 2.0 GB | 131k tokens ? | Q8_0 | Comfortable |
|
177
tok/s
106–282 · low confidence |
Pleias-RAG-1B ≈ | 1.2B | Apr 2025 | 2.0 GB | 131k tokens ? | Q8_0 | Comfortable |
|
172
tok/s
146–207 |
Llama 3.2 1B | 1.2B | Sep 2024 | 2.2 GB | 131k tokens | Q8_0 | Comfortable |
|
170
tok/s
102–272 · low confidence |
MiniCPM-1.2B ≈ | 1.2B | Jun 2024 | 2.0 GB | 131k tokens ? | Q8_0 | Comfortable |
|
163
tok/s
98–261 · low confidence |
DeepSeek Coder 1.3B ≈ | 1.3B | Jan 2024 | 2.1 GB | 131k tokens ? | Q8_0 | Comfortable |
|
163
tok/s
98–261 · low confidence |
DeepSeek-VL-1.3B ≈ | 1.3B | Mar 2024 | 2.1 GB | 131k tokens ? | Q8_0 | Comfortable |
|
163
tok/s
98–261 · low confidence |
DigiRL ≈ | 1.3B | Jun 2024 | 2.1 GB | 131k tokens ? | Q8_0 | Comfortable |
|
163
tok/s
98–261 · low confidence |
GLA Transformer 1.3B ≈ | 1.3B | Aug 2024 | 2.1 GB | 131k tokens ? | Q8_0 | Comfortable |
|
163
tok/s
98–261 · low confidence |
Janus 1.3B ≈ | 1.3B | Oct 2024 | 2.1 GB | 131k tokens ? | Q8_0 | Comfortable |
|
163
tok/s
98–261 · low confidence |
Kosmos-2.5 ≈ | 1.3B | Aug 2024 | 2.1 GB | 131k tokens ? | Q8_0 | Comfortable |
|
163
tok/s
98–261 · low confidence |
Otter ≈ | 1.3B | May 2023 | 2.1 GB | 131k tokens ? | Q8_0 | Comfortable |
|
163
tok/s
98–261 · 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
A10M 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
- 20 GB
- Memory bandwidth
- 500 GB/s
- Memory type
- GDDR6
- Memory bus width
- 320 bit
- Memory clock
- 1.56 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
- GA102
- Architecture
- Ampere
- Generation
- Server Ampere(Axx)
- Foundry
- Samsung
- Process size
- 8 nm
- Transistors
- 28.3 billion
- Transistor density
- 45,100 K/mm²
- Die size
- 628 mm²
- Package
- BGA-3328
- Released
- 1 February 2022
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
- 975 MHz
- Boost clock
- 1.64 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
- 7,168
- Texture mapping units
- 224
- Render output units
- 80
- Streaming multiprocessors
- 56
- Tensor cores
- 224
- Ray tracing cores
- 56
- L1 cache
- 128 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)
- 23.4 TFLOPS
- Single precision (FP32)
- 23.4 TFLOPS
- Double precision (FP64)
- 732.5 GFLOPS
- Pixel rate
- 131 GPixel/s
- Texture rate
- 366 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)
- 150 W
- Suggested power supply
- 450 W
- Power connectors
- 8-pin EPS
- Bus interface
- PCIe 4.0 x16
- Slot width
- Single-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
- 8.6
- DirectX
- 12.2
- OpenGL
- 4.6
- Vulkan
- 1.4
- OpenCL
- 3.0
- Shader model
- 6.8
Listings
Where to buy a A10M
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
Memory: the specification that decides everything
Memory
20 GB
Bandwidth
500 GB/s
Largest model
Qwen3-Omni-30B-A3B
20 GB of GDDR6 puts the A10M comfortably into small and mid-sized models, with roughly 18 GB usable once the driver overhead is taken out. The largest models are out of reach without splitting them.
The memory bus moves 500 GB/s across a 320-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 1.56 GHz. Both halves matter, and neither is visible in a gaming benchmark.
The practical ceiling is Qwen3-Omni-30B-A3B at 35.3B, held at IQ4_XS and running at roughly 81.9 tokens per second.
The chip and how it was built
The A10M is built on the GA102 graphics processor, using NVIDIA's Ampere architecture, as part of the Server Ampere(Axx) generation.
The chip is manufactured by Samsung, on a 8 nm process, with a die measuring 628 mm², holding 28.3 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 February 2022, roughly 4 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
23.4 TFLOPS
FP64
732.5 GFLOPS
Tensor cores
224
On paper the A10M reaches 23.4 TFLOPS at half precision and 23.4 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 732.5 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 224 tensor cores across 56 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 975 MHz at base to 1.64 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 A10M has 128 KB of L1 cache, backed by 6 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 7,168 shading units, 224 texture mapping units, and 80 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
150 W
The A10M is rated at 150 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 267 mm long, and needs 8-pin EPS. 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 A10M 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 A10M
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 A10M
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
Find the model in the table
The table lists 495 models this A10M can run. Search by name, or by size — typing 27b matches on the parameter count even when the name never states it.
-
02
Match the context to your work
Drag the slider to the conversation length you plan to work at. The cache grows with the conversation, and on 20 GB it is often what pushes a large model over the edge.
-
03
Choose how far you will compress
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 — 212 tok/s on Gemma 3 QAT 1B at the top end here. The same card and model vary by thirty to fifty per cent between inference runtimes.
-
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 20 GB available.
-
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 A10M is the right buy for it or merely a card that fits.
Answers
A10M — common questions
How many tokens per second does a A10M produce?
It depends on the model. On a A10M the fastest model we track is Gemma 3 QAT 1B at about 212 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 A10M run a 7B model?
Yes. For example a A10M runs Multi-Token Prediction 7B at Q8_0, using about 7.9 GB of memory and generating around 31.6 tokens per second.
Can a A10M run a 13B model?
Yes. For example a A10M runs DeepSeekMoE-16B at Q8_0, using about 17.5 GB of memory and generating around 73.6 tokens per second.
Can a A10M run a 30B model?
Yes. For example a A10M runs ERNIE-4.5-VL-28B-A3B at Q4_K_M, using about 16.2 GB of memory and generating around 97.0 tokens per second.
How much memory does a A10M have?
A A10M has 20 GB of GDDR6 memory. Around a tenth of that is reserved by the inference runtime and the driver, leaving roughly 18 GB available for a model and its conversation.
What is the memory bandwidth of a A10M?
The A10M has 500 GB/s of memory bandwidth, across a 320-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 A10M use?
It uses GDDR6 clocked at 1.56 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 A10M?
The A10M is a NVIDIA product, with the chip manufactured by Samsung, on a 8 nm process.
When was the A10M released?
The A10M was released in February 2022.
How much power does a A10M use?
The A10M has a rated board power of 150 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 A10M have?
The A10M has 128 KB of L1 cache, and 6 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 A10M?
The A10M is rated at 23.4 TFLOPS at half precision and 23.4 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 A10M have?
The A10M has 224 tensor cores across 56 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 A10M support CUDA?
Yes. The A10M 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 A10M 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 A10M 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 495 of the models we track. Whether that is enough depends entirely on which model you want — the table above answers that directly.
Can a A10M run a model that does not fit in its memory?
Only partly. Layers beyond the 20 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 A10M cards be twice as fast?
Pairing A10M cards buys headroom rather than pace: 40 GB of combined memory, at roughly the same generation speed as one.
What AI models can a A10M run?
495 of the 679 open-weight language models we track fit on a A10M 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 A10M can run?
The largest model in our catalogue that fits on a A10M is Qwen3-Omni-30B-A3B at 35.3B parameters, compressed to IQ4_XS. It generates roughly 81.9 tokens per second and needs about 17.9 GB of the card's memory.
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.