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
721 models in our catalogue altogether
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
Which AI models can run on a A10M?
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.
524 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 |
LFM2-1.2B ≈ | 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 |
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
A10M carries 20 GB of GDDR6. That reaches comfortably into small and mid-sized models, though the largest stay out of reach without splitting them. Driver overhead leaves roughly 18 GB.
Memory bandwidth reaches 500 GB/s across a bus of 320 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 1.56 GHz. Both halves matter, and neither is visible in a gaming benchmark.
The practical ceiling is Qwen3-Omni-30B-A3B, 35.3B, compressed to IQ4_XS and generating around 81.9 tokens per second.
The chip and how it was built
A10M is built on the graphics processor GA102, using the architecture Ampere from NVIDIA, as part of the generation Server Ampere(Axx).
The chip is manufactured by Samsung, on a process of 8 nm, 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 4.6152402721856 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 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 reaches 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 a base of 975 MHz to a boost of 1.64 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
A10M has an L1 cache of 128 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 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
A10M is rated at 150 W, and the suggested system power supply is 450 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 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 that run on a A10M
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 524 models this card 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 against a card holding 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. The top end here is 212 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 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 an available 20 GB.
-
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 right buy is A10M.
Answers
A10M — common questions
A10M— 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 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.
A10M— 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 47.1 tokens per second.
A10M— 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 73.6 tokens per second.
A10M— can it run 30B models?
Yes. For example it 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.
A10M— how much memory does it have?
This card has 20 GB of GDDR6. Around a tenth is reserved by the inference runtime and the driver, leaving roughly 18 GB available for a model and its conversation.
A10M— what is its memory bandwidth?
Memory bandwidth reaches 500 GB/s across a bus of 320 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.
A10M— what type of memory does it 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.
A10M— who makes it?
This is a product of NVIDIA, with the chip manufactured by Samsung, on a process of 8 nm.
A10M— when was it released?
It was released in February 2022.
A10M— how much power does it use?
Rated board power is 150 W, and the suggested system power supply is 450 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.
A10M— how much cache does it have?
The L1 cache is 128 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.
A10M— what are its TFLOPS?
It 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.
A10M— how many tensor cores does it have?
It 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.
A10M— does it support CUDA?
Yes. It 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.
A10M— what bus interface does it 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.
A10M— is it 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 524 of the models we track. Whether that is enough depends entirely on which model you want — the table above answers that directly.
A10M— can it run a model that does not fit in its memory?
Only partly. Layers beyond the card's 20 GB is possible and usually a false economy: the system-memory portion is slow enough to dominate the result.
Would two A10M cards be twice as fast?
Pairing them buys headroom rather than pace: 40 GB which lets you hold models neither could hold alone, but generation does not split that way. These figures describe one card.
A10M— which AI models can it run?
524 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.
A10M— what is the largest AI model it can run?
The largest model in our catalogue that fits 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.