Calculate the TPS of the Jetson Orin Nano Super 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
Baichuan 1-13B
13.3B · Q3_K_M · 8.8 tok/s
Fastest model
Gemma 3 QAT 1B
43.4 tok/s · 1B
Which AI models can run on a Jetson Orin Nano Super?
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.
337 models match
Calculating| Quantisation | Fit | ||||||
|---|---|---|---|---|---|---|---|
|
43.4
tok/s
37–52 |
Gemma 3 1B | 1B | Mar 2025 | 1.8 GB | 33k tokens | Q8_0 | Comfortable |
|
43.4
tok/s
37–52 |
Gemma 3 QAT 1B | 1B | Apr 2025 | 1.8 GB | 33k tokens | Q8_0 | Comfortable |
|
43.4
tok/s
26–69 · low confidence |
HGRN 1B (WT 103) ≈ | 1B | Nov 2023 | 1.8 GB | 131k tokens ? | Q8_0 | Comfortable |
|
43.4
tok/s
26–69 · low confidence |
LLama 3..2 Typhoon 2 1B ≈ | 1B | Dec 2024 | 1.8 GB | 131k tokens ? | Q8_0 | Comfortable |
|
43.4
tok/s
26–69 · low confidence |
OLMo-1B ≈ | 1B | Feb 2024 | 1.8 GB | 131k tokens ? | Q8_0 | Comfortable |
|
43.4
tok/s
26–69 · low confidence |
Pythia-1b ≈ | 1B | Apr 2023 | 1.8 GB | 131k tokens ? | Q8_0 | Comfortable |
|
40.2
tok/s
24–64 · low confidence |
OpenELM-1.1B ≈ | 1.1B | May 2024 | 1.9 GB | 131k tokens ? | Q8_0 | Comfortable |
|
39.4
tok/s
24–63 · low confidence |
DeciCoder-1B ≈ | 1.1B | Aug 2023 | 1.9 GB | 131k tokens ? | Q8_0 | Comfortable |
|
39.4
tok/s
24–63 · low confidence |
SantaCoder ≈ | 1.1B | Jan 2023 | 1.9 GB | 131k tokens ? | Q8_0 | Comfortable |
|
39.4
tok/s
24–63 · low confidence |
TinyLlama-1.1B (1T token checkpoint) ≈ | 1.1B | Oct 2023 | 1.9 GB | 131k tokens ? | Q8_0 | Comfortable |
|
39.4
tok/s
24–63 · low confidence |
TinyLlama-1.1B (3T token checkpoint) ≈ | 1.1B | Oct 2023 | 1.9 GB | 131k tokens ? | Q8_0 | Comfortable |
|
36.1
tok/s
22–58 · low confidence |
EXAONE 4.0 (1.2B) ≈ | 1.2B | Jul 2025 | 2.0 GB | 131k tokens ? | Q8_0 | Comfortable |
|
36.1
tok/s
22–58 · low confidence |
MinerU2.5 ≈ | 1.2B | Sep 2025 | 2.0 GB | 131k tokens ? | Q8_0 | Comfortable |
|
36.1
tok/s
22–58 · low confidence |
Pleias 1.0 1.2B ≈ | 1.2B | Dec 2024 | 2.0 GB | 131k tokens ? | Q8_0 | Comfortable |
|
36.1
tok/s
22–58 · low confidence |
Pleias-RAG-1B ≈ | 1.2B | Apr 2025 | 2.0 GB | 131k tokens ? | Q8_0 | Comfortable |
|
35.3
tok/s
30–42 |
Llama 3.2 1B | 1.2B | Sep 2024 | 2.2 GB | 131k tokens | Q8_0 | Comfortable |
|
34.8
tok/s
21–56 · low confidence |
MiniCPM-1.2B ≈ | 1.2B | Jun 2024 | 2.0 GB | 131k tokens ? | Q8_0 | Comfortable |
|
33.4
tok/s
20–53 · low confidence |
DeepSeek Coder 1.3B ≈ | 1.3B | Jan 2024 | 2.1 GB | 131k tokens ? | Q8_0 | Comfortable |
|
33.4
tok/s
20–53 · low confidence |
DeepSeek-VL-1.3B ≈ | 1.3B | Mar 2024 | 2.1 GB | 131k tokens ? | Q8_0 | Comfortable |
|
33.4
tok/s
20–53 · low confidence |
DigiRL ≈ | 1.3B | Jun 2024 | 2.1 GB | 131k tokens ? | Q8_0 | Comfortable |
|
33.4
tok/s
20–53 · low confidence |
GLA Transformer 1.3B ≈ | 1.3B | Aug 2024 | 2.1 GB | 131k tokens ? | Q8_0 | Comfortable |
|
33.4
tok/s
20–53 · low confidence |
Janus 1.3B ≈ | 1.3B | Oct 2024 | 2.1 GB | 131k tokens ? | Q8_0 | Comfortable |
|
33.4
tok/s
20–53 · low confidence |
Kosmos-2.5 ≈ | 1.3B | Aug 2024 | 2.1 GB | 131k tokens ? | Q8_0 | Comfortable |
|
33.4
tok/s
20–53 · low confidence |
Otter ≈ | 1.3B | May 2023 | 2.1 GB | 131k tokens ? | Q8_0 | Comfortable |
|
33.4
tok/s
20–53 · 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
Jetson Orin Nano Super 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
- 8 GB
- Memory bandwidth
- 102 GB/s
- Memory type
- LPDDR5
- Memory bus width
- 128 bit
- Memory clock
- 800 MHz
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
- GA10B
- Architecture
- Ampere
- Generation
- Tegra(Ampere)
- Foundry
- Samsung
- Process size
- 8 nm
- Die size
- 200 mm²
- Released
- 17 December 2024
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
- 1.02 GHz
- Boost clock
- 1.02 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
- 1,024
- Texture mapping units
- 32
- Render output units
- 16
- Streaming multiprocessors
- 8
- Tensor cores
- 32
- L1 cache
- 128 KB
- L2 cache
- 2 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)
- 4.2 TFLOPS
- Single precision (FP32)
- 2.1 TFLOPS
- Pixel rate
- 16 GPixel/s
- Texture rate
- 33 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)
- 25 W
- Bus interface
- PCIe 4.0 x4
- Slot width
- IGP
- Dimensions
- 70 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
- 8.7
- DirectX
- 12.2
- OpenGL
- 4.6
- Vulkan
- 1.4
- OpenCL
- 3.0
- Shader model
- 6.8
Listings
Where to buy a Jetson Orin Nano Super
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
Why memory is the number that matters here
Memory
8 GB
Bandwidth
102 GB/s
Largest model
Baichuan 1-13B
At 8 GB of LPDDR5 the Jetson Orin Nano Super is limited to the smaller end of the catalogue. About 7.2 GB is actually available to a runtime, and a model has to fit entirely inside it before generating anything at all.
At 102 GB/s across a 128-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 800 MHz. Both halves matter, and neither is visible in a gaming benchmark.
The practical ceiling is Baichuan 1-13B at 13.3B, held at Q3_K_M and running at roughly 8.8 tokens per second.
The chip and how it was built
The Jetson Orin Nano Super is built on the GA10B graphics processor, using NVIDIA's Ampere architecture, as part of the Tegra(Ampere) generation.
The chip is manufactured by Samsung, on a 8 nm process, with a die measuring 200 mm². 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 December 2024, roughly 1 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
4.2 TFLOPS
Tensor cores
32
On paper the Jetson Orin Nano Super reaches 4.2 TFLOPS at half precision and 2.1 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.
The card carries 32 tensor cores across 8 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 1.02 GHz at base to 1.02 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 Jetson Orin Nano Super has 128 KB of L1 cache, backed by 2 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 1,024 shading units, 32 texture mapping units, and 16 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
25 W
The Jetson Orin Nano Super is rated at 25 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 igp, measuring 70 mm long. 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 x4. 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 Jetson Orin Nano Super
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 Jetson Orin Nano Super
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 Jetson Orin Nano Super
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 337 models this Jetson Orin Nano Super can run. Search by name, or by size — typing 27b matches on the parameter count even when the name never states it.
-
02
Set the context length you will actually use
Longer conversations cost memory on top of the weights. With 8 GB to work in, that is frequently the difference between a model fitting and not.
-
03
Choose how far you will compress
By default the table picks the least-compressed copy that fits. Setting a floor removes models that only qualify through heavy compression.
-
04
Look at the range, not just the number
Each speed is an estimate for a single conversation, with a range beneath it — 43.4 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 headroom before you decide
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 8 GB available.
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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 Jetson Orin Nano Super is the right buy for it or merely a card that fits.
Answers
Jetson Orin Nano Super — common questions
What type of memory does a Jetson Orin Nano Super use?
It uses LPDDR5 clocked at 800 MHz. 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 Jetson Orin Nano Super?
The Jetson Orin Nano Super is a NVIDIA product, with the chip manufactured by Samsung, on a 8 nm process.
When was the Jetson Orin Nano Super released?
The Jetson Orin Nano Super was released in December 2024.
How much power does a Jetson Orin Nano Super use?
The Jetson Orin Nano Super has a rated board power of 25 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 Jetson Orin Nano Super have?
The Jetson Orin Nano Super has 128 KB of L1 cache, and 2 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 Jetson Orin Nano Super?
The Jetson Orin Nano Super is rated at 4.2 TFLOPS at half precision and 2.1 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 Jetson Orin Nano Super have?
The Jetson Orin Nano Super has 32 tensor cores across 8 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 Jetson Orin Nano Super support CUDA?
Yes. The Jetson Orin Nano Super reports CUDA compute capability 8.7. 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 Jetson Orin Nano Super use?
It uses PCIe 4.0 x4. 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 Jetson Orin Nano Super 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 337 of the models we track. Whether that is enough depends entirely on which model you want — the table above answers that directly.
Can a Jetson Orin Nano Super run a model that does not fit in its memory?
Only partly. Layers beyond the 8 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 Jetson Orin Nano Super cards be twice as fast?
Pairing Jetson Orin Nano Super cards buys headroom rather than pace: 16 GB of combined memory, at roughly the same generation speed as one.
What AI models can a Jetson Orin Nano Super run?
337 of the 679 open-weight language models we track fit on a Jetson Orin Nano Super 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 Jetson Orin Nano Super can run?
The largest model in our catalogue that fits on a Jetson Orin Nano Super is Baichuan 1-13B at 13.3B parameters, compressed to Q3_K_M. It generates roughly 8.8 tokens per second and needs about 7.2 GB of the card's memory.
How many tokens per second does a Jetson Orin Nano Super produce?
It depends on the model. On a Jetson Orin Nano Super the fastest model we track is Gemma 3 QAT 1B at about 43.4 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 Jetson Orin Nano Super run a 7B model?
Yes. For example a Jetson Orin Nano Super runs MetaMath 7B (LLaMa finetune) at Q4_K_M, using about 6.5 GB of memory and generating around 14.3 tokens per second.
Can a Jetson Orin Nano Super run a 13B model?
Yes. For example a Jetson Orin Nano Super runs Gemma 4 12B at Q3_K_M, using about 6.5 GB of memory and generating around 9.8 tokens per second.
How much memory does a Jetson Orin Nano Super have?
A Jetson Orin Nano Super has 8 GB of LPDDR5 memory. Around a tenth of that is reserved by the inference runtime and the driver, leaving roughly 7.2 GB available for a model and its conversation.
What is the memory bandwidth of a Jetson Orin Nano Super?
The Jetson Orin Nano Super has 102 GB/s of memory bandwidth, across a 128-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.
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.