Calculate the TPS of the Jetson Orin Nano 4 GB 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
DeciLM 6B
5.7B · Q3_K_M · 6.8 tok/s
Fastest model
Gemma 4 E2B
17.0 tok/s · 5.1B
Which AI models can run on a Jetson Orin Nano 4 GB?
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.
105 models match
Calculating| Quantisation | Fit | ||||||
|---|---|---|---|---|---|---|---|
|
17.0
tok/s
10–27 · low confidence |
Gemma 4 E2B ≈ | 5.1B | Apr 2026 | 3.4 GB | 11k tokens ? | Q3_K_M | Tight |
|
14.5
tok/s
12–17 |
Gemma 3 1B | 1B | Mar 2025 | 1.8 GB | 33k tokens | Q8_0 | Comfortable |
|
14.5
tok/s
12–17 |
Gemma 3 QAT 1B | 1B | Apr 2025 | 1.8 GB | 33k tokens | Q8_0 | Comfortable |
|
14.5
tok/s
9–23 · low confidence |
HGRN 1B (WT 103) ≈ | 1B | Nov 2023 | 1.8 GB | 131k tokens ? | Q8_0 | Comfortable |
|
14.5
tok/s
9–23 · low confidence |
LLama 3..2 Typhoon 2 1B ≈ | 1B | Dec 2024 | 1.8 GB | 131k tokens ? | Q8_0 | Comfortable |
|
14.5
tok/s
9–23 · low confidence |
OLMo-1B ≈ | 1B | Feb 2024 | 1.8 GB | 131k tokens ? | Q8_0 | Comfortable |
|
14.5
tok/s
9–23 · low confidence |
Pythia-1b ≈ | 1B | Apr 2023 | 1.8 GB | 131k tokens ? | Q8_0 | Comfortable |
|
13.4
tok/s
8–21 · low confidence |
OpenELM-1.1B ≈ | 1.1B | May 2024 | 1.9 GB | 131k tokens ? | Q8_0 | Comfortable |
|
13.1
tok/s
8–21 · low confidence |
DeciCoder-1B ≈ | 1.1B | Aug 2023 | 1.9 GB | 131k tokens ? | Q8_0 | Comfortable |
|
13.1
tok/s
8–21 · low confidence |
SantaCoder ≈ | 1.1B | Jan 2023 | 1.9 GB | 131k tokens ? | Q8_0 | Comfortable |
|
13.1
tok/s
8–21 · low confidence |
TinyLlama-1.1B (1T token checkpoint) ≈ | 1.1B | Oct 2023 | 1.9 GB | 131k tokens ? | Q8_0 | Comfortable |
|
13.1
tok/s
8–21 · low confidence |
TinyLlama-1.1B (3T token checkpoint) ≈ | 1.1B | Oct 2023 | 1.9 GB | 131k tokens ? | Q8_0 | Comfortable |
|
12.0
tok/s
7–19 · low confidence |
EXAONE 4.0 (1.2B) ≈ | 1.2B | Jul 2025 | 2.0 GB | 131k tokens ? | Q8_0 | Comfortable |
|
12.0
tok/s
7–19 · low confidence |
LFM2-1.2B ≈ | 1.2B | Jul 2025 | 2.0 GB | 131k tokens ? | Q8_0 | Comfortable |
|
12.0
tok/s
7–19 · low confidence |
MinerU2.5 ≈ | 1.2B | Sep 2025 | 2.0 GB | 131k tokens ? | Q8_0 | Comfortable |
|
12.0
tok/s
7–19 · low confidence |
Pleias 1.0 1.2B ≈ | 1.2B | Dec 2024 | 2.0 GB | 131k tokens ? | Q8_0 | Comfortable |
|
12.0
tok/s
7–19 · low confidence |
Pleias-RAG-1B ≈ | 1.2B | Apr 2025 | 2.0 GB | 131k tokens ? | Q8_0 | Comfortable |
|
11.8
tok/s
10–14 |
Llama 3.2 1B | 1.2B | Sep 2024 | 2.2 GB | 54k tokens | Q8_0 | Comfortable |
|
11.6
tok/s
7–19 · low confidence |
MiniCPM-1.2B ≈ | 1.2B | Jun 2024 | 2.0 GB | 131k tokens ? | Q8_0 | Comfortable |
|
11.1
tok/s
7–18 · low confidence |
DeepSeek Coder 1.3B ≈ | 1.3B | Jan 2024 | 2.1 GB | 131k tokens ? | Q8_0 | Comfortable |
|
11.1
tok/s
7–18 · low confidence |
DeepSeek-VL-1.3B ≈ | 1.3B | Mar 2024 | 2.1 GB | 131k tokens ? | Q8_0 | Comfortable |
|
11.1
tok/s
7–18 · low confidence |
DigiRL ≈ | 1.3B | Jun 2024 | 2.1 GB | 131k tokens ? | Q8_0 | Comfortable |
|
11.1
tok/s
7–18 · low confidence |
GLA Transformer 1.3B ≈ | 1.3B | Aug 2024 | 2.1 GB | 131k tokens ? | Q8_0 | Comfortable |
|
11.1
tok/s
7–18 · low confidence |
Janus 1.3B ≈ | 1.3B | Oct 2024 | 2.1 GB | 131k tokens ? | Q8_0 | Comfortable |
|
11.1
tok/s
7–18 · low confidence |
Kosmos-2.5 ≈ | 1.3B | Aug 2024 | 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 4 GB 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
- 4 GB
- Memory bandwidth
- 34 GB/s
- Memory type
- LPDDR5
- Memory bus width
- 64 bit
- Memory clock
- 533 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
- 1 March 2023
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
- 625 MHz
- Boost clock
- 625 MHz
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
- 512
- Texture mapping units
- 16
- Render output units
- 8
- Streaming multiprocessors
- 4
- Tensor cores
- 16
- 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)
- 1.3 TFLOPS
- Single precision (FP32)
- 640 GFLOPS
- Pixel rate
- 5 GPixel/s
- Texture rate
- 10 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)
- 10 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 4 GB
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
4 GB
Bandwidth
34 GB/s
Largest model
DeciLM 6B
Jetson Orin Nano 4 GB carries only 4 GB of LPDDR5. That limits it to the smaller end of the catalogue, and a model has to fit entirely inside before it generates anything at all. A runtime actually gets about 3.6 GB.
Memory bandwidth reaches 34 GB/s across a bus of 64 bits. 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 533 MHz. Both halves matter, and neither is visible in a gaming benchmark.
The biggest thing it holds is DeciLM 6B, 5.7B, compressed to Q3_K_M and generating around 6.8 tokens per second.
The chip and how it was built
Jetson Orin Nano 4 GB is built on the graphics processor GA10B, using the architecture Ampere from NVIDIA, as part of the generation Tegra(Ampere).
The chip is manufactured by Samsung, on a process of 8 nm, 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 March 2023, roughly 3.5388791790838 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
1.3 TFLOPS
Tensor cores
16
On paper Jetson Orin Nano 4 GB reaches 1.3 TFLOPS at half precision, and 640 GFLOPS 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 16 tensor cores across 4 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 625 MHz to a boost of 625 MHz. 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
Jetson Orin Nano 4 GB has an L1 cache of 128 KB, backed by an L2 cache of 2 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 512 shading units, 16 texture mapping units, and 8 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
10 W
Jetson Orin Nano 4 GB is rated at 10 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 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 4 GB
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 4 GB
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 4 GB
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
The table lists 105 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
Longer conversations cost memory on top of the weights. Against 4 GB so the setting is worth getting right.
-
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
Read the speed and the range
Speeds come with error bars for a reason. The best case here is 17.0 tok/s on Gemma 4 E2B. 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 an available 4 GB.
-
06
Cross-check against other hardware
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 Jetson Orin Nano 4 GB.
Answers
Jetson Orin Nano 4 GB — common questions
Jetson Orin Nano 4 GB— which AI models can it run?
105 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.
Jetson Orin Nano 4 GB— what is the largest AI model it can run?
The largest model in our catalogue that fits is DeciLM 6B at 5.7B parameters, compressed to Q3_K_M. It generates roughly 6.8 tokens per second and needs about 3.5 GB of the card's memory.
Jetson Orin Nano 4 GB— how many tokens per second does it produce?
It depends on the model. The fastest model we track here is Gemma 4 E2B at about 17.0 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.
Jetson Orin Nano 4 GB— how much memory does it have?
This card has 4 GB of LPDDR5. Around a tenth is reserved by the inference runtime and the driver, leaving roughly 3.6 GB available for a model and its conversation.
Jetson Orin Nano 4 GB— what is its memory bandwidth?
Memory bandwidth reaches 34 GB/s across a bus of 64 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.
Jetson Orin Nano 4 GB— what type of memory does it use?
It uses LPDDR5 clocked at 533 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.
Jetson Orin Nano 4 GB— who makes it?
This is a product of NVIDIA, with the chip manufactured by Samsung, on a process of 8 nm.
Jetson Orin Nano 4 GB— when was it released?
It was released in March 2023.
Jetson Orin Nano 4 GB— how much power does it use?
Rated board power is 10 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.
Jetson Orin Nano 4 GB— how much cache does it have?
The L1 cache is 128 KB, and the L2 cache is 2 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.
Jetson Orin Nano 4 GB— what are its TFLOPS?
It is rated at 1.3 TFLOPS at half precision and 640 GFLOPS 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.
Jetson Orin Nano 4 GB— how many tensor cores does it have?
It has 16 tensor cores across 4 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.
Jetson Orin Nano 4 GB— does it support CUDA?
Yes. It 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.
Jetson Orin Nano 4 GB— what bus interface does it 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.
Jetson Orin Nano 4 GB— is it 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 105 of the models we track. Whether that is enough depends entirely on which model you want — the table above answers that directly.
Jetson Orin Nano 4 GB— can it run a model that does not fit in its memory?
Only partly. Layers beyond the card's 4 GB is possible and usually a false economy: the system-memory portion is slow enough to dominate the result.
Would two Jetson Orin Nano 4 GB cards be twice as fast?
Capacity adds, throughput does not. Two of them give you 8 GB of combined memory, at roughly the same generation speed as one.
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.