Calculate the TPS of the Jetson AGX Xavier 32 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
679 models in our catalogue altogether
Largest model it holds
Phi-3.5-MoE
60.8B · Q3_K_M · 14.3 tok/s
Fastest model
Gemma 3 QAT 1B
57.8 tok/s · 1B
Which AI models can run on a Jetson AGX Xavier 32 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.
513 models match
Calculating| Quantisation | Fit | ||||||
|---|---|---|---|---|---|---|---|
|
57.8
tok/s
49–69 |
Gemma 3 1B | 1B | Mar 2025 | 1.8 GB | 33k tokens | Q8_0 | Comfortable |
|
57.8
tok/s
49–69 |
Gemma 3 QAT 1B | 1B | Apr 2025 | 1.8 GB | 33k tokens | Q8_0 | Comfortable |
|
57.8
tok/s
35–93 · low confidence |
HGRN 1B (WT 103) ≈ | 1B | Nov 2023 | 1.8 GB | 131k tokens ? | Q8_0 | Comfortable |
|
57.8
tok/s
35–93 · low confidence |
LLama 3..2 Typhoon 2 1B ≈ | 1B | Dec 2024 | 1.8 GB | 131k tokens ? | Q8_0 | Comfortable |
|
57.8
tok/s
35–93 · low confidence |
OLMo-1B ≈ | 1B | Feb 2024 | 1.8 GB | 131k tokens ? | Q8_0 | Comfortable |
|
57.8
tok/s
35–93 · low confidence |
Pythia-1b ≈ | 1B | Apr 2023 | 1.8 GB | 131k tokens ? | Q8_0 | Comfortable |
|
53.5
tok/s
32–86 · low confidence |
OpenELM-1.1B ≈ | 1.1B | May 2024 | 1.9 GB | 131k tokens ? | Q8_0 | Comfortable |
|
52.6
tok/s
32–84 · low confidence |
DeciCoder-1B ≈ | 1.1B | Aug 2023 | 1.9 GB | 131k tokens ? | Q8_0 | Comfortable |
|
52.6
tok/s
32–84 · low confidence |
SantaCoder ≈ | 1.1B | Jan 2023 | 1.9 GB | 131k tokens ? | Q8_0 | Comfortable |
|
52.6
tok/s
32–84 · low confidence |
TinyLlama-1.1B (1T token checkpoint) ≈ | 1.1B | Oct 2023 | 1.9 GB | 131k tokens ? | Q8_0 | Comfortable |
|
52.6
tok/s
32–84 · low confidence |
TinyLlama-1.1B (3T token checkpoint) ≈ | 1.1B | Oct 2023 | 1.9 GB | 131k tokens ? | Q8_0 | Comfortable |
|
48.2
tok/s
29–77 · low confidence |
EXAONE 4.0 (1.2B) ≈ | 1.2B | Jul 2025 | 2.0 GB | 131k tokens ? | Q8_0 | Comfortable |
|
48.2
tok/s
29–77 · low confidence |
MinerU2.5 ≈ | 1.2B | Sep 2025 | 2.0 GB | 131k tokens ? | Q8_0 | Comfortable |
|
48.2
tok/s
29–77 · low confidence |
Pleias 1.0 1.2B ≈ | 1.2B | Dec 2024 | 2.0 GB | 131k tokens ? | Q8_0 | Comfortable |
|
48.2
tok/s
29–77 · low confidence |
Pleias-RAG-1B ≈ | 1.2B | Apr 2025 | 2.0 GB | 131k tokens ? | Q8_0 | Comfortable |
|
47.0
tok/s
40–56 |
Llama 3.2 1B | 1.2B | Sep 2024 | 2.2 GB | 131k tokens | Q8_0 | Comfortable |
|
46.3
tok/s
28–74 · low confidence |
MiniCPM-1.2B ≈ | 1.2B | Jun 2024 | 2.0 GB | 131k tokens ? | Q8_0 | Comfortable |
|
44.5
tok/s
27–71 · low confidence |
DeepSeek Coder 1.3B ≈ | 1.3B | Jan 2024 | 2.1 GB | 131k tokens ? | Q8_0 | Comfortable |
|
44.5
tok/s
27–71 · low confidence |
DeepSeek-VL-1.3B ≈ | 1.3B | Mar 2024 | 2.1 GB | 131k tokens ? | Q8_0 | Comfortable |
|
44.5
tok/s
27–71 · low confidence |
DigiRL ≈ | 1.3B | Jun 2024 | 2.1 GB | 131k tokens ? | Q8_0 | Comfortable |
|
44.5
tok/s
27–71 · low confidence |
GLA Transformer 1.3B ≈ | 1.3B | Aug 2024 | 2.1 GB | 131k tokens ? | Q8_0 | Comfortable |
|
44.5
tok/s
27–71 · low confidence |
Janus 1.3B ≈ | 1.3B | Oct 2024 | 2.1 GB | 131k tokens ? | Q8_0 | Comfortable |
|
44.5
tok/s
27–71 · low confidence |
Kosmos-2.5 ≈ | 1.3B | Aug 2024 | 2.1 GB | 131k tokens ? | Q8_0 | Comfortable |
|
44.5
tok/s
27–71 · low confidence |
Otter ≈ | 1.3B | May 2023 | 2.1 GB | 131k tokens ? | Q8_0 | Comfortable |
|
44.5
tok/s
27–71 · 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 AGX Xavier 32 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
- 32 GB
- Memory bandwidth
- 137 GB/s
- Memory type
- LPDDR4X
- Memory bus width
- 256 bit
- Memory clock
- 2.13 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
- GV10B
- Architecture
- Volta
- Generation
- Tegra(Volta)
- Foundry
- TSMC
- Process size
- 12 nm
- Transistors
- 9 billion
- Transistor density
- 25,700 K/mm²
- Die size
- 350 mm²
- Released
- 1 October 2018
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
- 854 MHz
- Boost clock
- 1.38 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
- 512
- Texture mapping units
- 32
- Render output units
- 16
- Streaming multiprocessors
- 8
- Tensor cores
- 64
- L1 cache
- 128 KB
- L2 cache
- 0.5 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)
- 2.8 TFLOPS
- Single precision (FP32)
- 1.4 TFLOPS
- Double precision (FP64)
- 705 GFLOPS
- Pixel rate
- 22 GPixel/s
- Texture rate
- 44 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)
- 30 W
- Bus interface
- PCIe 4.0 x4
- Slot width
- IGP
- Dimensions
- 100 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
- 7.2
- DirectX
- 12.1
- OpenGL
- 4.6
- Vulkan
- 1.2
- OpenCL
- 1.2
- Shader model
- 6.0
Listings
Where to buy a Jetson AGX Xavier 32 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
What the memory subsystem means for AI
Memory
32 GB
Bandwidth
137 GB/s
Largest model
Phi-3.5-MoE
The Jetson AGX Xavier 32 GB carries 32 GB of LPDDR4X, which covers the mid-sized models most people actually run — about 28.8 GB of it after the runtime and driver reserve their working space.
At 137 GB/s across a 256-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 2.13 GHz. Both halves matter, and neither is visible in a gaming benchmark.
The biggest thing it holds is Phi-3.5-MoE (60.8B) at Q3_K_M compression, for about 14.3 tokens per second.
The chip and how it was built
The Jetson AGX Xavier 32 GB is built on the GV10B graphics processor, using NVIDIA's Volta architecture, as part of the Tegra(Volta) generation.
The chip is manufactured by TSMC, on a 12 nm process, with a die measuring 350 mm², holding 9 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 October 2018, roughly 7 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
2.8 TFLOPS
FP64
705 GFLOPS
Tensor cores
64
On paper the Jetson AGX Xavier 32 GB reaches 2.8 TFLOPS at half precision and 1.4 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 705 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 64 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 854 MHz at base to 1.38 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 AGX Xavier 32 GB has 128 KB of L1 cache, backed by 0.5 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 512 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
30 W
The Jetson AGX Xavier 32 GB is rated at 30 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 100 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 AGX Xavier 32 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 AGX Xavier 32 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 AGX Xavier 32 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
Find the model in the table
Every one of the 513 models this Jetson AGX Xavier 32 GB runs is in the table above. Search narrows it by name or by size.
-
02
Decide how long your conversations run
Set the context to your real working length. Short questions cost almost nothing; a long document can consume a large share of the card's 32 GB.
-
03
Set a minimum quality if you need one
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
Take the range as the answer
Each speed is an estimate for a single conversation, with a range beneath it — 57.8 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
Read the fit verdict last
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 32 GB available.
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06
Check the same model from the other side
Each model page repeats this calculation for the whole catalogue. Worth a look before deciding: it shows what else runs the same model, and how the Jetson AGX Xavier 32 GB compares.
Answers
Jetson AGX Xavier 32 GB — common questions
Is the Jetson AGX Xavier 32 GB good for running local AI models?
Its memory comfortably covers the mid-sized models most people run locally though its bandwidth means generation will feel slow on larger models. In total it runs 513 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 AGX Xavier 32 GB run a model that does not fit in its memory?
It can be split, with the overflow held in system memory — but that part drags the whole thing down, and none of the 32 GB figures on this page assume it.
Would two Jetson AGX Xavier 32 GB cards be twice as fast?
Capacity adds, throughput does not. Two of them give you 64 GB to work with rather than twice the tokens per second — every figure here is for a single Jetson AGX Xavier 32 GB.
What AI models can a Jetson AGX Xavier 32 GB run?
513 of the 679 open-weight language models we track fit on a Jetson AGX Xavier 32 GB 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 AGX Xavier 32 GB can run?
The largest model in our catalogue that fits on a Jetson AGX Xavier 32 GB is Phi-3.5-MoE at 60.8B parameters, compressed to Q3_K_M. It generates roughly 14.3 tokens per second and needs about 27.2 GB of the card's memory.
How many tokens per second does a Jetson AGX Xavier 32 GB produce?
It depends on the model. On a Jetson AGX Xavier 32 GB the fastest model we track is Gemma 3 QAT 1B at about 57.8 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 AGX Xavier 32 GB run a 7B model?
Yes. For example a Jetson AGX Xavier 32 GB runs Multi-Token Prediction 7B at Q8_0, using about 7.9 GB of memory and generating around 8.6 tokens per second.
Can a Jetson AGX Xavier 32 GB run a 13B model?
Yes. For example a Jetson AGX Xavier 32 GB runs DeepSeekMoE-16B at Q8_0, using about 17.5 GB of memory and generating around 20.1 tokens per second.
Can a Jetson AGX Xavier 32 GB run a 30B model?
Yes. For example a Jetson AGX Xavier 32 GB runs ERNIE-4.5-VL-28B-A3B at Q6_K, using about 22.7 GB of memory and generating around 16.7 tokens per second.
How much memory does a Jetson AGX Xavier 32 GB have?
A Jetson AGX Xavier 32 GB has 32 GB of LPDDR4X memory. Around a tenth of that is reserved by the inference runtime and the driver, leaving roughly 28.8 GB available for a model and its conversation.
What is the memory bandwidth of a Jetson AGX Xavier 32 GB?
The Jetson AGX Xavier 32 GB has 137 GB/s of memory bandwidth, across a 256-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 Jetson AGX Xavier 32 GB use?
It uses LPDDR4X clocked at 2.13 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 Jetson AGX Xavier 32 GB?
The Jetson AGX Xavier 32 GB is a NVIDIA product, with the chip manufactured by TSMC, on a 12 nm process.
When was the Jetson AGX Xavier 32 GB released?
The Jetson AGX Xavier 32 GB was released in October 2018.
How much power does a Jetson AGX Xavier 32 GB use?
The Jetson AGX Xavier 32 GB has a rated board power of 30 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 AGX Xavier 32 GB have?
The Jetson AGX Xavier 32 GB has 128 KB of L1 cache, and 0.5 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 AGX Xavier 32 GB?
The Jetson AGX Xavier 32 GB is rated at 2.8 TFLOPS at half precision and 1.4 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 Jetson AGX Xavier 32 GB have?
The Jetson AGX Xavier 32 GB has 64 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 AGX Xavier 32 GB support CUDA?
Yes. The Jetson AGX Xavier 32 GB reports CUDA compute capability 7.2. 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 AGX Xavier 32 GB 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.
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.