NVIDIA Jetson AGX Orin 64GB vs NVIDIA Jetson AGX Thor
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NVIDIA Jetson AGX Thor is faster in all 9 like-for-like comparisons (largest gap: LLM generation, 2.66×).
| NVIDIA Jetson AGX Orin 64GB | NVIDIA Jetson AGX Thor | |
|---|---|---|
| Best object detection | 435 FPS B 2026-10-13 (page last edited) | 719 FPS B 2026-10-13 (page last edited) |
| Power | 65.00 W spec | 130 W spec |
| Perf / W | 6.69 FPS/W spec | 5.53 FPS/W spec |
| Price | $3499 list | $5499 list |
| Workloads with data | 3 of 7 | 2 of 7 |
Best published object detection result per platform (may be from different like-for-like groups — see data). n/a = not published; never estimated.
Show data — all workloads, sources and like-for-like groups
object detection
✓ Like-for-like: model-size class nano (YOLO-n), batch 1. Model, precision and stack may still differ.
| Platform | Result | Model · precision · stack | Grade | Power | Source |
|---|---|---|---|---|---|
| NVIDIA Jetson AGX Thor | 719 FPS inference_time · b=1 | YOLO26n FP16 · Ultralytics TensorRT · nano (YOLO-n) | B | 130 W spec platform spec envelope (not measured with this result) | Ultralytics documentation (benchmark table) 2026-10-13 (page last edited) |
| NVIDIA Jetson AGX Orin 64GB | 435 FPS inference_time · b=1 | YOLO26n INT8 · Ultralytics TensorRT · nano (YOLO-n) | B | 65.00 W spec platform spec envelope (not measured with this result) | Ultralytics documentation (benchmark table) 2026-10-13 (page last edited) |
instance segmentation
◌
No published instance segmentation results for NVIDIA Jetson AGX Orin 64GB or NVIDIA Jetson AGX Thor yet.
We only show cited public figures — nothing is estimated to fill gaps. Requests tell us what to source or test next.
pose estimation
◌
No published pose estimation results for NVIDIA Jetson AGX Orin 64GB or NVIDIA Jetson AGX Thor yet.
We only show cited public figures — nothing is estimated to fill gaps. Requests tell us what to source or test next.
image classification
◌
No published image classification results for NVIDIA Jetson AGX Orin 64GB or NVIDIA Jetson AGX Thor yet.
We only show cited public figures — nothing is estimated to fill gaps. Requests tell us what to source or test next.
semantic segmentation
Only NVIDIA Jetson AGX Orin 64GB has published semantic segmentation results.
| Platform | Result | Model · precision · stack | Grade | Power | Source |
|---|---|---|---|---|---|
| NVIDIA Jetson AGX Orin 64GB | 104 FPS throughput · b=1 | SegFormer-B0 (640x640) FP16 · TensorRT (ONNX export) · unknown | C | 65.00 W spec platform spec envelope (not measured with this result) | arXiv 2608.00927 AgriJetsonBench (abstract figures) 2026-08-02 (published) |
NVIDIA Jetson AGX Thor:
◌
No published semantic segmentation results for NVIDIA Jetson AGX Thor or NVIDIA Jetson AGX Orin 64GB yet.
We only show cited public figures — nothing is estimated to fill gaps. Requests tell us what to source or test next.
speech-to-text
◌
No published speech-to-text results for NVIDIA Jetson AGX Orin 64GB or NVIDIA Jetson AGX Thor yet.
We only show cited public figures — nothing is estimated to fill gaps. Requests tell us what to source or test next.
LLM generation
✓ Like-for-like: model-size class LLM 5–14B, batch 1. Model, precision and stack may still differ.
| Platform | Result | Model · precision · stack | Grade | Power | Source |
|---|---|---|---|---|---|
| NVIDIA Jetson AGX Thor | 41.32 tok/s tok/s gen, concurrency 1 · b=1 | Deepseek R1 7B n/s on page · NVIDIA (vLLM/TensorRT-LLM per page) · LLM 5–14B | D | 130 W spec platform spec envelope (not measured with this result) | NVIDIA Jetson Benchmarks on or before 2025-11-08 |
| NVIDIA Jetson AGX Orin 64GB | 30.00 tok/s generation_throughput · b=1 | Qwen 3 8B W4A16 · vLLM · LLM 5–14B | C | 65.00 W spec platform spec envelope (not measured with this result) | NVIDIA Jetson AI Lab benchmarks (benchmarks.json) 2026-07-21 (page last edited) |
✓ Like-for-like: model-size class LLM 5–14B, batch 8. Model, precision and stack may still differ.
| Platform | Result | Model · precision · stack | Grade | Power | Source |
|---|---|---|---|---|---|
| NVIDIA Jetson AGX Thor | 305 tok/s tok/s gen, concurrency 8 · b=8 | Deepseek R1 7B n/s on page · NVIDIA (vLLM/TensorRT-LLM per page) · LLM 5–14B | D | 130 W spec platform spec envelope (not measured with this result) | NVIDIA Jetson Benchmarks on or before 2025-11-08 |
| NVIDIA Jetson AGX Orin 64GB | 157 tok/s generation_throughput · b=8 | Qwen 3 8B W4A16 · vLLM · LLM 5–14B | C | 65.00 W spec platform spec envelope (not measured with this result) | NVIDIA Jetson AI Lab benchmarks (benchmarks.json) 2026-07-21 (page last edited) |
✓ Like-for-like: model-size class LLM 15–40B, batch 1. Model, precision and stack may still differ.
| Platform | Result | Model · precision · stack | Grade | Power | Source |
|---|---|---|---|---|---|
| NVIDIA Jetson AGX Thor | 72.60 tok/s generation_throughput · b=1 | Qwen3-30B-A3B INT4 GPTQ · Edge-LLM · LLM 15–40B | C | 130 W spec platform spec envelope (not measured with this result) | NVIDIA Jetson AI Lab benchmarks (benchmarks.json) 2026-07-21 (page last edited) |
| NVIDIA Jetson AGX Orin 64GB | 55.00 tok/s generation_throughput · b=1 | Qwen3-30B-A3B W4A16 · vLLM · LLM 15–40B | C | 65.00 W spec platform spec envelope (not measured with this result) | NVIDIA Jetson AI Lab benchmarks (benchmarks.json) 2026-07-21 (page last edited) |
✓ Like-for-like: model-size class LLM 15–40B, batch 8. Model, precision and stack may still differ.
| Platform | Result | Model · precision · stack | Grade | Power | Source |
|---|---|---|---|---|---|
| NVIDIA Jetson AGX Thor | 242 tok/s generation_throughput · b=8 | Qwen3-30B-A3B NVFP4 · vLLM · LLM 15–40B | C | 130 W spec platform spec envelope (not measured with this result) | NVIDIA Jetson AI Lab benchmarks (benchmarks.json) 2026-07-21 (page last edited) |
| NVIDIA Jetson AGX Orin 64GB | 174 tok/s generation_throughput · b=8 | Qwen3-30B-A3B W4A16 · vLLM · LLM 15–40B | C | 65.00 W spec platform spec envelope (not measured with this result) | NVIDIA Jetson AI Lab benchmarks (benchmarks.json) 2026-07-21 (page last edited) |
✓ Like-for-like: model-size class LLM ≤4B, batch 1. Model, precision and stack may still differ.
| Platform | Result | Model · precision · stack | Grade | Power | Source |
|---|---|---|---|---|---|
| NVIDIA Jetson AGX Thor | 122 tok/s generation_throughput · b=1 | Qwen3-VL-2B NVFP4 · vLLM · LLM ≤4B | C | 130 W spec platform spec envelope (not measured with this result) | NVIDIA Jetson AI Lab benchmarks (benchmarks.json) 2026-07-21 (page last edited) |
| NVIDIA Jetson AGX Orin 64GB | 60.00 tok/s generation_throughput · b=1 | Qwen3-VL-2B W4A16 · vLLM · LLM ≤4B | C | 65.00 W spec platform spec envelope (not measured with this result) | NVIDIA Jetson AI Lab benchmarks (benchmarks.json) 2026-07-21 (page last edited) |
✓ Like-for-like: model-size class LLM ≤4B, batch 8. Model, precision and stack may still differ.
| Platform | Result | Model · precision · stack | Grade | Power | Source |
|---|---|---|---|---|---|
| NVIDIA Jetson AGX Thor | 523 tok/s generation_throughput · b=8 | Gemma 4 E2B NVFP4 · vLLM · LLM ≤4B | C | 130 W spec platform spec envelope (not measured with this result) | NVIDIA Jetson AI Lab benchmarks (benchmarks.json) 2026-07-21 (page last edited) |
| NVIDIA Jetson AGX Orin 64GB | 273 tok/s generation_throughput · b=8 | Cosmos Reasoning 2 2B W4A16 · vLLM · LLM ≤4B | C | 65.00 W spec platform spec envelope (not measured with this result) | NVIDIA Jetson AI Lab benchmarks (benchmarks.json) 2026-07-21 (page last edited) |
✓ Like-for-like: model-size class unknown, batch 1 (size or batch not stated by source — unverified). Model, precision and stack may still differ.
| Platform | Result | Model · precision · stack | Grade | Power | Source |
|---|---|---|---|---|---|
| NVIDIA Jetson AGX Thor | 117 tok/s generation_throughput · b=1 | Pi 0.5 NVFP4 + FP8 · TRT · unknown | C | 130 W spec platform spec envelope (not measured with this result) | NVIDIA Jetson AI Lab benchmarks (benchmarks.json) 2026-07-21 (page last edited) |
| NVIDIA Jetson AGX Orin 64GB | 44.00 tok/s generation_throughput · b=1 | Cosmos3 Edge (Reasoner) BF16 · vLLM · unknown | C | 65.00 W spec platform spec envelope (not measured with this result) | NVIDIA Jetson AI Lab benchmarks (benchmarks.json) 2026-07-21 (page last edited) |
✓ Like-for-like: model-size class unknown, batch 8 (size or batch not stated by source — unverified). Model, precision and stack may still differ.
| Platform | Result | Model · precision · stack | Grade | Power | Source |
|---|---|---|---|---|---|
| NVIDIA Jetson AGX Thor | 293 tok/s generation_throughput · b=8 | Cosmos3 Edge (Reasoner) BF16 · vLLM · unknown | C | 130 W spec platform spec envelope (not measured with this result) | NVIDIA Jetson AI Lab benchmarks (benchmarks.json) 2026-07-21 (page last edited) |
| NVIDIA Jetson AGX Orin 64GB | 171 tok/s generation_throughput · b=8 | Cosmos3 Edge (Reasoner) BF16 · vLLM · unknown | C | 65.00 W spec platform spec envelope (not measured with this result) | NVIDIA Jetson AI Lab benchmarks (benchmarks.json) 2026-07-21 (page last edited) |
NVIDIA Jetson AGX Thor is faster in all 9 like-for-like comparisons (largest gap: LLM generation, 2.66×).
| NVIDIA Jetson AGX Orin 64GB | NVIDIA Jetson AGX Thor | |
|---|---|---|
| Best object detection | 435 FPS B 2026-10-13 (page last edited) | 719 FPS B 2026-10-13 (page last edited) |
| Power | 65.00 W spec | 130 W spec |
| Perf / W | 6.69 FPS/W spec | 5.53 FPS/W spec |
| Price | $3499 list | $5499 list |
| Workloads with data | 5 of 7 | 2 of 7 |
Best published object detection result per platform (may be from different like-for-like groups — see data). n/a = not published; never estimated.
Show data — all workloads, sources and like-for-like groups
object detection
✓ Like-for-like: model-size class nano (YOLO-n), batch 1. Model, precision and stack may still differ.
| Platform | Result | Model · precision · stack | Grade | Power | Source |
|---|---|---|---|---|---|
| NVIDIA Jetson AGX Thor | 719 FPS inference_time · b=1 | YOLO26n FP16 · Ultralytics TensorRT · nano (YOLO-n) | B | 130 W spec platform spec envelope (not measured with this result) | Ultralytics documentation (benchmark table) 2026-10-13 (page last edited) |
| NVIDIA Jetson AGX Orin 64GB | 435 FPS inference_time · b=1 | YOLO26n INT8 · Ultralytics TensorRT · nano (YOLO-n) | B | 65.00 W spec platform spec envelope (not measured with this result) | Ultralytics documentation (benchmark table) 2026-10-13 (page last edited) |
instance segmentation
◌
No published instance segmentation results for NVIDIA Jetson AGX Orin 64GB or NVIDIA Jetson AGX Thor yet.
We only show cited public figures — nothing is estimated to fill gaps. Requests tell us what to source or test next.
pose estimation
◌
No published pose estimation results for NVIDIA Jetson AGX Orin 64GB or NVIDIA Jetson AGX Thor yet.
We only show cited public figures — nothing is estimated to fill gaps. Requests tell us what to source or test next.
image classification
Only NVIDIA Jetson AGX Orin 64GB has published image classification results.
| Platform | Result | Model · precision · stack | Grade | Power | Source |
|---|---|---|---|---|---|
| NVIDIA Jetson AGX Orin 64GB | 6424 FPS throughput_offline · b=unknown | MLPerf ResNet INT8/FP16 (MLPerf closed) · TensorRT (MLPerf Inference, older round) · mid-size CNN | D | 65.00 W spec platform spec envelope (not measured with this result) | NVIDIA Jetson Benchmarks (MLPerf v3.1 results table) Older data (2023) |
NVIDIA Jetson AGX Thor:
◌
No published image classification results for NVIDIA Jetson AGX Thor or NVIDIA Jetson AGX Orin 64GB yet.
We only show cited public figures — nothing is estimated to fill gaps. Requests tell us what to source or test next.
semantic segmentation
Only NVIDIA Jetson AGX Orin 64GB has published semantic segmentation results.
| Platform | Result | Model · precision · stack | Grade | Power | Source |
|---|---|---|---|---|---|
| NVIDIA Jetson AGX Orin 64GB | 104 FPS throughput · b=1 | SegFormer-B0 (640x640) FP16 · TensorRT (ONNX export) · unknown | C | 65.00 W spec platform spec envelope (not measured with this result) | arXiv 2608.00927 AgriJetsonBench (abstract figures) 2026-08-02 (published) |
NVIDIA Jetson AGX Thor:
◌
No published semantic segmentation results for NVIDIA Jetson AGX Thor or NVIDIA Jetson AGX Orin 64GB yet.
We only show cited public figures — nothing is estimated to fill gaps. Requests tell us what to source or test next.
speech-to-text
Only NVIDIA Jetson AGX Orin 64GB has published speech-to-text results.
| Platform | Result | Model · precision · stack | Grade | Power | Source |
|---|---|---|---|---|---|
| NVIDIA Jetson AGX Orin 64GB | 1170 FPS throughput_offline · b=unknown | MLPerf RNN-T INT8/FP16 (MLPerf closed) · TensorRT (MLPerf Inference, older round) · RNN-T | D | 65.00 W spec platform spec envelope (not measured with this result) | NVIDIA Jetson Benchmarks (MLPerf v3.1 results table) Older data (2023) |
NVIDIA Jetson AGX Thor:
◌
No published speech-to-text results for NVIDIA Jetson AGX Thor or NVIDIA Jetson AGX Orin 64GB yet.
We only show cited public figures — nothing is estimated to fill gaps. Requests tell us what to source or test next.
LLM generation
✓ Like-for-like: model-size class LLM 5–14B, batch 1. Model, precision and stack may still differ.
| Platform | Result | Model · precision · stack | Grade | Power | Source |
|---|---|---|---|---|---|
| NVIDIA Jetson AGX Thor | 41.32 tok/s tok/s gen, concurrency 1 · b=1 | Deepseek R1 7B n/s on page · NVIDIA (vLLM/TensorRT-LLM per page) · LLM 5–14B | D | 130 W spec platform spec envelope (not measured with this result) | NVIDIA Jetson Benchmarks on or before 2025-11-08 |
| NVIDIA Jetson AGX Orin 64GB | 30.00 tok/s generation_throughput · b=1 | Qwen 3 8B W4A16 · vLLM · LLM 5–14B | C | 65.00 W spec platform spec envelope (not measured with this result) | NVIDIA Jetson AI Lab benchmarks (benchmarks.json) 2026-07-21 (page last edited) |
✓ Like-for-like: model-size class LLM 5–14B, batch 8. Model, precision and stack may still differ.
| Platform | Result | Model · precision · stack | Grade | Power | Source |
|---|---|---|---|---|---|
| NVIDIA Jetson AGX Thor | 305 tok/s tok/s gen, concurrency 8 · b=8 | Deepseek R1 7B n/s on page · NVIDIA (vLLM/TensorRT-LLM per page) · LLM 5–14B | D | 130 W spec platform spec envelope (not measured with this result) | NVIDIA Jetson Benchmarks on or before 2025-11-08 |
| NVIDIA Jetson AGX Orin 64GB | 157 tok/s generation_throughput · b=8 | Qwen 3 8B W4A16 · vLLM · LLM 5–14B | C | 65.00 W spec platform spec envelope (not measured with this result) | NVIDIA Jetson AI Lab benchmarks (benchmarks.json) 2026-07-21 (page last edited) |
✓ Like-for-like: model-size class LLM 15–40B, batch 1. Model, precision and stack may still differ.
| Platform | Result | Model · precision · stack | Grade | Power | Source |
|---|---|---|---|---|---|
| NVIDIA Jetson AGX Thor | 72.60 tok/s generation_throughput · b=1 | Qwen3-30B-A3B INT4 GPTQ · Edge-LLM · LLM 15–40B | C | 130 W spec platform spec envelope (not measured with this result) | NVIDIA Jetson AI Lab benchmarks (benchmarks.json) 2026-07-21 (page last edited) |
| NVIDIA Jetson AGX Orin 64GB | 55.00 tok/s generation_throughput · b=1 | Qwen3-30B-A3B W4A16 · vLLM · LLM 15–40B | C | 65.00 W spec platform spec envelope (not measured with this result) | NVIDIA Jetson AI Lab benchmarks (benchmarks.json) 2026-07-21 (page last edited) |
✓ Like-for-like: model-size class LLM 15–40B, batch 8. Model, precision and stack may still differ.
| Platform | Result | Model · precision · stack | Grade | Power | Source |
|---|---|---|---|---|---|
| NVIDIA Jetson AGX Thor | 242 tok/s generation_throughput · b=8 | Qwen3-30B-A3B NVFP4 · vLLM · LLM 15–40B | C | 130 W spec platform spec envelope (not measured with this result) | NVIDIA Jetson AI Lab benchmarks (benchmarks.json) 2026-07-21 (page last edited) |
| NVIDIA Jetson AGX Orin 64GB | 174 tok/s generation_throughput · b=8 | Qwen3-30B-A3B W4A16 · vLLM · LLM 15–40B | C | 65.00 W spec platform spec envelope (not measured with this result) | NVIDIA Jetson AI Lab benchmarks (benchmarks.json) 2026-07-21 (page last edited) |
✓ Like-for-like: model-size class LLM ≤4B, batch 1. Model, precision and stack may still differ.
| Platform | Result | Model · precision · stack | Grade | Power | Source |
|---|---|---|---|---|---|
| NVIDIA Jetson AGX Thor | 122 tok/s generation_throughput · b=1 | Qwen3-VL-2B NVFP4 · vLLM · LLM ≤4B | C | 130 W spec platform spec envelope (not measured with this result) | NVIDIA Jetson AI Lab benchmarks (benchmarks.json) 2026-07-21 (page last edited) |
| NVIDIA Jetson AGX Orin 64GB | 60.00 tok/s generation_throughput · b=1 | Qwen3-VL-2B W4A16 · vLLM · LLM ≤4B | C | 65.00 W spec platform spec envelope (not measured with this result) | NVIDIA Jetson AI Lab benchmarks (benchmarks.json) 2026-07-21 (page last edited) |
✓ Like-for-like: model-size class LLM ≤4B, batch 8. Model, precision and stack may still differ.
| Platform | Result | Model · precision · stack | Grade | Power | Source |
|---|---|---|---|---|---|
| NVIDIA Jetson AGX Thor | 523 tok/s generation_throughput · b=8 | Gemma 4 E2B NVFP4 · vLLM · LLM ≤4B | C | 130 W spec platform spec envelope (not measured with this result) | NVIDIA Jetson AI Lab benchmarks (benchmarks.json) 2026-07-21 (page last edited) |
| NVIDIA Jetson AGX Orin 64GB | 273 tok/s generation_throughput · b=8 | Cosmos Reasoning 2 2B W4A16 · vLLM · LLM ≤4B | C | 65.00 W spec platform spec envelope (not measured with this result) | NVIDIA Jetson AI Lab benchmarks (benchmarks.json) 2026-07-21 (page last edited) |
✓ Like-for-like: model-size class unknown, batch 1 (size or batch not stated by source — unverified). Model, precision and stack may still differ.
| Platform | Result | Model · precision · stack | Grade | Power | Source |
|---|---|---|---|---|---|
| NVIDIA Jetson AGX Thor | 117 tok/s generation_throughput · b=1 | Pi 0.5 NVFP4 + FP8 · TRT · unknown | C | 130 W spec platform spec envelope (not measured with this result) | NVIDIA Jetson AI Lab benchmarks (benchmarks.json) 2026-07-21 (page last edited) |
| NVIDIA Jetson AGX Orin 64GB | 44.00 tok/s generation_throughput · b=1 | Cosmos3 Edge (Reasoner) BF16 · vLLM · unknown | C | 65.00 W spec platform spec envelope (not measured with this result) | NVIDIA Jetson AI Lab benchmarks (benchmarks.json) 2026-07-21 (page last edited) |
✓ Like-for-like: model-size class unknown, batch 8 (size or batch not stated by source — unverified). Model, precision and stack may still differ.
| Platform | Result | Model · precision · stack | Grade | Power | Source |
|---|---|---|---|---|---|
| NVIDIA Jetson AGX Thor | 293 tok/s generation_throughput · b=8 | Cosmos3 Edge (Reasoner) BF16 · vLLM · unknown | C | 130 W spec platform spec envelope (not measured with this result) | NVIDIA Jetson AI Lab benchmarks (benchmarks.json) 2026-07-21 (page last edited) |
| NVIDIA Jetson AGX Orin 64GB | 171 tok/s generation_throughput · b=8 | Cosmos3 Edge (Reasoner) BF16 · vLLM · unknown | C | 65.00 W spec platform spec envelope (not measured with this result) | NVIDIA Jetson AI Lab benchmarks (benchmarks.json) 2026-07-21 (page last edited) |
How to read these numbers
Every number is labelled. Confidence: A MLPerf closed, audited · B documented third-party method · C community / review · D vendor-published, not independently verified · E estimate. Power basis: measured with the result · spec vendor spec/envelope · power n/a not published.
Machine-readable: /find.json (see llms.txt); full tables are in “Show data”. Confidence grades: A audited MLPerf closed · B documented third-party · C community · D vendor-published · E estimate. Unknown values are shown as unknown, never estimated. Generated 2026-10-16 from public sources.