AMD Ryzen AI Max+ 395 / PRO 395 (Strix Halo, Radeon 8060S) vs NVIDIA Jetson AGX Orin 64GB

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NVIDIA Jetson AGX Orin developer kit with its retail box, on a black background — NVIDIA Jetson AGX Orin developer kit. Photo: Auledas, CC BY 4.0, via Wikimedia Commons.

Split result: AMD Ryzen AI Max+ 395 / PRO 395 (Strix Halo, Radeon 8060S) is faster in 2 and NVIDIA Jetson AGX Orin 64GB in 2 of 4 like-for-like comparisons.

	AMD Ryzen AI Max+ 395 / PRO 395 (Strix Halo, Radeon 8060S)	NVIDIA Jetson AGX Orin 64GB
Best object detection	476 FPS B 2026 (year only)	435 FPS B undated
Power	120 W spec	65.00 W spec
Perf / W	3.97 FPS/W spec	6.69 FPS/W spec
Price	n/a	$1999 list
Workloads with data	2 of 7	4 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
AMD Ryzen AI Max+ 395 / PRO 395 (Strix Halo, Radeon 8060S)	476 FPS inference_time · b=1	YOLO26n FP32 · Ultralytics 8.4.165 ONNX Runtime MIGraphX EP · nano (YOLO-n)	B	120 W spec platform spec envelope (not measured with this result)	Ultralytics docs: AMD ROCm/MIGraphX integration benchmark 2026 (year only)
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 docs benchmark table undated

instance segmentation

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No published instance segmentation results for AMD Ryzen AI Max+ 395 / PRO 395 (Strix Halo, Radeon 8060S) 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.

pose estimation

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No published pose estimation results for AMD Ryzen AI Max+ 395 / PRO 395 (Strix Halo, Radeon 8060S) 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.

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) 2026-10

AMD Ryzen AI Max+ 395 / PRO 395 (Strix Halo, Radeon 8060S):

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No published image classification results for AMD Ryzen AI Max+ 395 / PRO 395 (Strix Halo, Radeon 8060S) 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

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No published semantic segmentation results for AMD Ryzen AI Max+ 395 / PRO 395 (Strix Halo, Radeon 8060S) 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) 2026-10

AMD Ryzen AI Max+ 395 / PRO 395 (Strix Halo, Radeon 8060S):

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No published speech-to-text results for AMD Ryzen AI Max+ 395 / PRO 395 (Strix Halo, Radeon 8060S) 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 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) undated
AMD Ryzen AI Max+ 395 / PRO 395 (Strix Halo, Radeon 8060S) CPU-only	11.37 tok/s generation_throughput · b=1	DeepSeek R1 14b as in linked issue (Ollama default tags are 4-bit) · Ollama / llama.cpp (see linked issue) · LLM 5–14B	C	140 W measured reported in result source — measured	Jeff Geerling ai-benchmarks README (Findings tables) undated

✓ 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 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) undated
AMD Ryzen AI Max+ 395 / PRO 395 (Strix Halo, Radeon 8060S)	47.50 tok/s generation_throughput · b=1	Qwen3-Coder 30B (MoE, 3.3B active) not stated per model · Ollama (OpenAI-compatible endpoint) · LLM 15–40B	C	120 W spec platform spec envelope (not measured with this result)	Adam Taylor (Adiuvo Engineering) (manual extraction) undated

✓ 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
AMD Ryzen AI Max+ 395 / PRO 395 (Strix Halo, Radeon 8060S)	88.14 tok/s generation_throughput · b=1	Llama 3.2:3b as in linked issue (Ollama default tags are 4-bit) · Ollama / llama.cpp (see linked issue) · LLM ≤4B	C	133 W measured reported in result source — measured	Jeff Geerling ai-benchmarks README (Findings tables) undated
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) undated

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.