Apple M4 (10-core GPU, 120 GB/s) vs NVIDIA Jetson AGX Thor

Apple Mac mini (M4, 2024), small silver aluminium desktop, front view — Apple Mac mini (M4, 2024) — the M4 Pro model shares this enclosure. Photo: Seasider53, CC BY 4.0, via Wikimedia Commons.

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NVIDIA Jetson AGX Thor is faster in all 3 like-for-like comparisons (largest gap: LLM generation, 2.95×).

	Apple M4 (10-core GPU, 120 GB/s)	NVIDIA Jetson AGX Thor
Best object detection	357 FPS B 2026-10-01 (page last edited)	719 FPS B 2026-10-13 (page last edited)
Power	n/a	130 W spec
Perf / W	n/a	5.53 FPS/W spec
Price	n/a	$5499 list
Workloads with data	6 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)
Apple M4 (10-core GPU, 120 GB/s)	357 FPS latency_e2e · b=1	YOLO26n FP16 · Ultralytics 8.4.168 Core AI (coreai-torch 0.4.3), macOS 27.0 · nano (YOLO-n)	B	unknown power n/a no power published	Ultralytics documentation: Apple Core AI integration benchmark 2026-10-01 (page last edited)

instance segmentation

Only Apple M4 (10-core GPU, 120 GB/s) has published instance segmentation results.

Platform	Result	Model · precision · stack	Grade	Power	Source
Apple M4 (10-core GPU, 120 GB/s)	196 FPS latency_e2e · b=1	YOLO26n-seg FP16 · Ultralytics 8.4.168 Core AI (coreai-torch 0.4.3), macOS 27.0 · nano (YOLO-n)	B	unknown power n/a no power published	Ultralytics documentation: Apple Core AI integration benchmark 2026-10-01 (page last edited)

NVIDIA Jetson AGX Thor:

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No published instance segmentation results for NVIDIA Jetson AGX Thor or Apple M4 (10-core GPU, 120 GB/s) yet.
We only show cited public figures — nothing is estimated to fill gaps. Requests tell us what to source or test next.

pose estimation

Only Apple M4 (10-core GPU, 120 GB/s) has published pose estimation results.

Platform	Result	Model · precision · stack	Grade	Power	Source
Apple M4 (10-core GPU, 120 GB/s)	370 FPS latency_e2e · b=1	YOLO26n-pose FP16 · Ultralytics 8.4.168 Core AI (coreai-torch 0.4.3), macOS 27.0 · nano (YOLO-n)	B	unknown power n/a no power published	Ultralytics documentation: Apple Core AI integration benchmark 2026-10-01 (page last edited)

NVIDIA Jetson AGX Thor:

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No published pose estimation results for NVIDIA Jetson AGX Thor or Apple M4 (10-core GPU, 120 GB/s) 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 Apple M4 (10-core GPU, 120 GB/s) has published image classification results.

Platform	Result	Model · precision · stack	Grade	Power	Source
Apple M4 (10-core GPU, 120 GB/s)	417 FPS latency_e2e · b=1	YOLO26n-cls FP16 · Ultralytics 8.4.168 Core AI (coreai-torch 0.4.3), macOS 27.0 · nano (YOLO-n)	B	unknown power n/a no power published	Ultralytics documentation: Apple Core AI integration benchmark 2026-10-01 (page last edited)

NVIDIA Jetson AGX Thor:

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No published image classification results for NVIDIA Jetson AGX Thor or Apple M4 (10-core GPU, 120 GB/s) 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 Apple M4 (10-core GPU, 120 GB/s) has published semantic segmentation results.

Platform	Result	Model · precision · stack	Grade	Power	Source
Apple M4 (10-core GPU, 120 GB/s)	53.48 FPS latency_e2e · b=1	YOLO26n-sem FP16 · Ultralytics 8.4.168 Core AI (coreai-torch 0.4.3), macOS 27.0 · nano (YOLO-n)	B	unknown power n/a no power published	Ultralytics documentation: Apple Core AI integration benchmark 2026-10-01 (page last edited)

NVIDIA Jetson AGX Thor:

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No published semantic segmentation results for NVIDIA Jetson AGX Thor or Apple M4 (10-core GPU, 120 GB/s) 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

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No published speech-to-text results for Apple M4 (10-core GPU, 120 GB/s) 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
Apple M4 (10-core GPU, 120 GB/s)	24.11 tok/s tg128 · b=1	Llama 2 7B Q4_0 · llama.cpp Metal (commit 8e672ef) · LLM 5–14B	C	unknown power n/a no power published	llama.cpp discussion #4167 scoreboard (via GitHub API) 2026-10-01 (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)
Apple M4 (10-core GPU, 120 GB/s)	41.31 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	30.10 W measured reported in result source — measured	Jeff Geerling ai-benchmarks README (Findings tables) 2026-09-22 (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.