Intel Core Ultra 7 368H vs NVIDIA Jetson AGX Thor

IntelAI PCNo licensed photo yet

NVIDIAedge referenceNo licensed photo yet

Split result: Intel Core Ultra 7 368H is faster in 3 and NVIDIA Jetson AGX Thor in 1 of 4 like-for-like comparisons.

	Intel Core Ultra 7 368H	NVIDIA Jetson AGX Thor
Best LLM generation	250 tok/s D 2026-10-01 (published)	523 tok/s C 2026-07-21 (page last edited)
Power	n/a	130 W spec
Perf / W	n/a	4.02 tok/s/W spec
Price	n/a	$5499 list
Workloads with data	1 of 7	2 of 7

Best published LLM generation 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

Only NVIDIA Jetson AGX Thor has published object detection results.

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)

Intel Core Ultra 7 368H:

◌

No published object detection results for Intel Core Ultra 7 368H 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.

instance segmentation

◌

No published instance segmentation results for Intel Core Ultra 7 368H 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 Intel Core Ultra 7 368H 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 Intel Core Ultra 7 368H 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

◌

No published semantic segmentation results for Intel Core Ultra 7 368H 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.

speech-to-text

◌

No published speech-to-text results for Intel Core Ultra 7 368H 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
Intel Core Ultra 7 368H	83.33 tok/s 2nd-token throughput, input 32 tokens · b=1	lfm2-8b-a1b INT4-MIXED · OpenVINO 2026.4.1 GenAI · LLM 5–14B	D	unknown power n/a no power published	OpenVINO official LLM benchmarks (llm_models_7-368H.csv) 2026-10-01 (published)
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

✓ 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)
Intel Core Ultra 7 368H	52.36 tok/s 2nd-token throughput, input 32 tokens · b=1	lfm2-24b-a2b INT4-MIXED · OpenVINO 2026.4.1 GenAI · LLM 15–40B	D	unknown power n/a no power published	OpenVINO official LLM benchmarks (llm_models_7-368H.csv) 2026-10-01 (published)

✓ 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
Intel Core Ultra 7 368H	250 tok/s 2nd-token throughput, input 32 tokens · b=1	minicpm4-0.5b INT4-MIXED · OpenVINO 2026.4.1 GenAI · LLM ≤4B	D	unknown power n/a no power published	OpenVINO official LLM benchmarks (llm_models_7-368H.csv) 2026-10-01 (published)
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)

✓ 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
Intel Core Ultra 7 368H	227 tok/s 2nd-token throughput, input 1024 tokens · b=1	gemma-3-270m INT4-MIXED · OpenVINO 2026.4.1 GenAI · unknown	D	unknown power n/a no power published	OpenVINO official LLM benchmarks (llm_models_7-368H.csv) 2026-10-01 (published)
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)

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.