Intel Core Ultra 7 258V (Lunar Lake) vs NVIDIA Jetson AGX Thor

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Split result: Intel Core Ultra 7 258V (Lunar Lake) is faster in 3 and NVIDIA Jetson AGX Thor in 2 of 5 like-for-like comparisons.

	Intel Core Ultra 7 258V (Lunar Lake)	NVIDIA Jetson AGX Thor
Best object detection	300 FPS C 2026 (year only)	719 FPS B undated
Power	n/a	130 W spec
Perf / W	n/a	5.53 FPS/W spec
Price	n/a	$3499 list
Workloads with data	2 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 docs benchmark table undated
Intel Core Ultra 7 258V (Lunar Lake)	300 FPS inference_time · b=1	YOLO26n FP32 · Ultralytics + OpenVINO 2026.2.1 · nano (YOLO-n)	C	unknown power n/a no power published	Ultralytics docs: Intel OpenVINO integration benchmark 2026 (year only)

instance segmentation

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No published instance segmentation results for Intel Core Ultra 7 258V (Lunar Lake) 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

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No published pose estimation results for Intel Core Ultra 7 258V (Lunar Lake) 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

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No published image classification results for Intel Core Ultra 7 258V (Lunar Lake) 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

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No published semantic segmentation results for Intel Core Ultra 7 258V (Lunar Lake) 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

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No published speech-to-text results for Intel Core Ultra 7 258V (Lunar Lake) 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 258V (Lunar Lake)	89.29 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_7-258V.csv) undated
NVIDIA Jetson AGX Thor	45.00 tok/s tok/s gen, concurrency 1 · b=1	Qwen2.5-VL 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 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 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) undated
Intel Core Ultra 7 258V (Lunar Lake)	53.48 tok/s 2nd-token throughput, input 1024 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_7-258V.csv) 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
Intel Core Ultra 7 258V (Lunar Lake)	135 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_7-258V.csv) undated
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) undated

✓ 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 258V (Lunar Lake)	200 tok/s 2nd-token throughput, input 32 tokens · b=1	lfm2.5-350m INT8-CW · OpenVINO 2026.4.1 GenAI · unknown	D	unknown power n/a no power published	OpenVINO official LLM benchmarks (llm_models_7-258V.csv) undated
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) 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.