Dell Pro Max with GB10 vs NVIDIA Jetson AGX Thor
Split result: Dell Pro Max with GB10 is faster in 1 and NVIDIA Jetson AGX Thor in 3 of 4 like-for-like comparisons.
| Dell Pro Max with GB10 | NVIDIA Jetson AGX Thor | |
|---|---|---|
| Best LLM generation | 97.93 tok/s C 2026-09-22 (page last edited) | 523 tok/s C 2026-07-21 (page last edited) |
| Power | 129 W measured | 130 W spec |
| Perf / W | 0.762 tok/s/W measured | 4.02 tok/s/W spec |
| Price | n/a | $5499 list |
| Workloads with data | 2 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
| 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) |
| Dell Pro Max with GB10 MIXED: model-size class unknown vs nano (YOLO-n); batch unknown vs 1 | 78.73 FPS throughput_offline · b=unknown | MLPerf yolo-95 fp4 · CUDA 13.0 · unknown | A | unknown power n/a no power published | MLPerf Inference v6.1, closed division, edge suite 2026-09-16 (published) |
No shared like-for-like group exists for this pair.
instance segmentation
No published instance segmentation results for Dell Pro Max with GB10 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 Dell Pro Max with GB10 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 Dell Pro Max with GB10 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 Dell Pro Max with GB10 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 Dell Pro Max with GB10 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
| 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 |
| Dell Pro Max with GB10 | 23.92 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 | 138 W measured reported in result source — measured | Jeff Geerling ai-benchmarks README (Findings tables) 2026-09-22 (page last edited) |
| Platform | Result | Model · precision · stack | Grade | Power | Source |
|---|---|---|---|---|---|
| NVIDIA Jetson AGX Thor | 15.00 tok/s generation_throughput · b=1 | Nemotron 3 Super 120B-A12B NVFP4 · vLLM · LLM >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) |
| Dell Pro Max with GB10 | 4.71 tok/s generation_throughput · b=1 | Llama 3.1:70b as in linked issue (Ollama default tags are 4-bit) · Ollama / llama.cpp (see linked issue) · LLM >40B | C | 156 W measured reported in result source — measured | Jeff Geerling ai-benchmarks README (Findings tables) 2026-09-22 (page last edited) |
| Platform | Result | Model · precision · stack | Grade | Power | Source |
|---|---|---|---|---|---|
| Dell Pro Max with GB10 | 87.97 tok/s generation_throughput · b=1 | gpt-oss 20b as in linked issue (Ollama default tags are 4-bit) · Ollama / llama.cpp (see linked issue) · LLM 15–40B | C | 123 W measured reported in result source — measured | Jeff Geerling ai-benchmarks README (Findings tables) 2026-09-22 (page last edited) |
| 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) |
| 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) |
| Dell Pro Max with GB10 | 97.93 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 | 129 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
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.