AI chip · NVIDIA
NVIDIA Jetson AGX Thor
Blackwell-based embedded AI compute module aimed at humanoid robots and autonomous machines; positioned as the successor to Jetson Orin.
On the DEPLOY graph: 11 robots on record adopt this chip.
Market position
AGX Thor is the humanoid successor to Orin: 2,070 TFLOPS FP8 in a 130 W module. NVIDIA's GR00T ecosystem is anchored on it. Every 2025-2026 humanoid launch (Apollo 2, Aeon, Astribot S1, Booster T2) targets Thor.
DEPLOY editorial. What the vendor PDF cannot tell you.
Physical-AI cross-link
Derived from claims already on record (vendor first-party TDP, throughput, price, and the reference-design cluster registry). The arithmetic is shown per row so it can be audited. No estimated inputs.
- Perf per watt
- 15.92 FP8 TFLOPS/W2,070 TFLOPS ÷ 130 W = 15.92
What fits in 128 GB
Weights-only footprint for public open-weight LLMs. Pure arithmetic: params × bytes/param. Excludes KV cache and activation memory; add ~10-30% headroom for real serving. A model that does not fit at FP16 may still fit at INT8 or INT4 with quality trade-offs. Not a benchmark.
| Model | Params | FP16 | INT8 | INT4 |
|---|---|---|---|---|
| Llama 3.1 8B dense | 8 B | 16 GB ✓ | 8 GB ✓ | 4 GB ✓ |
| Llama 3.1 70B dense | 70 B | 140 GB ✗ | 70 GB ✓ | 35 GB ✓ |
| Llama 3.1 405B dense | 405 B | 810 GB ✗ | 405 GB ✗ | 203 GB ✗ |
| Llama 3.3 70B dense | 70 B | 140 GB ✗ | 70 GB ✓ | 35 GB ✓ |
| DeepSeek V3 MoE (671B total, 37B active per token) | 671 B | 1342 GB ✗ | 671 GB ✗ | 336 GB ✗ |
| DeepSeek R1 MoE (671B total, 37B active per token) | 671 B | 1342 GB ✗ | 671 GB ✗ | 336 GB ✗ |
| Qwen 2.5 7B dense | 7 B | 14 GB ✓ | 7 GB ✓ | 4 GB ✓ |
| Qwen 2.5 72B dense | 72 B | 144 GB ✗ | 72 GB ✓ | 36 GB ✓ |
| Mixtral 8x7B MoE (46.7B total, 12.9B active per token) | 46.7 B | 93 GB ✓ | 47 GB ✓ | 23 GB ✓ |
| Mixtral 8x22B MoE (141B total, 39B active per token) | 141 B | 282 GB ✗ | 141 GB ✗ | 71 GB ✓ |
| Gemma 2 27B dense | 27 B | 54 GB ✓ | 27 GB ✓ | 14 GB ✓ |
| Command R+ dense | 104 B | 208 GB ✗ | 104 GB ✓ | 52 GB ✓ |
| Kimi K2 MoE (1T total, 32B active per token) | 1000 B | 2000 GB ✗ | 1000 GB ✗ | 500 GB ✗ |
Math: FP16 = params × 2 bytes; INT8 = params × 1 byte; INT4 = params × 0.5 bytes. MoE models sum every expert (full weights on disk), not the per-token active subset.
Common questions
Answer-first, sourced. Every claim below traces to a specific field on this page or a cited datasheet. FAQPage schema is emitted so LLM crawlers can lift these verbatim.
How much does NVIDIA Jetson AGX Thor cost?
NVIDIA Jetson AGX Thor launch pricing is not on record in the DEPLOY registry. Vendor list prices are typically published in the vendor's own briefing rather than the datasheet.
How much memory does NVIDIA Jetson AGX Thor have?
128 GB of LPDDR5X at 273 GB/s. Vendor datasheet, first-party.
How much power does one NVIDIA Jetson AGX Thor draw?
130 W TDP (thermal design power) per chip. System-level draw is higher: see the reference-design section for rack-level kW.
What runs on NVIDIA Jetson AGX Thor?
11 robot models on the DEPLOY registry adopt this chip, including Nuro R3, Apollo, Figure 03, and 8 more. Full list below.
Which open-weight LLMs fit on one NVIDIA Jetson AGX Thor?
Of the 13 public open-weight LLMs DEPLOY tracks: 4 fit at FP16, 8 at INT8, 9 at INT4 (weights only, excludes KV cache). The full table is above with per-model math. Sparsity, offloading and multi-GPU serving change the picture; this row is single-chip weights-only.
Key facts
- Class
- Compute SoC (AI accelerator)
- Designer
- NVIDIA
- Safety-critical?
- Yes (embedded / automotive)
- Record as of
- 2026-08-25
Specifications (9 fields, click to expand)
Vendor datasheet figures (first-party). Dense throughput first; sparse (2:4) in parentheses where NVIDIA quotes it. The exhaustive spec sheet lives on the datasheet URL below the table: this row set covers what buyers actually compare on.
- Process node
- TSMC 4NP
- TDP
- 130 W
- Memory
- 128 GB LPDDR5X
- Memory bandwidth
- 273 GB/s
- FP8 (dense)
- 2,070 TFLOPS
- INT8 (dense)
- 2,070 TOPS
- Form factor
- Module (AGX Thor)
- Announced
- 2024-03-18
- Released
- 2025-08-01
Source: vendor datasheet
Generation
Compare with
Robot models running NVIDIA Jetson AGX Thor
Every model on the DEPLOY registry with a public chip-integration claim for NVIDIA Jetson AGX Thor. Ranked by verified deployment count.
| Model | Maker | Form factor | Role | Source | Deployments |
|---|---|---|---|---|---|
| Nuro R3 | Nuro | av | primary inference compute | reported | 9 |
| Apollo | Apptronik | humanoid | primary inference compute | reported | 6 |
| Figure 03 | Figure AI | humanoid | primary inference compute | reported | 4 |
| Atlas | Boston Dynamics | humanoid | primary inference compute | reported | 3 |
| NEO | 1X Technologies | humanoid | primary inference compute | reported | 3 |
| Apollo 2 | Apptronik | humanoid | onboard perception + planning SoC | reported | 2 |
| AEON | Hexagon | humanoid | onboard humanoid compute | reported | 1 |
| Astra | Apptronik | humanoid | onboard perception + planning SoC | inferred | 0 |
| Astribot S1 | Stardust Intelligence | humanoid | onboard humanoid compute | inferred | 0 |
| Booster T2 | Booster Robotics | humanoid | onboard humanoid compute | inferred | 0 |
| NEO Gamma | 1X Technologies | humanoid | onboard humanoid compute | reported | 0 |
Sources
- Jetson ThorNvidia
Adoption rows appear as we verify chip-integration claims per model. Every row is a public claim the maker or a tier-1 source has stated; verification tier (verified / reported / inferred) is shown per row. See every chip on record for the full catalog or the NVIDIA record.