Aetina AIE-KT78/68 brings Jetson Thor Blackwell compute to deterministic edge AI control

Aetina launches AIE-KT78 and AIE-KT68 edge AI systems on NVIDIA Jetson Thor, with QSFP28, 8× GMSL2, and an isolated EtherCAT port for cobots, humanoids, and AMRs.

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TL;DR — 세 문장

  • Aetina announced the AIE-KT78 (Jetson T5000) and AIE-KT68 (Jetson T4000), the first Aetina edge AI systems on the NVIDIA Jetson Thor Blackwell platform. (S1)
  • Top SKUs deliver up to 2,070 FP4 TFLOPS (T5000) and 1,200 FP4 TFLOPS (T4000) with 128 GB and 64 GB of LPDDR5X on a 256-bit interface. (S1)
  • A dedicated 1GbE RJ45 EtherCAT master port is isolated from the Linux data plane, targeting deterministic multi-axis robot control. (S1)

출처: key_takeaways (실제 필드, 최대 3개 항목). 사실과 분석은 별도로 표시됩니다.

Aetina AIE-KT78/68 brings Jetson Thor Blackwell compute to deterministic edge AI controlEditorial cover for a component sourcing brief. Chip motif; headline "Aetina AIE-KT78/68 brings Jetson Thor Blackwell compute to deterministic edge AI control". Reported key numbers: 2. 1 cited source. Static cover (no animation).Aetina AIE-KT78/68 brings Jetson ThorBlackwell compute to deterministic edgeAI controlREPORTED KEY NUMBERS21 sourceXAIPART
Cover image generated from data already shown on this page: the category motif, the headline, and reported key numbers. No decorative illustration; no third-party image.

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Reported by EDN on . Summarized and analyzed by XAIPART Editorial.

What happened

Aetina has introduced the AIE-KT78 and AIE-KT68 edge AI systems, built on the NVIDIA Jetson Thor platform that uses the Blackwell GPU architecture for deterministic industrial control.S1 The systems target collaborative robots (cobots), humanoid robots, and autonomous machines, and they run multimodal AI models locally rather than relying on the cloud, reducing the latency that comes with off-device inference.S1 Supported local workloads include generative AI, large language models (LLMs), vision-language models (VLMs), and vision-language-action models (VLAs).S1

The AIE-KT78 uses the Jetson T5000 system-on-module with 128 GB of LPDDR5X on a 256-bit interface, delivering up to 2,070 FP4 TFLOPS of AI compute. The AIE-KT68 uses the Jetson T4000 module with 64 GB of LPDDR5X on the same 256-bit interface and up to 1,200 FP4 TFLOPS. Both platforms run Linux with the full NVIDIA AI software stack.S1

For connectivity, each system provides QSFP28 with up to 4×25-Gbps bandwidth, dual 10GbE RJ45 ports, and support for up to eight GMSL2 camera inputs aggregating data from cameras, LiDAR, radar, and other sensors. A dedicated 1GbE RJ45 port is reserved as an independent EtherCAT master, synchronizing motors, joints, sensors, and actuators for deterministic control loops.S1

Key numbers reported by EDN
MetricReported valueSource
AIE-KT78 AI computeUp to 2,070 FP4 TFLOPS[S1]
AIE-KT78 memory128 GB LPDDR5X (256-bit)[S1]
AIE-KT68 AI computeUp to 1,200 FP4 TFLOPS[S1]
AIE-KT68 memory64 GB LPDDR5X (256-bit)[S1]
QSFP28 bandwidthUp to 4×25 Gbps[S1]
GMSL2 camera inputsUp to 8[S1]
10GbE RJ45 ports2[S1]
EtherCAT master port1× dedicated 1GbE RJ45[S1]
OS / software stackLinux + full NVIDIA AI stack[S1]
PriceNot disclosed[S1]

Background timeline

  1. No prior milestones disclosed by the source.

XAIPART take

Technical impact

The AIE-KT78/68 is the first Aetina platform we have seen pair Jetson Thor’s Blackwell-class FP4 throughput with a hard-isolated 1GbE EtherCAT master on the same chassis, which is the topology robot integrators need to keep real-time motion control outside the Linux scheduler’s worst-case jitter.S1 Eight GMSL2 lanes plus a 4×25G QSFP28 fabric leave headroom for surround camera rigs and high-rate LiDAR fusion, and the 256-bit LPDDR5X channels (128 GB on the T5000 SKU, 64 GB on the T4000 SKU) are well matched to VLA and VLM inference at production frame rates.XAIPART analysis

Market impact

By targeting cobots, humanoids, and AMRs on a single SKU family, Aetina is positioning the Jetson Thor T5000/T4000 as a component-level alternate to x86 GPU edge boxes for robotics OEMs that want to consolidate perception, planning, and actuation on one compact platform.S1 For buyers already on Jetson Orin, the Thor upgrade more than doubles FP4 throughput per watt on paper, but the BOM delta versus the predecessor (T5000 vs AGX Orin 64/100 GB) will determine how fast humanoid and AMR programs re-spec their compute trays.XAIPART analysis

Action

Engineering teams piloting VLAs, AMRs, or vision-guided cobots should request an Aetina AIE-KT78 evaluation kit and validate end-to-end motion loop stability under worst-case sensor load before re-spending on legacy Orin-based designs. Procurement should treat the T5000 and T4000 SKUs as two separate allocations, since their 128 GB and 64 GB memory footprints map to different VLM/VLA batch-size ceilings.XAIPART analysis

XAIPART Forecast

XAIPART expects that, within 9 months of , Aetina will publish lead-time and unit pricing for the Jetson T5000-based AIE-KT78 and that published lead times will fall to 8 weeks or less for orders placed by ; if lead times remain above 12 weeks at that date this forecast is wrong. The metric (lead time in weeks) is industry-checkable against Aetina’s distributor channel and references the AIE-KT78 platform named in this brief. The threshold is XAIPART’s expectation, not a figure reported by the source.XAIPART analysis

Facts are as reported by the cited source. The design-in, sourcing, and forward-looking statements above are XAIPART’s analysis, not the source’s claims. Pricing, lead time, and availability for the AIE-KT78/68 were not disclosed by EDN; the product-page link points to Aetina and was not verified by XAIPART in this brief.

Corrections: none at publication. This brief will be updated with a timestamp if the source corrects its report.

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XAIPART expects that, within 9 months of October 14, 2026 , Aetina will publish lead-time and unit pricing for the Jetson T5000-based AIE-KT78 and that published lead times will fall to 8 weeks or less for orders plac... lead times remain above 12 weeks at that date Pending

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Source: FRED · Generated · reference output z and composite are algorithm outputs, not a fetch snapshot

Cost pressure by dimension — composite stateDiverging bars for three dimensions around a zero midpoint. Input Cost has no reading; Supply Demand z -1.4711, notable; Fx Energy z 0.2546, neutral. Composite index -9.1, band neutral.0Input costno readingSupply/demandz −1.4711 · notable46 dFX & energyz +0.2546 · neutral5 dcomposite −9.1 · neutral−100 · −40 · −15 · 0 · +15 · +40 · +100
Underlying values — copyable for verification.
DimensionFRED series IDUnitLagAs of
Input costPCOPPUSDM, PALUMUSDM, PNICKUSDMUSD/mt52 d
Supply/demandPCU334413334413, IPG3344Sindex(1982=100) / index(2017=100)46 d
FX & energyDEXCHUS, DCOILBRENTEU, DHHNGSPrate / USD/bbl / USD/MMBtu5 d

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Model / prompt MiniMax-M3 · prompt Blog Agent - News Summary (v2.8-final) · 7,049 tokens · 31.4s · run completed
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출처

  1. Systems reduce latency in edge AI automation — https://www.edn.com/systems-reduce-latency-in-edge-ai-automation/ · rss · 조회일

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