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On October 21, 2026, NVIDIA showcased three local AI hardware lines—RTX Spark, DGX Spark, and DGX Station—at GTC Berlin, and announced multiple software updates. Official information shows that DGX Spark 64GB goes on sale on October 23; NVIDIA Sync will support clusters of up to eight DGX Sparks and will add Model Launcher this month; DGX Station for Windows is expected to launch in December; the Recursive Flint local coding model is released with open-source Apache 2.0 weights. A follow-up report on October 23 stated that DGX Spark officially supports eight-node clusters, with unified memory expandable to 1TB, and can locally run frontier models such as the full Z.ai GLM 5.3 (753B); official benchmarks show that Qwen3.8-27B (NVFP4) decoding throughput increases from about 12 tokens/s on a single node to 52 tokens/s on eight nodes, with eight nodes achieving a 4.43x decoding speedup, and nanochat pretraining achieving a 7.79x speedup with 97.4% scaling efficiency. NVIDIA Sync's eight-node cluster support will be available before the end of the year.
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NVIDIA showcases its full line of local AI hardware products at GTC Berlin
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Oct 23
NVIDIA RTX SparkRelated discussion
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NVIDIA Developer · Generative AIFirst-party
NVIDIA DGX Spark supports eight-node clusters, unified memory expands to 1TB
At GTC Berlin, NVIDIA announced that DGX Spark officially supports eight-node clusters, with unified memory expanding to 1TB, enabling local execution of frontier models such as Z.ai's full GLM 5.3 (753B). Official benchmarks show that Qwen3.8-27B (NVFP4) decoding throughput rises from about 12 tokens/s on a single node to 52 tokens/s on eight nodes, and with the Spark-optimized RoCEnante collective communication backend, achieves a 4.43x decoding speedup on eight nodes. From-scratch pretraining of the nanochat model on eight nodes achieved a 7.79x speedup and 97.4% scaling efficiency. NVIDIA Sync's eight-node cluster support will launch before the end of the year.
Oct 22
AYi AI Notes
NVIDIA GTC Berlin Finale: Hermes Agent Bridges the Gap to Robot Deployment
In the finale of his GTC Berlin 2026 keynote, Jensen Huang did not simply show off new GPU benchmark scores; instead, he used the open-source agent Hermes to demonstrate a fully automated pipeline that goes from a one-sentence instruction straight to deployment on a physical robot.
Oct 21
nvidia_newsroomFirst-party
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