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FlashInfer

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2

COMPANIES · 1 SOURCE · NVIDIA Developer

NVIDIA adds official eight-node DGX Spark clusters with 1 TB unified memory to run frontier models

At GTC Berlin NVIDIA announced official support for eight-node DGX Spark clusters, scaling unified memory to 1 TB and enabling local execution of frontier models including the full GLM 5.3 (753B) from Z.ai. NVIDIA also introduced a Spark-optimized collective backend called RoCEnante (merged into FlashInfer) that reduces all-reduce latency—reporting a 4.43x decode speedup at eight nodes (vs 3.05x without it)—and published benchmarks (e.g., Qwen3.8-27B NVFP4 scaling from ~12 to 52 tokens/sec, GLM 5.3 ~80 TPS, and 7.79x pretraining speedup at 97.4% efficiency for a nanochat model); NeMo Switchyard and vLLM integration are highlighted for multi-model routing and distributed inference, and NVIDIA Sync eight-node support is due later this year.

7.0

RESEARCH · 1 SOURCE · NVIDIA Developer

NVIDIA and Dao AI Lab publish ReplaySSM to speed SSM-based hybrid-model decoding

NVIDIA and the Dao AI Lab (Princeton) released ReplaySSM (paper on arXiv and code on GitHub), a technique that caches recent SSM inputs instead of full state to halve state memory traffic and speed SSM decoding kernels by ~1.46–1.80× (batch 256 on Blackwell Ultra B300). ReplaySSM also simplifies speculative-decoding rollback (1.85–2.10× higher speculative throughput on 120B-class models) and enables mixed-precision state (three of four tested models stay within ~2 percentage points of FP32 on GPQA‑Diamond); it's integrated into TensorRT‑LLM, vLLM, and FlashInfer and runs in production in GLM‑5.3‑Flash and Kimi‑K3.

8.0