NVIDIA DGX Spark

Headlines · 4

1. 3d
   Axera launches AX8860 AI chip, claims 25% faster LLM inference than DGX Spark

   Axera unveiled its AX8860 series AI chip at the 2026 China International Industry Fair, positioning it as a compute base for running large models at the edge and on-device. The chip pairs a 16-core Arm Cortex-A55 CPU with a self-developed NPU rated at 256 TOPS (native FP4, no sparsity), supports up to 128GB of LPDDR5X memory with 273 GB/s of bandwidth over a 256-bit bus, is built on a 6nm process and offers PCIe 5.0 connectivity. Form factors include a 40W module, a single-slot PCIe card and a single-slot four-chip design that Axera claims reaches 1024 TOPS at roughly 80% scaling efficiency, plus planned Mini PCs and Pocket PCs; software support covers vLLM, SGLang and PyTorch with an OpenAI-compatible API, alongside the newly announced Yuanxi A256S module. Axera and Sipeed claim the chip is 25% faster on prefill and 30% faster on decode than NVIDIA's DGX Spark on Qwen 27B, at close to half the price (DGX Spark lists at $4,999 for 64GB and $6,950 for 128GB) — claims that have not been independently verified.

   Wccftech · 🔥 0
2. 4d
   Modded 4× Tesla V100 rig beats two DGX Sparks and M5 Ultra in local LLM inference

   YouTuber Jun Yamog fitted four 2017-era NVIDIA Tesla V100 cards (32GB HBM2, 900GB/s bandwidth) onto an NVLink carrier board and used milled-down A100 heatsinks to cool them, then benchmarked local inference with a 4-bit quantized Qwen3.8-Flash-Next. The quad-V100 setup processed long prompts roughly 40% faster than two DGX Sparks and about 230% faster than a single M5 Ultra, and generated 100.6 tok/s on short prompts versus 44.5 tok/s for the M5 Ultra, about 121% faster than the DGX Spark pair. A separate eight-GPU RTX PRO 6000 configuration still led most of the runs, and Yamog noted Apple Silicon's gap may largely reflect a lack of software optimization.

   Wccftech · 🔥 0
3. 7d
   Unsloth trains a decision model on Qwen3.5-2B in about 2 minutes on one DGX Spark

   Unsloth shared an Ivan Fioravanti demo showing a "decision model" trained in roughly two minutes on a single NVIDIA DGX Spark using Unsloth, with Qwen3.5-2B as the base model. The post points to Unsloth's documentation page on training your own decision model with Unsloth. The source gives no details on the model's intended use, evaluation results, or training data.

   UnslothAI (X) · 🔥 0
4. Oct 6
   Long-WAM scales world-action model context for real-time robot control

   Long-WAM is a model-system framework for scaling the context of causal world-action models under real-time robot-control constraints; its authors report that longer histories only pay off when the video foundation is pretrained autoregressively. On RoboCasa GR-1, raising context from 0.0 to 19.2 seconds lifted success from 63.3% to 78.7%, while a bidirectionally pretrained initialization showed no net gain, and robot-domain autoregressive pretraining further raised peak success on GR-1 and LIBERO-Long. The paper also reports deployment on RTX 5090, DGX Spark and Jetson AGX Thor, with each action chunk including future-video latent prediction taking 107.4 ms on RTX 5090, plus real-time long-horizon manipulation on Unitree G1 and YAM reaching 95% success on dynamic cup stacking, where it says Pi0.5 and Fast-WAM succeeded in none of 20 trials.

   Hugging Face · Papers · 🔥 0

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