原文 · 未翻译
Nvidia pitches RTX Spark as the chip that finally makes local AI agents practical on Windows devices
Key Points
Nvidia announced RTX Spark, a Grace Blackwell chip for Windows laptops with up to 128 GB unified memory and 1 petaflop FP4 AI compute, competing directly with Apple Silicon and Qualcomm Snapdragon.
The chip targets local AI agent execution, backed by new security tools like OpenShell Runtime for agent isolation and privacy controls.
Devices from major OEMs including ASUS, Dell, HP, Lenovo, and Microsoft Surface launch fall 2026.
RTX Spark is Nvidia's first move into Windows laptops, designed to run AI agents locally. The hardware is a Windows version of the already-known DGX Spark chip.
Nvidia unveiled RTX Spark at GTC Taipei. At the top end, the chip is the same GB10 Grace Blackwell Superchip that powers the DGX Spark. The difference is who it's for. Instead of a Linux workstation aimed at AI developers, RTX Spark targets Windows laptops and compact desktops for consumers. Nvidia is offering several variants with different core and SM counts, plus memory options ranging from 16 to 128 GB.
The top SKU pairs a Blackwell RTX GPU with 6,144 CUDA cores and fifth-gen Tensor Cores with a 20-core Arm-based Grace CPU, linked via NVLink-C2C. MediaTek helped design the CPU, according to Nvidia. Memory tops out at 128 GB, shared between CPU and GPU. The claimed 1 petaflop peak refers to FP4 precision with sparsity, a theoretical best case per Nvidia's specs. GPU performance sits close to a GeForce RTX 5070 Laptop GPU depending on the workload, Nvidia says.
Nvidia's answer to Apple Silicon and Snapdragon
RTX Spark follows the path Apple charted in 2020 with its M-series chips: Arm CPU, GPU, and memory controller on one package, sharing a unified memory pool instead of separate VRAM. Apple's M4 Max also offers up to 128 GB of unified memory at 546 GB/s bandwidth, but its Neural Engine tops out at 38 TOPS (INT8). RTX Spark claims roughly 1,000 TOPS by comparison, though that's FP4 with sparsity, so the conditions are very different. Still, the gap in raw AI compute is significant. Nvidia's real edge remains its CUDA stack, including TensorRT and RTX, which runs natively.