Nvidia announced the RTX Spark, a Grace Blackwell chip designed for Windows laptops, aiming to enable practical local AI agent execution. The chip features up to 128 GB of unified memory and 1 petaflop FP4 AI compute, competing directly with Apple Silicon and Qualcomm Snapdragon. RTX Spark targets Windows laptops and compact desktops for consumers, with variants offering different core and SM counts, plus memory options 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. 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. *Source: [thedecoder](https://the-decoder.com/nvidia-pitches-rtx-spark-as-the-chip-that-finally-makes-local-ai-agents-practical-on-windows-devices/)*