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NVIDIA Announces RTX Spark Laptops and New AI Model Updates

According to NVIDIA. NVIDIA unveiled RTX Spark Windows laptops featuring an integrated superchip with up to 128GB of unified memory, alongside new speech models, resource controls, and gaming updates.

By TigerIQ News DeskPublished 10/28/2026, 10:18 PM5 min readShare · LinkedInShare · Email
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NVIDIA Announces RTX Spark Laptops and New AI Model Updates
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KEY TAKEAWAYS
  • • RTX Spark laptops combine an NVIDIA Blackwell RTX GPU and Grace CPU into a single superchip.
  • • NVIDIA Nemotron 3.5 Transcribe is an open weight speech model supporting transcription, speaker attribution, and translation.
  • • New resource management controls in the NVIDIA App allow users to allocate GPU resources between apps and background agents.

RTX Spark Hardware

NVIDIA RTX Spark powers thin and light laptops built for developers, creators, and gamers. These systems combine an NVIDIA Blackwell RTX GPU and an NVIDIA Grace CPU in a single superchip with up to 128GB of unified memory and up to 1 petaflop of FP4 AI performance. RTX Spark laptops are now available from ASUS, Dell, HP, Lenovo, Microsoft, and MSI, starting at $2,599.

Nemotron 3.5 Transcribe

Introduced by NVIDIA, Nemotron 3.5 Transcribe is a new, open weight speech model that turns recorded conversations into useful information. Instead of needing multiple models for transcription, speaker labels, and translation, developers can use one promptable model that handles all three across 40 languages. It features approximately 4.3 billion active parameters and is available today on Hugging Face.

GPU Resource Controls

On RTX Spark, users can have long-running agents working alongside their favorite applications while sharing system GPU compute. With the new resource management feature in NVIDIA App, users can choose how GPU resources are shared by prioritizing their foreground application for best performance or giving background agents more resources to work faster.

SOURCES & METHODOLOGY
  1. 1. blogs.nvidia.com
    Open source
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