Urgent.News

What's breaking now, across thousands of outlets.

AI

I asked Nvidia Spark's AI agent to run Blender for me – here's what happened

I'm a beginner, so how did I get on?

I asked Nvidia Spark's AI agent to run Blender for me – here's what happened

Welcome to the benchmark thunderdome. Nvidia RTX Spark is here, and I'm allowed to share my week-long testing and live benchmarking results with you. This chip is an Arm-based Grace CPU glued to a Blackwell RTX GPU, sharing up to 128GB of unified memory. But let's get straight to the numbers.

First, let's talk CPU performance. In Geekbench 7, the RTX Spark's single-core and multi-core scores are noticeably slower than Apple's M5 Pro and Max chips. It comes closer to Snapdragon X2 Elite Extreme, but still has single-core prowess. Current scores are 2,604 (single-core) and 23,610 (multi-core) for the RTX Spark, compared to 3,690 (single-core) and 29,659 (multi-core) for the Apple M5 Pro.

AMD's Ryzen AI Max 395+ and Intel Core Ultra X9 388H follow at 2,903 (single-core) and 17,223 (multi-core), and 2,574 (single-core) and 17,223 (multi-core), respectively.

Now, let's dive into 3DMark gaming benchmarks. Nvidia's RTX Spark matches up to an RTX 5070 gaming laptop in terms of raw performance. It scores 7,492 in Fire Strike Ultra (x64) and 6,050 in Time Spy Extreme (x64). However, Apple's M5 Pro is n/a for these tests on the 3DMark suite for MacBooks. Battery life is a mixed bag, with Nvidia promising all-day battery life, but it still doesn't come close to Apple's MacBook Pro in efficiency.

Web browsing tests show 10 hours and 15 minutes for the RTX Spark, compared to 21 hours and 25 minutes for the M5 Pro. Gaming and local AI performance tests are in line with expectations from an RTX gaming laptop.

Written by urgent.news from Tom's Guide's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.

This story

This is one outlet's version. Read the fullest account.

Read the original at creativebloq.com →

More in AI

Why Frontend Devs Need MCP: The Universal Plug for AI Apps

Why Frontend Devs Need MCP: The Universal Plug for AI Apps AI is everywhere today. GitHub Copilot writes code, ChatGPT answers queries, and autonomous agents orchestrate complex workflows.

  • Model Context Protocol (MCP) simplifies AI app integration with external tools.
  • Reduces maintenance overhead by exposing tools via a single MCP server.
  • Benefits frontend developers through dynamic tool discovery and cleaner code.

How to Trigger AI Agents from Webhooks: Signature Verification, Retries, and Idempotency

Message me in Slack when Dev finally uploads the Q4 financials. That one line is why webhooks matter for AI agents. Without them, an agent can only act when someone asks it to, or when it is polled on…

  • Verify webhook signature using HMAC SHA256 with shared secret key
  • Check timestamp freshness window (5 minutes) to prevent replay attacks
  • Use idempotency keys to deduplicate retries and ensure single agent action

More from Thursday 15 October →