PCMag introduces AI PC benchmarking tests for local AI hardware
PCMag has launched its first AI hardware test suite to evaluate local AI performance across three device tiers. The tests use MLPerf Client and LMStudio, focusing on response speed and token generation rates. Class 1 systems (basic NPU laptops) run Llama 3.1 8B Instruct, while Class 2 (workstations with discrete GPUs) tests Phi 4 Reasoning 14B. Class 3 (developer-grade PCs) uses Qwen 3.5 122B…
Key points
- PCMag’s test suite uses MLPerf Client 1.6.1 and LMStudio with Llama 3.1 8B, Phi 4 Reasoning 14B, and Qwen 3.5 122B A10B-GGUF models
- Class 1 (NPU laptops) needs ≥16GB RAM, Class 2 (≥32GB RAM + discrete GPU) tests larger models, Class 3 (≥96GB RAM) runs 122B-parameter models
- Metrics measure TTFT and TPS, but exclude response quality or accuracy—focused solely on speed
The initiative follows Nvidia’s DGX Spark (1,000 TOPS) and RTX Spark laptops, which blend AI acceleration with unified memory. PCMag’s team, including Matthew Murray and Scott Wasson of Damage Labs, designed tests to adapt to evolving hardware and models. Metrics like Time to First Token (TTFT) and Tokens Per Second (TPS) track speed, though quality and accuracy remain unmeasured. Future tests may include power efficiency and agentic workflows, but the current focus is on repeatable benchmarks for AI PCs.
How We Test AI PCs: Benchmarking Local AI as It Moves From the Data Center to Your Desk
pcmag.com · 4 November 2026
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