13B-class quantized models (Llama 3 13B, Mistral, Qwen 14B) need ~10–14GB resident plus headroom for context. These laptops clear the AIPC 13B-Q4 profile at usable tokens/sec.
Workstation-class — 128GB unified memory for 70B local
Why: 128GB unified LPDDR5X — runs 70B Q4 locally on iGPU
Best for Local LLMs (unified memory)
Why: Unified memory lets 70B-class quantized models stay resident
Top GPU for 70B local inference
Why: RTX 5090 with 24GB VRAM runs 70B Q4 locally
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Workstation-class — 128GB unified memory for 70B local
AMD Ryzen AI Max+ 395 (Strix Halo)
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Best for Local LLMs (unified memory)
Apple M4 Max (16-core CPU, 40-core GPU)
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Top GPU for 70B local inference
AMD Ryzen AI 9 HX 370 + NVIDIA RTX 5090
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Get the chip-level breakdown — NPU TOPS, sustained thermals, tokens/sec — and compare any two of these laptops side-by-side on the AIPC engine.
Two top-tier laptops, two completely different routes to local LLM throughput. Apple's unified memory vs Razer's discrete GPU — here's the AIPC verdict.
Read the full comparisonRun your real workload through the AIPC engine and get a chip-level shortlist matched to your budget, RAM needs, and battery requirements.
About 9GB for Q4 weights plus 4–6GB for context. 16GB systems work; 32GB is comfortable.
MacBook Pro M4 Max leads at ~78 tok/s on Llama 3 13B Q4. Razer Blade 16 with RTX 5090 reaches similar speeds via CUDA.
Currently NPUs handle small models (~1–4B) well; 13B still runs better on the GPU or unified memory. NPUs accelerate Copilot+ features, not full LLM inference.