Run Llama 3.1, Mistral and Qwen entirely on-device. Memory bandwidth, NPU TOPS and quantization efficiency matter more than clock speed — and AIPC measures all three.
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
Type your workload — coding, travel, local LLM, students — and get an AIPC-ranked shortlist with a direct link to each laptop's AIPC profile.
Local LLM throughput is bound by memory bandwidth and quantization efficiency, not clock speed. Apple unified memory and NVIDIA discrete GPUs dominate — Copilot+ NPUs help with small models only.
At Q4 quantization, weights consume ~0.65 GB per billion params. Add 4–8 GB for context window.
Apple M4 Max: 546 GB/s unified. RTX 5090 mobile: 896 GB/s VRAM. x86 LPDDR5X: ~120 GB/s — the bottleneck for non-Apple integrated systems.
MLX (Apple), llama.cpp CUDA (NVIDIA), and Vulkan (AMD/Intel) all work — but MLX and CUDA lead on tokens/sec by a wide margin.
Long generations push the chip for minutes. Throttling cuts tokens/sec 30–50%; vapor chambers and 28W+ TDPs matter.
MacBook Pro M4 Max with 64 GB runs Llama 3 70B Q4 at ~78 tok/s in MLX — silently, on battery. Razer Blade 16 with RTX 5090 hits ~110 tok/s on the same model via CUDA, but plugged in, with fans audible. Pick Apple for portability and silent inference; pick NVIDIA for raw throughput and CUDA-only research stacks.
Today's 40–50 TOPS NPUs (Lunar Lake, Snapdragon X, Ryzen AI) accelerate Copilot+ features and small models (~1–4B params), not 13B+ inference. NPU acceleration of larger LLMs is a 2026–2027 software story — for now, GPU and unified memory remain the only credible local-LLM substrates.
7B (Phi-3, Mistral 7B) runs comfortably on any 16 GB Copilot+ machine. 13B (Llama 3 13B, Qwen 14B) needs 32 GB and a real GPU or M-series chip. 30B+ requires 48 GB+ unified memory or 24 GB VRAM. 70B is realistic only on M4 Max 64 GB or RTX 5090 24 GB at Q4.
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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Modular AI workstation — repairable, upgradeable, Linux-first
AMD Ryzen AI 9 HX 370 (XDNA 2)
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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.
MacBook Pro M4 Max with 64GB unified memory runs Llama 3 70B Q4 at ~78 tok/s in MLX. For Windows, the Razer Blade 16 with RTX 5090 (24GB VRAM) leads CUDA inference.
Roughly 0.65GB per billion parameters at Q4. So 13B needs ~9GB, 70B needs ~46GB. Always add 4–8GB for context window.