Yo, saw this earlier—quick q: what’s ur typical workload (CV/NLP? batch sizes?) and do you need CUDA on-device 100%? If yes, honestly grab a Lenovo or Dell with an NVIDIA GPU; if not, go cloud + light laptop.

Ok so… after years doing PyTorch on the road, laptop GPUs are worth it for fast prototyping + small fine-tunes, but VRAM/thermals make long runs annoying; i still use cloud for serious training, unfortunately.

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Just jumping in here. Before I drop the hammer on recommendations, what's your actual budget range? Market-wise, the "laptop GPU" label is a bit of a trap because TGP (Total Graphics Power) varies wildly between brands even with the same chip name. If you're looking for peak CUDA performance, the Razer Blade 16 or MSI Titan GT77 usually offer the highest wattage for the RTX 4090 mobile chips, but you pay a massive premium for that thermal headroom. If you're doing heavy NLP and need massive VRAM for local LLM inference, the MacBook Pro M3 Max with 128GB Unified Memory is honestly a game changer, though you lose the native CUDA ecosystem and have to rely on MLX or Metal. For a balanced dev machine that doesn't scream "gamer," the ASUS ROG Zephyrus G16 is usually the sweet spot for thermals vs. portability. Setting up WSL2 on Windows has actually gotten way easier lately, so drivers aren't the nightmare they used to be tbh. Are you looking at the $2k range or the $4k+ "workstation replacement" tier?? That'll change my advice completely.