AMD vs Nvidia and the New Reality of LLM Deployment: Practical Lessons from 2025

AMD vs Nvidia isn’t just a brand feud anymore—it’s a stack decision that hits cost and speed in real deployments. In 2025, the hardware/software combo you choose can make or break a model rollout, and a beginner guide to deploying LLMs with AMD on Windows using PyTorch shows there’s a workable path for folks outside pure Nvidia ecosystems ^[1]. The sentiment around MI300x and corporate exclusivity also echoes how some builders feel left behind by consumer-friendly options ^[1].

Hardware reality, meet software reality. The Qwen3-Next work in llama.cpp isn’t just a PR sprint; it spotlights CUDA plans and the memory and multi-batch hurdles that come with modern open weights. Expect ongoing tweaks as developers tackle convolution for multi-batch inputs and push for faster kernel paths ^[2]. RAM and throughput aren’t abstract concerns here; they’re what decide deployment scale and price/performance in practice ^[2].

On‑prem, open models still win for devs who want control. In VS Code with Cursor, you can run open weights like Qwen 3, GLM 4.6, and Kimi K2 with Hugging Face Copilot Chat—cheap, vendor‑neutral, and editable inside your editor. The setup stories emphasize no vendor lock‑in and flexible model swapping for coding, debugging, and doc work ^[3].

Bottom line: 2025 deployments demand a pragmatic mix of hardware flexibility, CUDA acceleration paths, and open models to hit cost, accessibility, and performance sweet spots.

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