If you are choosing between an NVIDIA and an AMD GPU for local AI, the hardware is only half the decision — the software stack is the other half. CUDA and ROCm are the two acceleration platforms, and while they increasingly offer the same capabilities, the day-to-day experience still differs in ways worth understanding before you buy.
What the two platforms are
CUDA is NVIDIA's platform: drivers, libraries and tooling that virtually every AI project targets first — or only. Fifteen years of ecosystem gravity mean that when a new runtime, quantization format or optimization appears, CUDA support arrives first and works reliably.
ROCm is AMD's open equivalent, providing the same kind of acceleration for AMD GPUs through HIP (a CUDA-like API). It has matured dramatically: llama.cpp, Ollama and vLLM all run on ROCm, and RDNA 3 flagship cards perform close to their NVIDIA bandwidth-equivalents.
The honest comparison
| Aspect | CUDA (NVIDIA) | ROCm (AMD) |
|---|---|---|
| Runtime coverage | Everything | llama.cpp, Ollama, vLLM and most majors |
| New-feature lag | First | Weeks-to-months later |
| Windows support | Full | Limited — Vulkan/DirectML paths |
| Linux support | Full | Good, distro-sensitive |
| Setup friction | Low | Moderate |
| Price per GB of VRAM | Higher | Lower |
The pattern is consistent: AMD wins on hardware value; NVIDIA wins on software experience. A 7900 XTX gives you 24 GB near-flagship bandwidth for less than a 4090 — but you will spend more time reading documentation, and some niche tooling simply won't work.
What actually works on each
On NVIDIA (CUDA), everything just works: Ollama, LM Studio, llama.cpp, vLLM, ExLlama, all GGUF/GPTQ/AWQ builds, flash-attention paths for long context. This is the zero-thought-option.
On AMD (ROCm), the reliable path is:
- llama.cpp with ROCm/HIP on Linux (or Vulkan on Windows) — best coverage for GGUF models.
- Ollama — supports ROCm on Linux; Vulkan support covers Windows reasonably.
- vLLM on Linux for serving-style workloads on flagship cards.
Avoid assuming every tool works on AMD: some quantization paths and optimizations are CUDA-only, and support for mid-range SKUs can lag behind flagship coverage.
A third path: Vulkan
Vulkan-based inference (in llama.cpp and friends) deserves mention: it runs on NVIDIA, AMD and Intel GPUs with no vendor stack at all. It is usually somewhat slower than CUDA or ROCm paths, but it is the most friction-free cross-vendor option — and on Windows AMD machines it is often the sensible default.
Decision guide
- Buy NVIDIA if: you want zero setup friction, use bleeding-edge runtimes, or run workloads (flash-attention-heavy serving, exotic quantizations) that are CUDA-first.
- Buy AMD if: you are comfortable on Linux, your runtime shortlist is llama.cpp/Ollama/vLLM, and the VRAM-per-euro matters — 24 GB at midrange prices is a genuine capability advantage.
- Either way: GGUF models run everywhere, which keeps your options open regardless of vendor. See what is GGUF.
One more honest note: for Intel GPUs, the parallel stack is IPEX-LLM/SYCL/OpenVINO — capable but the youngest of the three, covered in the GPU directory.
CanIHost's checker treats backend as part of your profile: the hardware checker and GPU pages note which backends each card supports, so recommendations match the stack you will actually run.