NVIDIA vs AMD vs Intel for local AI
Updated June 2026
VRAM gets you in the door; the brand decides how much software friction you fight to get there. Here is the honest 2026 picture across CUDA, ROCm and Intel Arc — and the live $/GB-VRAM to back it up.
The short version
All three brands run local LLMs today via llama.cpp, Ollama and LM Studio. The difference is software maturity vs value: NVIDIA costs the most per GB of VRAM but has the smoothest, best-supported stack; AMD and Intel are cheaper per GB but ask for more setup and occasionally lag on cutting-edge tooling.
| Brand | AI stack | Maturity | $/GB VRAM | Best for |
|---|---|---|---|---|
| NVIDIA | CUDA | Highest — the default target for models & tools | Highest (scalped flagships) | Least friction, CUDA-only tools, pro work |
| AMD | ROCm / Vulkan | Good and improving; inference solid | Lower (e.g. RX 9070 XT, 7900 XTX 24GB) | Gaming + AI value if you tinker |
| Intel | IPEX-LLM / Vulkan | Newest; advancing fast, most setup | Lowest (Arc B580 12GB) | Budget inference, most $/GB |
Why VRAM still comes first
Whatever the brand, a model has to fit in VRAM to run fast — if it spills to system RAM it slows 5-20×. So size VRAM to your target model first (see the best-GPU guide), then let brand decide software friction and $/GB.
How to choose
- Want it to just work / use CUDA-only tools / fine-tune: NVIDIA.
- Gaming first, AI second, value-focused: AMD (RX 9070 XT 16GB, or 7900 XTX for 24GB) — strong $/GB, ROCm inference is solid.
- Cheapest entry into 12GB AI, willing to set up: Intel Arc B580.
- Either way: confirm your specific toolchain supports the card, and compare live $/GB below.
Live GPU prices ($/GB VRAM, all brands)
NVIDIA vs AMD vs Intel FAQ
Which GPU brand is best for local AI?
NVIDIA, for the least friction — CUDA is the default target for almost every model, framework and tool, so things just work. AMD (ROCm) and Intel (IPEX-LLM / Vulkan) run local LLMs well via llama.cpp and similar, often at cheaper dollars-per-GB of VRAM, but you trade some software maturity and may need extra setup.
Can you run local LLMs on AMD or Intel GPUs?
Yes. llama.cpp, Ollama and LM Studio support AMD (ROCm/Vulkan) and Intel Arc (Vulkan / IPEX-LLM), so inference works well on both. The gaps are in cutting-edge training, some quantization kernels, and tools that ship CUDA-only first — those favor NVIDIA.
Is the Intel Arc B580 good for AI?
For the money, it offers among the cheapest dollars-per-GB of VRAM of any current new card (12GB), which makes it a tempting budget entry for inference of small-to-mid models. Expect to do more setup than on NVIDIA, and check that your toolchain supports Arc before buying.
Does VRAM still matter more than brand?
Yes. Across all three brands, whether a model fits in VRAM decides what you can run at all; brand mainly decides how much software friction you fight and your dollars-per-GB. Buy enough VRAM first, then pick the brand that fits your software needs and budget.