NVIDIA, AMD and Intel for local AI: which platform to choose

The question is the same for almost everyone: do you need an NVIDIA GPU for local AI, or can AMD and Intel hold their own too? Short answer: it depends on which model you want to run, how much you want to spend, and how much you enjoy dealing with software. The long answer is this guide, which compares the three platforms on what is actually available today: official software stacks, cards by VRAM tier, and real speeds.

The ecosystem matters as much as the card's numbers

Before you look at prices, one practical rule: at the same price, a GPU that is "mature" for AI often beats a newer card with younger software. The three ecosystems in 2026:

• NVIDIA / CUDA — the de facto standard. CUDA is NVIDIA's GPU programming stack (official site): Ollama, llama.cpp, vLLM and LM Studio support it first, and the optimizations (Flash Attention, quantized kernels, the Blackwell generation's FP4 format) arrive earlier and are more polished. The cost: a higher price for the same VRAM.
• AMD / ROCm — ROCm is AMD's equivalent stack (official documentation). Since ROCm 7, consumer Radeon RDNA 3 and RDNA 4 cards (RX 7000 and RX 9000 series) are officially supported on Linux: install Ollama or llama.cpp with the ROCm backend and models run without environment variables or workarounds. Recent releases moved the process to a modular release system (TheRock) that keeps components updated more easily.
• Intel / Vulkan — Intel bet on the open path: IPEX-LLM, the library that brought LLM inference to Arc GPUs, was archived in January 2026 (repository). The maintained path is Vulkan, the cross-platform standard from Khronos (vulkan.org): Ollama and llama.cpp support it natively on Windows, Linux and macOS (via MoltenVK). Less optimized than CUDA, but it works on practically every card.

The numbers: bandwidth and VRAM

For token decoding (the phase that determines tokens/second), memory bandwidth is the main factor: for each token, the engine has to re-read all the weights from VRAM. The theoretical limit is bandwidth divided by weight size; in practice you get 60-75% of that limit. Q4_K_M weights for a 14B model occupy about 8.4GB, for a 27B about 17GB — numbers consistent with our setup planner.

Card	VRAM	Bandwidth	What fits in Q4
Intel Arc B580	12GB	456 GB/s	8-14B
NVIDIA RTX 5070	12GB	672 GB/s	8-14B
NVIDIA RTX 5060 Ti	16GB	448 GB/s	up to 27B
AMD RX 9070 XT	16GB	640 GB/s	9-20B
NVIDIA RTX 5070 Ti	16GB	896 GB/s	up to 27B
NVIDIA RTX 4090	24GB	1008 GB/s	27-32B
AMD RX 7900 XTX	24GB	960 GB/s	27-32B

12GB tier: 8-14B, where speed decides

Here VRAM is enough for the most-used model class (8-14B in Q4) and the decisive factor is bandwidth. On a 14B in Q4 (~8.4GB of weights): the RTX 5070 reaches ~44 tok/s theoretically (26-33 in practice), the RX 9070 XT ~33 (20-25 in practice), the Arc B580 ~24 (14-18 in practice). The difference is felt: on an 8B, the B580 still lands at 40-42 real tok/s thanks to Ollama's Vulkan, a speed entirely sufficient for chat and writing.

16GB tier: the 27B and the context compromise

16GB is the center of gravity in 2026: a 27B in Q4 (~17GB including KV cache) only fits here and on 24GB cards. But beware: at 16GB a 27B "fits" barely, with a context that cannot be very long. In practice, the 16GB class is home to 9-20B models with plenty of context — and there, bandwidth decides. The RX 9070 XT (640 GB/s) is the fastest 16GB card for 9-20B models, a 43% advantage over the 5060 Ti (448 GB/s); the 5070 Ti (896 GB/s) dominates when you want to push a 27B: ~52 tok/s theoretical versus 26 on the 5060 Ti, 18-22 in practice on the latter.

24GB tier: capacity wins

24GB and beyond changes the game: the 27B fits with a wide context, 32B in Q4 fits, and large MoE models (100B+ with few active parameters) become realistic. The RX 7900 XTX's 960 GB/s are 96% of the RTX 4090 (1008 GB/s): the same decode speed, and the 7900 XTX generally costs less. At this level, the choice between NVIDIA and AMD comes down to price, power draw and personal preference — the software is stable and official on both sides.

When does it make sense to leave CUDA?

• Budget under 500 — the Arc B580 12GB is the entry point: for 8-14B models it is more than decent, and the Vulkan path is now mature with no exotic dependencies.
• You want 16GB with the most tokens/second for 9-20B models — the RX 9070 XT offers the best bandwidth-per-dollar in the class, with official ROCm 7 and no workarounds.
• You need 24GB and want to spend less — RX 7900 XTX: the same speed class as the 4090, a lower street price.
• The rest of the time, stay with NVIDIA — you want a 27B with context and zero friction, you do serving with vLLM, or you want maximum compatibility with engines and quantization formats: CUDA is still the most complete ecosystem, and the 5070 Ti 16GB is the right pick for "everything, right now, no stress".

One last note for those who already own a card: before buying, try your current GPU with the model you care about. If you reach acceptable speeds (20-30 tok/s are comfortable for personal use), the GPU budget stays free for system RAM, which with heavy MoE models and long contexts matters at least as much as VRAM.

Verdict

The three platforms are no longer "NVIDIA yes, the others no": in 2026 AMD has official consumer support, Intel has a stable open path, and the common software (Ollama, llama.cpp) speaks all of them. The right GPU is the one that puts the weights of the model you want to use into VRAM, at the best bandwidth for your budget — and the CUDA ecosystem remains the premium you pay for the most tokens/second with the least stress.

Article generated with AI