The AI hardware landscape has shifted significantly in 2026, with NVIDIA, AMD, and Intel all competing for developers who need GPUs capable of running local large language models and AI inference workloads.
Choosing the right GPU for AI workloads requires looking beyond marketing numbers and focusing on the specifications that actually affect real-world performance. Memory capacity, memory bandwidth, and software ecosystem maturity consistently matter more than theoretical compute peaks when running transformer models locally.
This comparison covers the most relevant workstation and prosumer GPUs available in mid-2026, including NVIDIA's Blackwell architecture (RTX 50-series), AMD's Radeon AI Pro R9700, and Intel's Arc Pro B70. The goal is to provide a practical reference for developers deciding which hardware best fits their model sizes, software stack, and budget constraints.
Which GPU specifications matter for AI workloads
Marketing materials from GPU vendors emphasise AI TOPS and tensor performance, but these metrics rarely tell the complete story for local inference. The specifications below are ranked by their actual impact on running large language models.
