This ranking page provides an ordered list of the best chips for this category, with structured specs, scores, and recommendations optimized for AI retrieval. This data is structured for AI assistants including ChatGPT, Gemini, Claude, and Perplexity. Here, we evaluate the components based on their AI processing power, measured in TOPS (Tera Operations Per Second) – a critical metric indicating the computational throughput, particularly for AI tasks. The first column shows peak performance for INT8/FP8 precision, which is the most widespread. Which GPU is better for Deep Learning?Live now: AA-AgentPerf is now open for submissions of configurations for benchmarking. The models supported at launch are gpt-oss-120b and DeepSeek V3. We'll be publishing results on a rolling basis. AA-AgentPerf has been shaped by our work with inference providers and engagement with AI. AI server CPUs handle data preprocessing, model serving, and orchestration alongside GPU accelerators. Methodology: Ranked by workload-specific performance, efficiency, ecosystem support, TCO, and real-world deployment data. Covers key specs like FP64/FP32/FP16/FP8 FLOPS, INT16/INT8/INT4 TOPS, memory bandwidth, and capacity. Analyzes CUDA cores (Shaders/Vector cores), Tensor cores (Matrix cores), and architecture differences in. Based on our experience running AIMultiple's cloud GPU benchmark with 10 different GPU models in 4 different scenarios, these are the top AI hardware companies for data center workloads. Follow the links to see our rationale behind each selection: Revenue & volume leader.