AI Hardware Benchmarks: Inference, Training and AI Workloads
AI hardware are specialized processors for AI inference and model training. We analyzed major AI chip manufacturers, benchmarking the latest generation AI chips on cloud and serverless environments with different LLMs.
Serverless GPU Benchmark
Benchmarked 8 serverless GPUs on Modal for inference and Llama-3.2 finetuning.
AI Hardware Revenue Growth at NVIDIA
Mapped top AI chipmakers by efficiency, scale, and workload performance.
Explore AI Hardware Benchmarks: Inference, Training and AI Workloads
Cloud GPUs for Deep Learning: Availability& Price / Performance
If you are flexible about the GPU model, identify the most cost-effective cloud GPU based on our benchmark of 10 GPU models in image and text generation & finetuning scenarios. If you prefer a specific model (e.g. A100), identify the lowest-cost GPU cloud provider offering it.
GPU Concurrency Benchmark
We benchmarked the latest NVIDIA GPUs, including the H100, H200, and B200, for concurrency scaling analysis. Using the vLLM framework with the gpt-oss-20b model, we tested how these GPUs handle concurrent requests, from 1 to 1024.
Top 30 Cloud GPU Providers & Their GPUs
We benchmarked 10 most common GPUs in typical scenarios (e.g. finetuning an LLM like Llama 3.2). Based on these learnings, if you: Ranking: Sponsors have links and are highlighted at the top. After that, hyperscalers are listed by US market share. Then, providers are sorted by the number of models that they offer.
Top 20 AI Chip Makers: NVIDIA & Its Competitors
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.
AI Chips: A Guide to Cost-efficient AI Training & Inference
In the past decade, machine learning, particularly deep neural networks, has been pivotal in the rise of commercial AI applications. Significant advancements in the computational power of modern hardware enabled the successful implementation of deep neural networks in the early 2010s.