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.
Explore AI Hardware Benchmarks: Inference, Training and AI Workloads
DGX Spark vs Mac Studio & Halo: Benchmarks & Alternatives
NVIDIA’s DGX Spark entered the desktop AI market in 2025 at $3,999, positioning itself as a “desktop AI supercomputer”. It packs 128GB of unified memory and promises one petaflop of FP4 AI performance in a Mac Mini-sized chassis. See the benchmark results on value and performance compared to alternatives: Competitive analysis: DGX Spark vs.
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.
LLM Inference Engines: vLLM vs LMDeploy vs SGLang
We benchmarked 3 leading LLM inference engines on NVIDIA H100: vLLM, LMDeploy, and SGLang. Each engine processed identical workloads; 1,000 ShareGPT prompts using Llama 3.1 8B-Instruct to isolate the true performance impact of their architectural choices and optimization strategies.
Top 10 Edge AI Chip Makers with Use Cases
The demand for low-latency processing has driven innovation in edge AI chips. These processors are designed to perform AI computations locally on devices rather than relying on cloud-based solutions. Based on our experience analyzing AI chip makers, we identified the leading solutions for robotics, industrial IoT, computer vision, and embedded systems.
GPU Software for AI: CUDA vs. ROCm
Raw hardware specifications tell only half the story in GPU computing. To measure real-world AI performance, we ran 52 distinct tests comparing AMD’s MI300X with NVIDIA’s H100, H200, and B200 across multi-GPU and high-concurrency scenarios.
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.
Multi-GPU Benchmark: B200 vs H200 vs H100 vs MI300X
For over two decades, optimizing compute performance has been a cornerstone of my work. We benchmarked NVIDIA’s B200, H200, H100 and AMD’s MI300X to assess how well they scale for Large Language Model (LLM) inference. Using the vLLM framework with the meta-llama/Llama-3.1-8B-Instruct model, we ran tests on 1, 2, 4 and 8 GPUs.
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: H100 vs H200 vs B200 vs MI300X
I have spent the last 20 years focusing on system-level computational performance optimization. We benchmarked the latest NVIDIA GPUs, including the NVIDIA (H100, H200, and B200) and AMD (MI300X), 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 512.