AI
Explore practical insights, research, and benchmarks on artificial intelligence, including generative AI, large language models, RAG, governance frameworks, MLOps practices, and AI hardware. Gain an understanding of key tools, implementation strategies, and enterprise use cases shaping the AI landscape.
How to Design an AI Infrastructure & Key Components
AI infrastructure is the foundation of current AI applications, combining specialized hardware, software, and operating methods to meet AI needs. Businesses across various industries utilize it to integrate AI into products and processes, such as chatbots (e.g., ChatGPT), facial/speech recognition, and computer vision.
Top Vector Database for RAG: Qdrant vs Weaviate vs Pinecone
Vector databases power the retrieval layer in RAG workflows by storing document and query embeddings as high‑dimensional vectors. They enable fast similarity searches based on vector distances.
Top Image Recognition Tools Compared in 2026
We evaluated the real-world performance of top cloud image recognition tools for object detection tasks by benchmarking their default API configurations across 5 classes using 100 images. This included contrasting performances, analyzing features, and comparing service offerings in relation to pricing. Benchmark Results Performance overview at IoU=0.
Top 125 Generative AI Applications
Based on our analysis of 30+ case studies and 10 benchmarks, where we tested and compared over 40 products, we identified 125 generative AI use cases across the following categories: For other applications of AI for requests where there is a single correct answer (e.g., prediction or classification), check out AI applications.
LLM Market Share: Compare Usage & Adoption
We analyzed LLM market share by combining usage-based data and web visit estimates to show how demand for large language models is distributed across AI labs and AI applications: LLM market share comparison by country Read the methodology to see how we measured and calculated these results.
Best 10 Serverless GPU Clouds & 14 Cost-Effective GPUs
Serverless GPU can provide easy-to-scale computing services for AI workloads. However, their costs can be substantial for large-scale projects. Navigate to sections based on your needs: Serverless GPU price per throughput Serverless GPU providers offer different performance levels and pricing for AI workloads.
Compare Relational Foundation Models
We benchmarked SAP-RPT-1-OSS against gradient boosting (LightGBM, CatBoost) on 17 tabular datasets spanning the semantic-numeral spectrum, small/high-semantic tables, mixed business datasets, and large low-semantic numerical datasets. Our goal is to measure where a relational LLM’s pretrained semantic priors may provide advantages over traditional tree models and where they face challenges under scale or low-semantic structure.
Top 10 Multilingual Embedding Models for RAG
We benchmarked 10 multilingual embedding models on ~606k Amazon reviews across 6 languages (German, English, Spanish, French, Japanese, Chinese). We generated 1,800 queries (300 per language), each referencing concrete details from its source review.
LLM Quantization: BF16 vs FP8 vs INT4
We benchmarked Qwen3-32B at 4 precision levels (BF16, FP8, GPTQ-Int8, GPTQ-Int4) on a single NVIDIA H100 80GB GPU. Each configuration was evaluated on 2 benchmarks (~12.2K questions) covering knowledge and code generation, plus 2,000+ inference runs to measure throughput. Int4 is 2.
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’s H100, H200, and B200, and AMD’s 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.