Artificial Intelligence
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.
Explore Artificial Intelligence
- AI ProductivityBenchmarkJun 30
Top AI Document Generator Tools
AI document generators promise to create documents, presentations, and even websites from a short prompt. We tested how well several popular tools can generate different types of outputs, including: We tested AI Doc Maker by asking it to create documents about AIMultiple without providing any company details. The results were not perfect but fair. It…
- RAGBenchmarkJun 30
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. Models trained for search (query vs document separation) outperform larger models trained for general text similarity: e5_base (110M params) outperforms models…
- AI HardwareFeature ComparisonJun 30
GPU Software for AI: CUDA vs. ROCm
Raw hardware specifications tell 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. While AMD’s MI300X boasts 1,307 TFLOPS compared to NVIDIA’s H100/H200 at 990 TFLOPS, a 32% theoretical advantage, real-world performance is a…
- AI FoundationsInsightJun 25
Large Quantitative Models: Applications & Challenges
Modern systems are becoming too complex for traditional statistical analysis, as institutions now handle massive datasets, including patient, weather, and financial market data. Large quantitative models (LQMs) help by processing these datasets, integrating structured and unstructured data, and applying predictive modeling to uncover patterns and provide data-driven insights that traditional methods cannot deliver. Discover what…
- AI CodingOpen World EvaluationJun 25
Top 25 Version Control Tools
At AIMultiple, we use version control tools every day to manage the code for over 1,000 web pages across multiple projects. Based on our experience, we picked the top version control tools, including open-source and proprietary software: Git is a free and open-source distributed version control system originally created by Linus Torvalds in 2005 for…
- AI HardwareOpen World EvaluationJun 22
Top 15 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, and embedded systems. *TOPS = Tera Operations Per Second.…
- GenAI ApplicationsInsightJun 16
AI Text Generation: Top 17 Use Cases & 5 Case Studies
Generative AI, a subset of artificial intelligence, enables the creation of new content, such as text, code, images, designs, and videos, by learning from and building on existing data. Explore how generative AI can be used to generate content in the form of text via 17 use cases and 5 case studies of AI text…
- LLMBenchmarkApr 15
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 2.7x faster than BF16 while losing less than 2 points on MMLU-Pro, but code…