Research Library
Explore our research, benchmarks, and practical guides on enterprise AI and software.
- Cloud ComputingBenchmarkJul 2
Top Serverless Functions: Vercel vs Azure vs AWS
Serverless functions enable developers to run code without having to manage a server. This allows them to focus on writing and deploying applications while infrastructure scaling and maintenance are handled automatically in the background. In this benchmark, we evaluated 7 popular cloud service providers following our methodology to test their serverless function performance. We measured…
- AI HardwareBenchmarkJul 1
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. We measured offline batch throughput across 10,000 total inference operations (1,000 prompts × 10 runs per…
- 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…