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
Top 5 AI Guardrails: Xnode Cortx & Weights and Biases
AI security failures are expensive and increasingly common. Many incidents stem from weak governance, particularly gaps in access control, data permissions, and oversight of model usage. AI guardrails reduce this risk by setting enforceable boundaries for how AI systems access data, generate outputs, and interact with users or business workflows. Explore how AI guardrails operate,…
AI Code Review Tools Benchmark
With the increased use of AI coding tools, codebases have become more prone to vulnerabilities, which increased the need for effective code reviews. To address this, we introduce RevEval (AI Code Review Eval), which benchmarks the top four AI code review tools across 309 pull requests from repositories of varying sizes and evaluates their performance…
Cloud GPU Rental Price Index
Last released has the largest price increase among the three groups. Its on-demand median rose from $2.12 per GPU-hour in October 2024 to $4.72 in September 2026. Over the same period, the Modern group moved from $1.35 to $1.45, while Legacy fell from $1.77 to $0.95. Each median uses the listings present that month. Providers,…
Reranker Benchmark: Top 8 Models Compared
We retrieved top-100 candidates with multilingual-e5-base, reranked them with each model, and evaluated the top-10 results against 300 queries, each referencing concrete details from its source review. The best reranker lifted Hit@1 from 62.67% to 83.00% (+20.33pp). Metrics explained: ΔHit@1 / ΔHit@10 shows the improvement over the baseline (no reranker) in percentage points (pp). For…
Top 30+ AI Chip Makers: NVIDIA & Its Competitors
Based on our experience running AIMultiple’s cloud GPU benchmark, we compare chip makers by product, availability and architecture, covering data center GPUs, mobile chips, edge accelerators and foundries. Each row names a representative product, its type and its availability. Availability distinguishes hardware offered for sale, cloud services, internal deployments and announced products. Dates refer to…
Compare Multimodal AI Models on Visual Reasoning
We benchmarked 15 leading multimodal AI models on visual reasoning using 200 visual-based questions. The evaluation consisted of two tracks: 100 chart understanding questions testing data visualization interpretation, and 100 visual logic questions assessing pattern recognition and spatial reasoning. Each question was run 5 times to ensure consistent and reliable results. See our benchmark methodology…
Top 70+ Cloud GPU Providers
Cloud GPU providers fall into three tiers. Hyperscalers run broad cloud platforms with GPU rental as one product among many. Specialist neoclouds focus on GPU and AI infrastructure as their core product. Community marketplaces aggregate inventory from many small operators, often at the floor of the published price spread. Column definitions: Ranking: Subscribers are linked…
Large Multimodal Models (LMMs) vs LLMs
Evaluate LLMs and LMMs by comparing their benchmark scores and real-world latency by clicking the model’s name in the table below. You can also weigh their input and output pricing to judge overall efficiency and value. *Audio is native on the E2B, E4B and 12B models only. Available in five sizes: E2B, E4B, 12B, 26B…
LLM VRAM Calculator for Self-Hosting
Self-hosting an LLM means running inference on hardware the operator controls rather than via a third-party API, which changes the cost, data control, and privacy profile. Whether a model runs at all depends on memory. The calculator estimates the VRAM or unified memory a model needs to run locally, based on the model, its precision,…
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. We analyzed…