Analysis Articles
Browse our analysis articles on AI inference, model selection, and GPU planning.
The Real Cost of Inference at Enterprise Scale: A 2026 Pricing Audit
A cross-provider audit of LLM inference pricing in May 2026, applying the four-factor cost framework to real numbers across frontier models, OSS hosts, and self-hosted GPUs.
How Close Are Roofline Estimates to Real vLLM Benchmarks?
Inferbase's GPU sizing engine uses physics-based roofline math to predict throughput. Here's how the predictions compare to published vLLM benchmark numbers across five common configurations, including where we under- and over-shoot.
Why Most GPU Memory Calculators Are Wrong About KV Cache
Public GPU sizing calculators mostly haven't caught up to 2026 inference. Three specific things they get wrong: paged attention, FP8 KV precision, and Mixture-of-Experts memory.
Claude-Class Agent Workloads: When Self-Hosting Beats the Anthropic API
For agentic workloads built on Claude Sonnet or Opus, the self-host vs API decision is rarely about price. It's about cache mechanics, rate limits, and tail latency. Here's the full math.
LLM Benchmarks Explained: What the Scores Actually Mean
LLM benchmark scores dominate model marketing, but most are saturated or contaminated. A practical guide to reading them critically before choosing a model.
The Hidden Costs of LLM APIs: What Token Price Tables Don't Show
The $/M token figure on LLM provider pricing pages represents roughly 60% of what teams actually pay in production. Caching, output ratios, rate limits, and reliability determine the rest.
Claude Opus 4.7: Output Verification, High-Resolution Vision, and Anthropic's Agentic Ambitions
Anthropic's Opus 4.7 verifies its own outputs, adds 3.75 MP vision, and a new xhigh reasoning tier. Benchmarks, pricing, and how it compares to GPT-5.4 and Gemini 3.1 Pro.
Best LLM for Coding in 2026: A Data-Driven Comparison
Which LLM is best for coding? We rank the top models by coding benchmarks, pricing, and context window to help you pick the right one.
Google Gemma 4: Architecture, GPU Requirements, and What It Means for Open-Source AI
Technical breakdown of Google's Gemma 4 model family: the 31B dense, 26B MoE, and on-device E2B/E4B variants. GPU memory requirements, benchmarks, and where each model fits.
Open Source vs Proprietary LLMs: Which Should You Choose?
Compare open-source and proprietary LLMs on cost, performance, privacy, and customization to pick the right approach for your use case.
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