benchmarking

HEWN 2.0 2026: AI Output Router for Precision Summaries & Polished Code

Statistical analysis for LLM evaluations, from model and prompt comparisons to inference resilient to LLM judge bias, including at small sample sizes. All defaults battle-tested in Monte Carlo simulations.

Data Center and Client workload and software optimizations for Intel hardware.

Deepmark AI enables a unique testing environment for language models (LLM) assessment on task-specific metrics and on your own data so your GenAI-powered solution has predictable and reliable performance.

Community registry of LLM serving-path traps that produce confidently wrong measurements: templates, tool parsers, reasoning fields, quant kernel paths, CUDA toolchains, KV allocation, eval harnesses, versioning. Symptom-first, with the check that catches each.

Generative AI-based CyberSecurity-focused Prompt Dataset for Benchmarking Large Language Models

Dual-engine (llama.cpp + vLLM) LLM benchmarking pipeline for GGUF & safetensors on NVIDIA GPUs — speed, quality, live dashboard, publishable cards.

A pure-Python structured memory benchmark for multi-agent LLM systems — context graph vs vector RAG vs raw history dump, five scenarios, 18 graded queries, zero API calls.