Run Frontier AI Models
On Your Own Metal
Empirical tokens-per-second benchmarks, exact KV-cache equations, and production Model Context Protocol (MCP) architectures. No fluff, no vendor lock-in.
Running local open-weights LLMs (Llama 3.3, DeepSeek-R1, Qwen 2.5) eliminates SaaS token costs and latency variability. For high-concurrency production serving, vLLM delivers 3.5x higher throughput via PagedAttention, whereas Ollama is the premier developer runtime for zero-config single-stream desktop and edge deployments.
Interactive Local VRAM & Hardware Sizer
Adjust model architecture and context buffer to compute required memory and hardware recommendations.
Foundational Engineering Guides
Grounded reproducible configurations, memory math, and terminal commands.
Ollama vs vLLM: Concurrency & Throughput Benchmark (2026)
Single-user developer latency vs 10-stream continuous batching throughput tested on RTX 4090 with PagedAttention analysis.
VRAM Requirements Calculator for 70B Models: Sizing Guide
Exact GPU memory equations for Q4_K_M vs FP8 across 8k to 64k context windows. Avoid catastrophic CPU RAM fallback.
DeepSeek R1 Local Setup Ollama: Step-by-Step Optimization
Optimal chain-of-thought parameter tuning, temperature 0.6, and custom Modelfile templates for 8B, 14B, and 32B checkpoints.
Mac Studio M4 Max LLM Speed: Tokens Per Second Report
Unified memory bandwidth (546 GB/s) testing across 8B, 32B, and 70B parameter models at 65W total system power draw.
Custom MCP Server Python Tutorial: Build Production Tools
FastMCP stdio transport, automated schema generation, and stateful SQLite integrations for Claude Code and Cursor.