Running LLMs locally for coding is now viable. We measured latency, token throughput, and privacy tradeoffs between local Ollama/CodeLlama setups and cloud AI tools.

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A performance-focused analysis comparing local LLM inference (Ollama with CodeLlama 34B and Qwen2.5-Coder 32B) against cloud AI coding APIs (GPT-4.1, Claude Sonnet 4, Gemini 2.5 Pro) across latency, throughput, privacy, cost, and reliability dimensions. Key findings: local inference wins on time-to-first-token (15–80ms vs 180–600ms for cloud), making it superior for autocomplete tasks. Cloud models win on sustained token throughput (80–150 tok/s vs 35–65 tok/s locally), giving them an edge for outputs exceeding ~200–300 tokens. A hybrid approach routing short completions locally and complex tasks to cloud can eliminate 60–80% of API spend. Local setups offer true air-gapped privacy with no data leaving the machine. Hardware break-even for heavy users is 2–5 months. The article includes a reproducible Python benchmarking harness and Ollama configuration details.

Local vs Cloud AI Coding: Latency, Privacy & Performance Guide

Latency Benchmarks: Local GPU vs Cloud API