Local LLMs hit coding viability on 8GB GPUs

The "8GB barrier" for local AI coding has finally been broken, shifting the focus from VRAM quantity to model efficiency.

• –Qwen2.5-Coder-7B delivers over 50 tokens/sec on mid-range GPUs, making real-time IDE autocompletion fluid.
• –Performance on benchmarks like HumanEval (88.4%) now puts 7B-class local models in direct competition with GPT-4 for code generation.
• –Local execution eliminates API latency and subscription costs while ensuring codebase privacy.
• –Ecosystem maturity through tools like Ollama, Continue.dev, and Aider has made "local-first" development a practical reality.