Ollama Guide Targets 90% Local LLM Coding Scores With 64,000-Token Tweaks
Updated
Updated · InfoWorld · Sep 15
Ollama Guide Targets 90% Local LLM Coding Scores With 64,000-Token Tweaks
3 articles · Updated · InfoWorld · Sep 15
Summary
Local LLMs that once flopped on laptop coding tests are now far more usable: Simon P. Couch said recent Qwen 3.5 and Gemma 4 releases scored 90% on his agentic coding eval.
Ollama’s guide says performance depends first on matching model and hardware—Gemma 4 12B needs about 6.7 GB memory, while Meta’s Muse Glimmer 30B needs roughly 24 GB VRAM.
64,000 tokens is the recommended context window for coding tools, agents and web search, but Ollama defaults to 4,000 on systems with under 24 GB VRAM unless users override it or create a Modelfile.
Five minutes is Ollama’s default unload timer, and changing keep-alive, KV cache and Flash Attention settings can reduce sluggishness; checking ollama ps also shows whether a model actually fits in VRAM.
5-fold to 8-fold speed gains were seen when Gemma 4 thinking mode was turned off for some summarization tests, underscoring the trade-off: local models remain weaker than top cloud systems but are increasingly practical for private, basic work.