Guide Matches 7 Local LLMs to Specific Tasks, From 0.3GB RAG to 6.5GB Generalists
Updated
Updated · KDnuggets · Oct 28
Guide Matches 7 Local LLMs to Specific Tasks, From 0.3GB RAG to 6.5GB Generalists
3 articles · Updated · KDnuggets · Oct 28
Summary
Seven local models are mapped to concrete use cases instead of a single "best" ranking, with the guide arguing that task mismatch—not model quality—drives much user disappointment.
Three benchmark lenses underpin the picks: MMLU for general knowledge, HumanEval for coding, and MT-Bench for instruction-following, showing why one leaderboard score cannot predict performance across jobs.
Qwen3.5-9B emerges as the default all-rounder at about 6.5GB VRAM, while Qwen2.5-Coder-7B is highlighted for coding, Gemma 4 E2B/E4B for writing and multimodal edge use, and Phi-4-mini for tight hardware limits.
Mistral 7B is positioned for clean general writing, Llama 3.1 8B for its broad fine-tuning ecosystem, and nomic-embed-text—under 300MB with 85.6 million Ollama pulls—for the retrieval side of local RAG pipelines.
All seven are available as ready-to-pull GGUF builds via Ollama, reinforcing the guide's broader case for local models on privacy, offline use, cost control, and stable behavior.