Model Releases
Best Local Models for Claude Code with 8gb vram
Tested on Laptop with RTX 5060 8gb vram and 16gb ram Claude code - autocompact = false Ollama - Flash attention = 1, kv cache = q8_0 Gemma4 12B 64K - https://ollama.com/SetneufPT/Gemma4-12B-IT-QAT_Q4_
Tested on Laptop with RTX 5060 8gb vram and 16gb ram Claude code - autocompact = false Ollama - Flash attention = 1, kv cache = q8_0 Gemma4 12B 64K - https://ollama.com/SetneufPT/Gemma4-12B-IT-QAT_Q4_64K_16GB-GPU Qwen3.5 9B 256K - https://ollama.com/reecdev/qwen3.5-lowvram Omnicoder2 9B 32K, increase context 128k- https://ollama.com/carstenuhlig/omnicoder-2-9b Qwopos3.5 4B 64K, can increase context upto 256K (Best at 128k)- https://ollama.com/SetneufPT/Qwopus3.5-4B-Coder-MTP_Q4_64k_8GB-GPU Qwen3.5 4B 64K, can increase context upto 256K (Best at 128k) - https://ollama.com/rafw007/qwen35-claude-coder Gemma4 e4B 64K, can increase context upto 128k - https://ollama.com/rafw007/gemma4-e4b-claude-coder Muse Glimmer 30B 256K, Unstable and slow better if decrease context to 128k - https://ollama.com/venkataparswanadh/muse-glimmer submitted by /u/Actual-Requirement-2 [link] [comments]
Related
- A tool to get Claude Code-style reliability from fully local models
- I built a thing that benchmarks Ollama models on your own hardware and ranks them
- claudely: launch Claude Code against Local LLM provider like LM Studio / Ollama / llama.cpp without trashing your real claude config
Source: r/ollama | 2026-09-11