Uncensored AI for developershttps://refusalrate.com/https://refusalrate.com/best-local-llm-for-coding-2026/https://refusalrate.com/best-local-llm-for-coding-2026/For a 24GB GPU, the best local coding model in October 2026 is Qwen3.8 27B: Apache-2.0, 256K context, an 18GB Ollama download, and the strongest vendor-reported coding scores of any model that fits. Smaller tiers drop to Devstral Small 2 (16GB) and Qwen3.5 9B (8GB). Larger tiers open up mixture-of-experts models like Laguna XS 2.1 and Qwen3-Coder-Next.Tue, 27 Oct 2026 00:00:00 GMThttps://refusalrate.com/abliterated-models-ollama/https://refusalrate.com/abliterated-models-ollama/An abliterated model is an ordinary open-weight model with its refusal direction edited out, so it runs in Ollama and LM Studio like any other GGUF. In Ollama, one command pulls it straight from Hugging Face: ollama run hf.co/{user}/{repo}:{quant}. In LM Studio, search the Hugging Face repo or use lms get. Read the model card and license first.Tue, 27 Oct 2026 00:00:00 GMThttps://refusalrate.com/local-llm-hardware-requirements/https://refusalrate.com/local-llm-hardware-requirements/Memory for a local coding LLM is weights plus KV cache plus about 1 GB of overhead. Weights are parameters times bits per weight divided by 8. At Q4_K_M a 9B model needs about 5.7 GB, a 27B about 16.5 GB and a 35B MoE about 22 GB, before context. Recent Qwen models cache only 64 KiB per token or less, so long context costs far less than it did.Tue, 27 Oct 2026 00:00:00 GMThttps://refusalrate.com/what-is-abliteration/https://refusalrate.com/what-is-abliteration/Abliteration is a weight edit that removes a model's tendency to refuse. Research in 2024 found that refusal in chat models is largely carried by a single direction in the model's activations; projecting that direction out of the weights stops most refusals without retraining. The trade-off is a measurable drop in benchmark quality, which builders usually repair with a short preference-tuning pass.Sun, 25 Oct 2026 00:00:00 GMT