Qwen2.5-Coder and DeepSeek-Coder V2 lead local coding LLM race

For users with 8GB of VRAM, the Qwen2.5-Coder 7B model is the top choice for coding tasks, offering impressive benchmark scores and a large context window. Those with 12-16GB of VRAM face a trade-off between a dense 14B parameter model like Qwen2.5-Coder 14B-Instruct, which offers faster inference, and the DeepSeek-Coder-V2-Lite, a Mixture-of-Experts model with fewer active parameters per token but potentially higher quality due to specialized experts. AI

IMPACT Provides clear guidance on selecting local coding LLMs based on VRAM, influencing developer tool choices and hardware investment.

RANK_REASON Comparison of open-weight coding LLMs with benchmark data and hardware requirements. [lever_c_demoted from research: ic=1 ai=1.0]

Read on dev.to — LLM tag →

• Codestral
• Continue.dev
• DeepSeek-Coder-V2
• DeepSeek-Coder-V2-Lite
• HumanEval
• MBPP+
• Ollama
• Qwen2.5-Coder
• Qwen2.5-Coder 14B-Instruct
• Qwen2.5-Coder 7B

• model release
• product

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →