LocalLLaMA users seek coding model

The real constraint here is less about raw benchmark heroics and more about throughput, context size, and how much you can quantize before the experience gets sluggish.

• –On 8GB VRAM, the sweet spot is usually a 7B/8B coder model in a tight quantization; bigger models will lean on system RAM and slow down fast.
• –Agentic coding rewards reliable tool use and instruction following more than flashy leaderboard scores, so the fastest stable model often wins.
• –Qwen2.5-Coder-7B is explicitly sized for code work, while DeepSeek-Coder-V2-Lite-Instruct is much larger overall even though only 2.4B parameters are active, so it may still be awkward on this machine without heavy offload.
• –For this setup, local runners, context length, and prompt caching may matter as much as the model choice itself.