Devstral Small 2 vs Qwen: coding models for 16GB VRAM

This model-card comparison examines six coding models for a GPU with 16GB of VRAM. The speaker judges them by memory headroom and usefulness during repeated coding requests, rather than parameter count alone. Model weights share memory with the runtime and KV cache, so a download that fits does not guarantee room for repository context or an AI agent's growing tool history.

Qwen 2.5 Coder 14B is the speaker's starting recommendation for slower cards: its cited Ollama build is around 9GB with a 32,000-token context window. DeepSeek Coder V2 Lite is a roomier fallback, while StarCoder2 15B gets a narrower recommendation for completion and fill-in-the-middle work. Codestral 22B remains a contender, though the speaker flags its 32,000-token context and licensing restrictions for commercial use.

Qwen3 Coder 30B has an appealing agentic design, but its cited 19GB package exceeds the GPU budget. The speaker discusses system RAM offload and reduced context as compromises, not a clean fit.

The preferred local LLM is Devstral Small 2, described as a 24B software engineering model with Apache 2.0 licensing and a roughly 15GB Ollama package. That recommendation depends on sensible quantization, capped context and selective repository input. The video also explains why GPU bandwidth and runtime support affect responsiveness: equal VRAM capacity does not make an RTX 4060 Ti and RTX 5080 equivalent coding setups.