Qwen3 thread says 16GB barely helps

This is a comfort upgrade, not a capability leap. 16GB opens a few more 24B-class quants, but it does not change the local-coding tier the way 24GB does.

• –The top reply on [Reddit](https://www.reddit.com/r/LocalLLaMA/comments/1s0nkqi/is_there_actually_something_meaningfully_better/) matches the usual LocalLLaMA take: 12GB to 16GB is marginal, while 24GB is the first truly useful step up.
• –[Qwen3-30B-A3B](https://huggingface.co/Qwen/Qwen3-30B-A3B) is already near the efficient end of the family: 30.5B total, 3.3B active, and 32k native / 131k with YaRN, so the upgrade bottleneck is memory headroom more than raw model size.
• –[Qwen3-Coder-30B-A3B](https://huggingface.co/Qwen/Qwen3-Coder-30B-A3B-Instruct) advertises 256K native context, extendable to 1M with YaRN, but Unsloth's [run guide](https://unsloth.ai/docs/models/tutorials/qwen3-coder-how-to-run-locally) still asks for about 18GB unified memory for decent 4-bit speed.
• –The most interesting 16GB-class option is a 24B model such as [Mistral Small 3.1 24B](https://huggingface.co/muranAI/Mistral-Small-3.1-24B-Instruct-2503-GGUF); its q5 variants land around 15.6-16.5GB and still offer a 128k context window.
• –For 12GB, the safe bet remains 14B-class coders like [Qwen2.5-Coder-14B](https://huggingface.co/Qwen/Qwen2.5-Coder-14B-Instruct) or Qwen3-14B; with 40-120k context, KV cache pressure matters as much as parameter count.
• –Keeping both 12GB and 16GB only helps if the runtime can split or offload cleanly; otherwise a single 24GB card remains the cleaner move.