Readit NewsWhat usages can be reasonable to expect from them? Are there uses out of the box or does one have to go through some custom post-training to get useful behavior?
I feel like there is a huge gap between understanding models as a user of commercial tools and the kind of discussions happening in these threads, but I’m not sure what are the in-between steps.
- private, on-device models (possibly with lower latency than models via web API); also edge devices
- algorithm research (faster and cheaper to prototype new ideas)
- cheap tasks, like classification/categorization; sure, you don't need a decoder-style LLM for that, but it has the advantage of being more free-form, which is useful in many scenarios; or maybe a sanity checker for grammar; or even a router to other model (GPT-5 style)
tldr; I'll save you a lot of time trying things out for yourself. If you are on a >=32 GB Mac download LMStudio and then the `qwen3-coder-30b-a3b-instruct-mlx@5bit` model. It uses ~20 GB of RAM so a 32GB machine is plenty. Set it up with opencode [1] and you're off to the races! It has great tool calling ability. The tool calling ability of gpt-oss doesn't even come close in my observations.
This is contrary to what I've seen in a large ML shop, where architectural tuning was king.
From that table, the A100 tok/sec (larger is faster) numbers are:
- Eager: 28
- Compiled: 128
And
- KV cache eager: 26
- KV cache compiled: 99
The reason that the KV cache is slower is likely because it's not GPU-optimized code. On CPU the KV cache is faster. To make it faster on GPU, you would pre-allocate the tensors on the device for example instead of `torch.cat`ting them on the fly