Alhena AI vs Ollama

Ollama's current window is almost entirely about what it can run and how fast it runs it. Qwen 3.8 27B arrives in v0.32.12 with a separately tuned MLX variant for Apple Silicon, and v0.32.13 completes that model's steering surface a day later. The rest is quantization and prefill work — NVFP4 global-scale kernel fusion for roughly 7-8% faster prefill — plus launch integrations for third-party coding harnesses. v0.32.14 is the smallest entry in the window: WebP transcoding for llama-server and a qwen renderer that no longer insists system messages come first.

MLX is no longer a side path here. Every recent model addition lands with an Apple Silicon build tuned separately from the CUDA one, and the performance and defaults work — NVFP4 fusion, repeat_penalty matched to what other engines do — reads as Ollama closing the gap with the runtimes it gets benchmarked against rather than differentiating from them. What v0.32.14 adds to the picture is the maintenance tail: input-format and message-shape fixes arriving days behind a model launch, which is what tracking someone else's release schedule actually costs.

Expect the next notable release to be another same-week model addition with a paired MLX build, since four of the last six entries take that shape, with small renderer and input-handling patches trailing it. Whether the coding-harness integrations keep accumulating is harder to call — v0.32.11 is the only entry in this window that touches them.