Massive local model speedup on Apple Silicon with MLX
Ollama v0.19 rebuilds Apple Silicon inference on top of MLX, bringing much faster local performance for coding and agent workflows. It also adds NVFP4 support and smarter cache reuse, snapshots, and eviction for more responsive sessions.
The engineering in Ollama v0.19 is a massive leap for anyone running local models on macOS. Moving to Apple's native MLX framework changes the game for performance, leveraging the unified memory architecture and the new GPU Neural Accelerators on the M5 chips.
v0.19 now also supports NVFP4, which brings local inference closer to production parity, and the KV cache has been reworked with cache reuse across conversations, intelligent checkpoints, and smarter eviction. For branching agent workflows like @Claude Code or @OpenClaw , that should mean lower memory use and faster responses.
If you have a Mac with 32GB+ of unified memory, you can pull the new Qwen3.5-35B-A3B NVFP4 model and test this right now. Running heavy agentic workflows locally just became a lot more viable!
About Ollama v0.19 on Product Hunt
“Massive local model speedup on Apple Silicon with MLX”
Ollama v0.19 launched on Product Hunt on April 1st, 2026 and earned 425 upvotes and 12 comments, earning #2 Product of the Day. Ollama v0.19 rebuilds Apple Silicon inference on top of MLX, bringing much faster local performance for coding and agent workflows. It also adds NVFP4 support and smarter cache reuse, snapshots, and eviction for more responsive sessions.
On the analytics side, Ollama v0.19 competes within Open Source, Artificial Intelligence and Apple — topics that collectively have 549.9k followers on Product Hunt. The dashboard above tracks how Ollama v0.19 performed against the three products that launched closest to it on the same day.
Who hunted Ollama v0.19?
Ollama v0.19 was hunted by Zac Zuo. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
Zac Zuo
Hi everyone!
The engineering in Ollama v0.19 is a massive leap for anyone running local models on macOS. Moving to Apple's native MLX framework changes the game for performance, leveraging the unified memory architecture and the new GPU Neural Accelerators on the M5 chips.
v0.19 now also supports NVFP4, which brings local inference closer to production parity, and the KV cache has been reworked with cache reuse across conversations, intelligent checkpoints, and smarter eviction. For branching agent workflows like @Claude Code or @OpenClaw , that should mean lower memory use and faster responses.
If you have a Mac with 32GB+ of unified memory, you can pull the new Qwen3.5-35B-A3B NVFP4 model and test this right now. Running heavy agentic workflows locally just became a lot more viable!