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OptiQ
Quantize, serve and code with local models on your Mac
OptiQ is the local-LLM stack for Apple Silicon. It measures each layer's sensitivity and gives sensitive layers more bits, so its mixed-precision quants beat a flat 4-bit quant at the same size. One MLX-native engine runs a CLI and OpenAI/Anthropic-compatible server, a local web workbench (Lab) and a terminal coding agent (Code). On Gemma-4-12B (M3 Max) it decodes 1.7x faster on prose and up to 2.9x on code edits, identical output. 200+ quants on Hugging Face. No PyTorch, no cloud.
OptiQ started from a simple frustration: local models on a Mac usually mean one flat 4-bit quant, whether a layer can take it or not.
OptiQ measures each layer's sensitivity (a KL-divergence pass) and spends the bit budget where it matters. Sensitive layers stay at 8-bit, the rest drop to 4-bit, at the same average size. The gap is biggest on small models: +4.4 Capability Score on MiniCPM5-1B, +3.2 on Spark-X2.5-4B and +1.9 on Qwen3.5-4B over uniform 4-bit.
It's one MLX-native install with three tools: a server that speaks both the OpenAI and Anthropic APIs, a local web workbench (optiq lab) to chat, quantize, fine-tune and compare models, and a terminal coding agent (optiq code) for whatever model you're serving. On Gemma-4-12B on an M3 Max we measured 1.7x faster decode on prose and up to 2.9x on code edits, with identical output (mlx-optiq.com/compare).
200+ quants are on Hugging Face and load with stock mlx_lm.load. `pip install mlx-optiq`
What we'd most like to hear: which model you'd want quantized next, and what breaks on your Mac.
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About OptiQ on Product Hunt
“Quantize, serve and code with local models on your Mac”
OptiQ was submitted on Product Hunt and earned 7 upvotes and 1 comments, placing #56 on the daily leaderboard. OptiQ is the local-LLM stack for Apple Silicon. It measures each layer's sensitivity and gives sensitive layers more bits, so its mixed-precision quants beat a flat 4-bit quant at the same size. One MLX-native engine runs a CLI and OpenAI/Anthropic-compatible server, a local web workbench (Lab) and a terminal coding agent (Code). On Gemma-4-12B (M3 Max) it decodes 1.7x faster on prose and up to 2.9x on code edits, identical output. 200+ quants on Hugging Face. No PyTorch, no cloud.
OptiQ was featured in Developer Tools (521.5k followers) and Artificial Intelligence (481.6k followers) on Product Hunt. Together, these topics include over 219.9k products, making this a competitive space to launch in.
Who hunted OptiQ?
OptiQ was hunted by Latent Node. 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.
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Latent Node
Hey Product Hunt 👋
OptiQ started from a simple frustration: local models on a Mac usually mean one flat 4-bit quant, whether a layer can take it or not.
OptiQ measures each layer's sensitivity (a KL-divergence pass) and spends the bit budget where it matters. Sensitive layers stay at 8-bit, the rest drop to 4-bit, at the same average size. The gap is biggest on small models: +4.4 Capability Score on MiniCPM5-1B, +3.2 on Spark-X2.5-4B and +1.9 on Qwen3.5-4B over uniform 4-bit.
It's one MLX-native install with three tools: a server that speaks both the OpenAI and Anthropic APIs, a local web workbench (optiq lab) to chat, quantize, fine-tune and compare models, and a terminal coding agent (optiq code) for whatever model you're serving. On Gemma-4-12B on an M3 Max we measured 1.7x faster decode on prose and up to 2.9x on code edits, with identical output (mlx-optiq.com/compare).
200+ quants are on Hugging Face and load with stock mlx_lm.load. `pip install mlx-optiq`
What we'd most like to hear: which model you'd want quantized next, and what breaks on your Mac.