mlx-optiq Local Models
Okyasoft Pte Ltd v0.1.2
Run models on your Mac
From the developer
Not specified in the available declarations: capabilities, limitations, pricing & account requirements.
Onboarding example
Check if my Mac can run a local model and serve one
Sources & dates
Developer declarations; not independently tested. Listing last checked Nov 14, 2026. Package observed Nov 14, 2026.
Description
From the marketplace listing
Guides you through running a quantized language model on your own Apple Silicon Mac with mlx-optiq. The workflow checks your Mac's memory, checks that mlx-optiq is installed (and points you to the install steps if it is not, without installing anything itself), helps you pick a model that fits, and, once you confirm the download, starts it with optiq serve as a local chat-completions endpoint. It shows how to connect a client to that endpoint, run a quick speed and quality check, and troubleshoot common problems such as running out of memory. It states the limits plainly: KV-cache quantization saves memory but can slow decoding, long contexts need memory, and quantized models should be checked on your own tasks instead of trusting speed numbers. This plugin contains instructions only; everything runs on your own machine.
Language: English · Automatically detected from descriptions.
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Publisher full description
Guides you through running a quantized language model on your own Apple Silicon Mac with mlx-optiq. The workflow checks your Mac's memory, checks that mlx-optiq is installed (and points you to the install steps if it is not, without installing anything itself), helps you pick a model that fits, and, once you confirm the download, starts it with optiq serve as a local chat-completions endpoint. It shows how to connect a client to that endpoint, run a quick speed and quality check, and troubleshoot common problems such as running out of memory. It states the limits plainly: KV-cache quantization saves memory but can slow decoding, long contexts need memory, and quantized models should be checked on your own tasks instead of trusting speed numbers. This plugin contains instructions only; everything runs on your own machine.
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First seen Oct 28, 2026. Last seen Nov 14, 2026.
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Files & skills
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Skill instructions
optiq-local-models5.3 KB
---
name: optiq-local-models
description: Serve a quantized language model locally on an Apple Silicon Mac with mlx-optiq and an OpenAI-compatible endpoint. Use when the user wants to run a model on their own Mac, pick a model that fits their memory, connect a tool to a local server, or check local speed and quality.
---
Use this skill when the user wants to run a language model on their own Apple Silicon Mac with mlx-optiq (https://mlx-optiq.com). Do not use it for Intel Macs, Windows, or hosted-model advice.
Run the read-only checks below yourself and show the output. This skill does not install software. Ask the user before downloading a model (downloads are several GB) or starting a server.
## 1. Check the Mac
```bash
uname -m # expect arm64
sysctl -n hw.memsize | awk '{printf "%.0f GB\n", $1/1073741824}'
python3 --version # mlx-optiq needs Python 3.11+
```
Memory is the main limit. A model's weights plus its KV cache must fit with room left for macOS and other apps.
## 2. Check that mlx-optiq is installed
This skill does not install mlx-optiq. Check whether it is already available:
```bash
optiq --version
optiq --help
```
If `optiq` is not found, tell the user it is not installed and point them to the install steps on https://mlx-optiq.com (it is published on PyPI as `mlx-optiq` and needs Python 3.11+). Do not run install commands on the user's behalf. Continue once the user confirms it is installed.
## 3. Pick a model
The models page (https://mlx-optiq.com/models) lists quantized models. Pick one whose download size is clearly below the Mac's memory, roughly 60 percent or less as a starting point (this is a rule of thumb, not a guarantee). Smaller models fit more context. Quant repo ids look like `mlx-community/<model>-OptiQ-4bit`. Check the id on the models page before using it. The help text of `optiq serve` uses `mlx-community/Qwen3.5-9B-OptiQ-4bit` as its example.
## 4. Serve
```bash
optiq serve --model mlx-community/Qwen3.5-9B-OptiQ-4bit --host 127.0.0.1 --port 8080
```
Starting the server downloads the model on first use, so confirm with the user before running it. Let the download finish; do not restart it. Flags confirmed in `optiq serve --help`:
- `--max-context auto|N|off`: caps context so a long prompt cannot crash the server. `auto` is the default.
- `--max-concurrent N`: how many requests decode in parallel (default 8). Lower it on a tight-memory Mac.
- `--idle-timeout SECONDS`: unload the model after idle time to free memory (default off).
- `--stream-experts`: for large mixture-of-experts models that do not fit in memory; the help says decode is slower.
- `--kv-bits 4|8`, `--kv-config FILE`: KV-cache quantization (see Limits).
- `--mtp`, `--ngram-draft N`, `--drafter ID`: optional speculative decoding. Only use them if the model supports it, and check the output as in step 6.
- `--host`, `--port` and other mlx-lm server options are forwarded.
Keep the host at `127.0.0.1` unless the user wants other machines to reach the server, and say that exposing it on a network has security consequences.
## 5. Connect a client
The server speaks the OpenAI chat API at `http://127.0.0.1:8080/v1`. `optiq serve --help` lists the other endpoints it exposes. Smoke test:
```bash
curl http://127.0.0.1:8080/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{"model":"mlx-community/Qwen3.5-9B-OptiQ-4bit","messages":[{"role":"user","content":"Say hello in five words."}],"max_tokens":32}'
```
For an SDK or tool, set its base URL to `http://127.0.0.1:8080/v1`. A local server needs no real key, but some clients insist on a non-empty value; if auth is on, the help says Bearer tokens must start with `sk-optiq-` (a missing header is allowed). By default the `model` field is only a label and the one served model answers.
## 6. Quick speed and quality check
Speed: send one short and one long prompt, and time them (`time curl ...`, or read the Preview of the first 4,000 characters. Read full instructions →
Referenced files: 1 — file references are metadata, not package contents.
Package details
Publisher declarations from the archived package. These are separate from our research and the live service's terms.
- Package author
- Okyasoft Pte Ltd
- Keywords
- See keywords
Package observed Nov 14, 2026.
Technical details
- First seen
- Oct 28, 2026 · 12:00 UTC
- Last seen
- Nov 14, 2026 · 12:00 UTC
- Collection status
- Collected
plugins_6abdebf357bc8191991d153411eec76e
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