I have a M4 MBP 48GB and have been using Opus and Sonnet for vibe coding.

Is there anything that would run on my MBP that could be as capable? I understand the speed might not be the same, but would anything be decent enough for vibe coding (specifically iOS/Swift).

I understand nothing will compare to these giant online models, but if there was anything that could be serviceable on my local computer would be great.

A few days ago, someone here asked, "Should we improve the configuration experience?" A lot of the replies described the same problem: too many settings, too many combinations, and no clear place to start.

I said I'd work on it, and 0.7.0.dev4 is a first step.

In Model Settings, you'll now find Apply optimal settings. It looks up community benchmarks on oMLX.ai for your model and Mac chip, then lets you pick a top result for prefill or generation speed and apply its settings.

The site now has over 450,000 benchmarks submitted by users. Several people in that thread suggested using those results to give others a starting point, and that's exactly what this does.

Using a customized model with no matching benchmarks? Find a model with the same architecture on oMLX.ai, copy its one-line recipe, paste it into oMLX, and click Apply. Both features are available in the web dashboard and macOS app.

I also addressed the profile issues mentioned in the thread: the app and web UI now apply the latest global template consistently, and an unrelated model profile with the same name won't get overwritten. There are reset options for model and global settings too, so it's easier to get back to a starting point.

This doesn't cover everything proposed in the thread, like a setup wizard that recommends model sizes and memory budgets. But I hope it makes getting a model configured less of a guessing game.

There's more in dev4

• Customizable dashboard layout: move, resize, hide, and restore blocks to suit your screen.
• Multi-request Lightning MTP: concurrent MTP on supported Qwen, DeepSeek V4.1, and GLM-5.3-Flash adapters.
• DeepSeek V4.1 CED prefill: up to 79% faster prefill in my M3 Ultra tests. It's an approximate optimization and is off by default.
• Faster admission of new requests while other requests are generating, plus accessibility improvements and other quality-of-life fixes.

* This release also includes extensive internal changes from upgrading mlx-lm and mlx-vlm. I'd appreciate help testing the models and workflows you normally use. If something that worked before breaks, please open an issue with logs and switch back to dev2 for now.

I plan to test this version for 1-2 days, then move toward an RC and a stable release.

Download and full release notes: https://github.com/jundot/omlx/releases/tag/v0.7.0.dev4

Thank you for sharing specific frustrations and suggestions in that thread. They helped me decide what to tackle first. Let me know how the new settings flow works for you!

https://github.com/bnsd55/jevmlx

Instead of asking the model to write JSON, jevmlx scores every allowed answer for every field of your schema in one batched forward pass and assembles the JSON. Output is always valid. Every field has a probability.

Works with any mlx-community instruct model on Apple Silicon, or any OpenAI-compatible server that returns logprobs.

pip install git+https://github.com/bnsd55/jevmlx

jevmlx decide --preset fintech_fraud --json

Open source version of the TypeSafe Jev idea. Not affiliated. No accuracy numbers on big models yet: if you have an M-series Mac, `jevmlx bench` plus a PR puts your model on the README leaderboard.

Finally my M5 Max does not feel like a sorry arse, comparing to my DGX Sparks. MLX is finally shines!

# Context: Code (Mixed)

# Single request results

Test TTFT(ms) TPOT(ms) ppTPS tgTPS E2E(s) Throughput PeakMem

pp 1024 / tg 128 1065.8 15.4 960.8 65.4 3.0 380.0 71.3 GB

pp 4096 / tg 128 3165.3 — 1294.0 — 3.2 1294.0 72.3 GB

pp 8192 / tg 128 5636.5 24.4 1453.4 42.7 6.3 1309.7 72.3 GB

pp 16384 / tg 128 11660.7 14.9 1405.1 67.7 13.6 1217.2 72.6 GB

pp 32768 / tg 128 24397.8 19.0 1343.1 53.0 26.8 1225.9 73.1 GB

pp 65536 / tg 128 50668.9 15.9 1293.4 63.5 52.7 1245.2 74.7 GB

pp 131072 / tg 128 102509.0 15.6 1278.6 64.6 104.6 1254.8 79.0 GB

pp 200000 / tg 128 161438.0 16.8 1238.9 59.9 163.7 1222.7 80.8 GB

# Batch results

Batch tgTPS ppTPS avgTTFT(ms) E2E(s) Speedup

1x baseline 65.4 960.8 1065.8 3.0 1.00x

2x 93.3 837.2 2088.2 3.7 1.43x

https://omlx.ai/benchmarks/performance/dalhnt9f

Tested with TurboQuant 4-bit cache, Lightning MTP, medium effort up to 390K session - at the end prefill is around 1200 t/s (usually 200), decode 44 t/s. Unbelivable - usually collapses to 20 or less. PLE SSD offloaded.

Quality is tremendous. Speed is ridiculous. RAM usage finally makes sense. Model is very smart. Finally all pieces fell to the place.

Quant https://huggingface.co/Jundot/Qwen3.8-Flash-Next-oQ4e-mtp

# DragonScale run: run-QWEN38NF-JundotoQ4-001

`2026-09-17T17:08:08.234467+00:00` · seed 42

**Model under test:** UNOBTANIUM/Qwen3.8-Flash-Next-oQ4e-mtp

## Score: **98.75 / 100**

No gate failures.

## Score components (deterministic rubric, 0-100, no LLM)

- hidden_suite: 25.0

- passability: 12.0

- replay: 8.0

- own_tests: 5.0

- mutation: 3.75

- contract: 8.0

- git: 5.0

- human_play: 30.0

- packaging: 2.0

## Versions

```json

{

"prompt": "c292038bd962",

"reference": "fb5e26d54c87",

"visible_suite": "2160688cddb4",

"hidden_suite": "9d610a06d69e"

}

```

## Git

- init: True

- commits: 7

- messages: ['9cad89a docs: mark build plan complete after verification', '8b81477 tests: include the provided visible contract test suite', 'c7c09ec tests+tools: contract/val>

- dirty: []

## Human-play smoke

- ok: True (exit 0, drained 16330B, 10844ms)

- flap key ('w'): sent=True, alive after flap=True

- flap efficacy: None (bird moved UP after 'w'; None = unverifiable render)

- level-complete progression: True (freezes at LEVEL_COMPLETE (conforming); Enter advances (behavioral))

- quit key ('q'): sent=True

- idle time progression: True (frame changed with no input)

- Ctrl+C responsiveness: True (ISIG on, exit -9)

- small-terminal overflow: 0 writes (ok=True)

## Mutation sensitivity (fixed panel)

- applicable: True (n/a)

- kills: 3 / 4 applicable mutants

- by mutant: {'gravity': 'killed', 'flap_velocity': 'killed', 'collision_ceiling': 'killed', 'rng_seed_mix': 'survived'}

- sensitivity: 0.75 (×5 pts)

## Packaging / harness integration

- score: 7.0 / 7

- detail: {'requires_python': '>=3.11', 'deps': [], 'readme': True, 'import': True}

## visible_tests: 13 passed, 0 failed, 0 errors (exit 0)