---
created: 2026-06-29
updated: 2026-06-29
sources:
  - xpost/2026-06-29_paulcouvert-local-llm-laptop-guide.md
  - concepts/llm/mlx-moe-local-ai-optimization.md
  - concepts/hardware/cloud-exit-and-local-superiority.md
  - concepts/llm/llm-model-catalog.md
  - concepts/llm/chinese-model-cost-routing.md
tags: [concept, llm, local-ai, laptop, quantization, unsloth, qwen, gemma, parakeet, llama-cpp, lm-studio, edge-ai, voice-to-text, diffusion, moe]
---

# Local LLM Laptop Guide — Best Models Without a $10k Mac Studio

> **TL;DR:** Paul Couvert (@itsPaulAi) lists the best local models for laptop-class hardware. Top picks: Qwen3.6-27B for coding, Gemma 4 12B for everyday tasks, Parakeet 0.6B v3 for voice-to-text, Gemma 4 E4B for phone-class edge inference, and Gemma 4 26B diffusion for maximum tok/s. Key tips: use Unsloth quantized versions, run via LM Studio or llama.cpp, try Google AI Edge Gallery on mobile. Complements Jun Song's MLX MoE optimization post — both converge on the Qwen3.6-35B-A3B MoE variant.

## Source

| Source | Author | Date | Engagement |
|--------|--------|------|------------|
| [X-Post: "Best local models on a laptop"](https://x.com/itsPaulAi/status/2071717503105900993) | Paul Couvert (@itsPaulAi) | 2026-06-29 | 138 likes, 12 reposts, 170 bookmarks, ~13.5K views |

**Shared by:** Pit Weber in OME-Gruppe, Topic "Tips & Tricks" (Topic 27)

---

## The Model Stack

### 1. Coding: Qwen3.6-27B (or Qwen3.6-35B-A3B for speed)

| Dimension | Qwen3.6-27B (Dense) | Qwen3.6-35B-A3B (MoE) |
|-----------|---------------------|------------------------|
| **Total params** | 27B | 35B |
| **Active params/token** | 27B (all active) | 3B (sparse) |
| **Best for** | Coding agents — "by far the best" | Coding, but faster |
| **Laptop fit** | Needs quantization (Unsloth) | MoE sparse activation = faster on limited hardware |
| **Cross-ref** | Jun Song warns: slow on Mac/MLX | Jun Song's MoE preference aligns |

> **⚠️ Tension with Jun Song:** Jun Song explicitly says dense 27B on Mac is "painfully slow." Paul recommends it for coding on any laptop — likely assuming Unsloth quantization on non-Mac hardware. The **MoE variant (35B-A3B)** is where both agree: it's the faster, better-suited option for constrained hardware. See [resolution below](#resolving-the-dense-27b-tension).

### 2. Everyday Tasks: Gemma 4 12B

| Dimension | Detail |
|-----------|--------|
| **Size** | 12B |
| **Best for** | Everyday tasks and questions |
| **Why** | Balanced size-to-quality ratio for general use |
| **Runtime** | LM Studio, llama.cpp, or Google AI Edge Gallery |

### 3. Voice-to-Text: Parakeet 0.6B v3

| Dimension | Detail |
|-----------|--------|
| **Size** | 0.6B — tiny |
| **Best for** | Speech-to-text (STT) |
| **Claim** | "Basically the best voice-to-text model you can find" |
| **Relevance** | Local Whisper alternative; relevant for OpenClaw voice interaction pipelines |
| **Edge fit** | Small enough to run on any laptop or phone |

### 4. Edge / Phone: Gemma 4 E4B

| Dimension | Detail |
|-----------|--------|
| **Size** | ~4B |
| **Best for** | On-device inference, offline phone use |
| **Claim** | "Surprisingly good for the size" |
| **Mobile deployment** | Google AI Edge Gallery (iOS/Android) |
| **Cross-ref** | Extends [Cloud-Exit thesis](../hardware/cloud-exit-and-local-superiority.md) to mobile — connects to [OpenClaw mobile apps](../../tools/openclaw.md) |

### 5. Speed Champion: Gemma 4 26B Diffusion

| Dimension | Detail |
|-----------|--------|
| **Size** | 26B |
| **Type** | Diffusion-based LLM |
| **Best for** | Maximum tok/s on local hardware |
| **Claim** | "The most tok/s you can have with a local model" |
| **Architecture note** | Diffusion LLMs generate tokens via iterative denoising — different paradigm from standard AR models. See [[post-transformer-llm-architectures.md]] for DeepMind's diffusion LLM research (10× efficiency gains). |

---

## Practical Deployment Tips

### Quantization: Unsloth

[Unsloth](https://github.com/unslothai/unsloth) provides quantized model versions optimized for laptop deployment:

| Benefit | Detail |
|---------|--------|
| **Precision/size/speed** | "Fantastic" balance — Paul's explicit recommendation |
| **Compatibility** | Works with LM Studio, llama.cpp |
| **How to use** | Download Unsloth quantized GGUF files → load in LM Studio or llama.cpp |

### Runtime Options

| Runtime | Type | Best For |
|---------|------|----------|
| [LM Studio](https://lmstudio.ai) | GUI app | Easy setup, model browsing, chat interface |
| [llama.cpp](https://github.com/ggerganov/llama.cpp) | CLI / library | Maximum control, minimal overhead, embedding in apps |
| [Google AI Edge Gallery](https://ai.google.dev/edge) | Mobile app | On-device inference on iOS/Android |

---

## Resolving the Dense 27B Tension

Paul recommends Qwen3.6-27B (dense) for coding. Jun Song says dense 27B on Mac is "painfully slow." This isn't a contradiction — it's a **context distinction**:

| Factor | Paul's Context | Jun Song's Context |
|--------|---------------|-------------------|
| **Hardware** | Any laptop (x86, ARM, Mac) | Specifically Apple Silicon / MLX |
| **Quantization** | Unsloth quantized (explicitly recommended) | Dequantized (dq) MoE models |
| **Framework** | llama.cpp / LM Studio (CPU/GPU offload) | MLX (Apple native) |
| **Dense 27B verdict** | ✅ Good with Unsloth quants | ❌ "Painfully slow" on MLX |
| **MoE 35B-A3B verdict** | ✅ "Faster option" | ✅ Implied by MoE preference |

**Resolution:** The performance of dense 27B depends heavily on **framework + quantization**:
- **Unsloth quantized on llama.cpp** (Paul's path): tractable on commodity laptops
- **Unquantized/dq on MLX** (Jun Song's path): slow because MLX doesn't optimize for dense
- **MoE variant (35B-A3B)**: optimal on both paths — sparse activation reduces compute per token

**Recommendation:** If you're on a Mac, follow Jun Song's advice (MoE models via MLX). If you're on any laptop with llama.cpp + Unsloth quants, Paul's dense 27B recommendation works. When in doubt, pick the MoE variant (Qwen3.6-35B-A3B) — it's the convergence point.

---

## Complement to MLX MoE Optimization

This page is the **cross-platform companion** to [[mlx-moe-local-ai-optimization.md]]:

| Dimension | [MLX MoE Optimization](mlx-moe-local-ai-optimization.md) | This Page (Laptop Guide) |
|-----------|----------------------------------------------------------|--------------------------|
| **Scope** | Apple Silicon / MLX | Any laptop (x86, ARM, Mac) |
| **Key insight** | MoE > Dense on MLX (unified memory) | Quantized models work on any laptop |
| **Model sources** | Minimax-M3.0, DeepSeek-v4-Flash (Chinese MoE) | Qwen3.6, Gemma 4, Parakeet (broader mix) |
| **Focus** | Why MoE works on Apple Silicon | What to run + how (runtime, quantization) |
| **Unique coverage** | Info gap thesis (frontier AIs can't tell you) | STT (Parakeet), phone-edge (Gemma E4B), diffusion speed (Gemma 26B) |

**Together:** Jun Song tells you *why* MoE on Mac is better. Paul tells you *what* to run on any laptop and *how*. The MoE variant Qwen3.6-35B-A3B is the overlap — both recommend it, for different reasons.

---

## Model Landscape Summary

| Use Case | Model | Size | Architecture | Hardware Floor |
|----------|-------|------|--------------|----------------|
| Coding (best) | Qwen3.6-27B | 27B | Dense | Laptop w/ quantization |
| Coding (fast) | Qwen3.6-35B-A3B | 35B/3B active | MoE | Laptop |
| Everyday | Gemma 4 12B | 12B | Dense | Laptop |
| Voice-to-text | Parakeet 0.6B v3 | 0.6B | STT | Any device |
| Phone/edge | Gemma 4 E4B | ~4B | Dense | Phone |
| Speed champion | Gemma 4 26B diffusion | 26B | Diffusion LLM | Laptop (GPU helpful) |

---

## Connection to Our Setup

### Model Routing Alignment

| Model | Paul's Role | Our Role | Match |
|-------|-------------|----------|-------|
| Qwen3.6-35B-A3B | "Faster coding option" (MoE) | Not yet in active routing — candidate | Potential addition |
| Qwen3.6-27B | "Best for coding agents" | Not in active routing | Reference point |
| Gemma 4 12B | "Everyday tasks" | Not in active routing | Consider for lightweight tasks |

### Cloud-Exit Extension

Paul's guide extends the Cloud-Exit thesis to **laptop-class hardware** — you don't need a Mac Studio, DGX Station, or Strix Halo:

| Hardware Tier | Cloud-Exit Option | Source |
|---------------|-------------------|--------|
| Phone | Gemma 4 E4B via Google AI Edge Gallery | Paul Couvert |
| Laptop (any) | Qwen3.6/Gemma 4 with Unsloth quants via LM Studio/llama.cpp | Paul Couvert |
| Mac (Apple Silicon) | MoE models (Minimax-M3.0, DeepSeek-v4-Flash) via MLX | Jun Song |
| Prosumer desktop | AMD Strix Halo (128 GB) | [[../hardware/edge-inference-als-cloud-alternative.md]] |
| Enterprise desktop | NVIDIA DGX Station (748 GB) | [[../hardware/nvidia-dgx-station-748gb.md]] |

---

## Cross-References

- [[mlx-moe-local-ai-optimization.md]] — Jun Song's MLX MoE post (direct complement, Apple Silicon focus)
- [[../hardware/cloud-exit-and-local-superiority.md]] — Cloud-Exit thesis (this page extends to laptop tier)
- [[../hardware/edge-inference-als-cloud-alternative.md]] — AMD Strix Halo (edge inference, x86)
- [[../hardware/nvidia-dgx-station-748gb.md]] — NVIDIA DGX Station (enterprise tier)
- [[llm-model-catalog.md]] — Consolidated model catalog
- [[chinese-model-cost-routing.md]] — DeRonin's cost routing (Qwen models in cloud→local swap)
- [[post-transformer-llm-architectures.md]] — Diffusion LLMs (Gemma 4 26B diffusion context)
- [[../../tools/openclaw.md]] — OpenClaw mobile apps (Gemma E4B on-phone connection)
- [[ai-search-language-blind-spot.md]] — "Local" als Querschnittsthema: Quellen-Routing (lokale Sprache) analog zu Edge-Inferenz (lokale Hardware)

## External Links

- [Original X-Post by Paul Couvert](https://x.com/itsPaulAi/status/2071717503105900993)
- [Unsloth GitHub](https://github.com/unslothai/unsloth)
- [LM Studio](https://lmstudio.ai)
- [llama.cpp](https://github.com/ggerganov/llama.cpp)
- [Google AI Edge Gallery](https://ai.google.dev/edge)