POCKET-KR-MLX

🆕 POCKET-Qwen3.8-Flash-Next — a 180B model running on a laptop with 8 GB VRAM + 32 GB RAM · 4.17 tok/s measured.

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🆕 POCKET-Zimage-CPU — photoreal images in 46 s on a CPU only. No GPU, no CUDA, no Python.

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📚 Collections

▶ POCKET Models — this family (on-device, no GPU) Darwin Family · Aether Foundation · VKAE Accelerated

POCKET-KR-MLX · 🍎 iPhone / Mac

35B 한국어 모델을 아이폰에서 네이티브로. Apple MLX 2-bit, 5 GB. iPhone·iPad·Mac에서 MLX Swift로 바로 실행.

🚀 Try it live, no install → — both answering on a CPU-only box (no GPU). POCKET-26B is Gemma4-based.

Pick your build →

The POCKET lineup — pick by your device

*Wikipedia-Korean perplexity, lower is better. Q4_K_M = 5.79 baseline. English builds are tuned on English; see each repo. †Separate 80-chunk run (40,960 tokens) on a different model — compare within a model, not across rows.

🍎 Why MLX for Korean but GGUF for English on iPhone? Apple-native MLX only does uniform quantization. Korean survives it; English needs our proprietary quantization, which only GGUF supports — so the English iPhone build ships as a GGUF you run with PocketPal. Honest, not lazy.

🆕 POCKET-26B — a Gemma4-26B-A4B-based sibling that loads in any app today (Ollama · LM Studio · PocketPal · MLX), no bleeding-edge runtime needed: GGUF (Q2_K 11 GB · Q4_K_M 17 GB · GPQA-Diamond 67%). Universal compatibility for 12 GB phones, PC, and browser.

Benchmarks — what is measured, what is not

We measure Bonsai on the same machine with the same stock llama.cpp, and we tell you where we lose.

[measured] Generation speed — POCKET wins on both CPU and GPU:

[measured on a MacBook M3 Pro, 18 GB] — and on a laptop, POCKET wins every axis, including prompt processing:

On a laptop GPU the arithmetic headroom that let Bonsai win prefill on an H100 is gone, so MoE sparsity wins across the board. POCKET-35B-Q2_K runs on the M3 Pro's CPU at 19.5 tok/s — on an 18 GB Mac, run Q2_K on CPU (-ngl 0); its 13 GB exceeds the recommended Metal budget.

[measured — GPQA Diamond, 198q, greedy] reasoning quality vs quantization:

[pending — community reports welcome] on-device iPhone and Strix Halo throughput. We publish only what we ran ourselves; help us fill the rest.

The same-size rival Ternary-Bonsai-27B-Q2_0 (7.2 GB) fails to load in upstream llama.cpp — it needs the PrismML fork. POCKET runs on the tools you already have.

Files in this repo

Apple-silicon native (Metal). For Android/PC use the GGUF build.

Quickstart (Mac)

pip install mlx-lm
mlx_lm.generate --model FINAL-Bench/POCKET-KR-MLX --prompt "안녕하세요"

On iPhone/iPad: MLX Swift examples.

⚠️ On-device speed is not yet measured by us — reports welcome.

Lineage — where POCKET comes from

POCKET is quantized from Darwin-36B-Opus, VIDRAFT's flagship — a model bred and evolved over several generations on the Darwin platform (crossbreeding, healing, expert surgery). Darwin-36B-Opus itself traces back to a Qwen3.5-family MoE architecture.

The CPU/GPU speed comes from the sparse-MoE architecture plus ordinary quantization — reproducible with the same base and the same tools. What we add is the Darwin-evolved weights, the honest measurement, the Korean tuning, and the pruning that makes the 5 GB phone builds.

Limitations

• The iPhone/Mac speed is not yet measured by us — community reports welcome.
• Extreme quants (IQ1_M) hurt Korean ~2.8× more than English; use Q2_K or larger for quality.
• English phone builds trade quality for size; the PC build (PC-mix) is much closer to full quality.

License

Apache-2.0.

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POCKET is a VIDRAFT model family. 35B, in your pocket. No GPU.

Learn more

• On-device LLMs without a GPU — and how POCKET measures up: Can you run a large LLM without a GPU?
• What model quantization is, and why a 4-bit model stays smart: What is model quantization?

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🧩 The POCKET Family — On-device AI by VIDRAFT

Big models, small hardware. No GPU, no cloud.

Models

• 📦 POCKET-35B-GGUF — flagship, PC / server, no GPU
• 📦 POCKET-26B-GGUF — compact 26B
• 🇰🇷 POCKET-KR-GGUF — Korean, Android
• 🍎 POCKET-KR-MLX — Korean, iPhone / Mac
• 🌍 POCKET-EN-GGUF — English, phone / PC
• 💻 POCKET-Qwen3.8-Flash-Next-GGUF — 180B on a laptop (8 GB VRAM + 32 GB RAM)
• 🖼️ POCKET-Image-Zimage — character-perfect text in any image
• 🖥️ POCKET-Zimage-CPU — photoreal images on a CPU only

Demos & tools (Spaces)

• 🎨 POCKET-Image Studio — text-in-image, generate in-page
• 🖥️ POCKET-35B-CPU — 35B answering on a CPU
• 🖥️ POCKET-26B-CPU — 26B on a CPU
• 🖼️ POCKET-Zimage-CPU — image generation on a CPU

📚 Full POCKET collection

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