POCKET-26B-GGUF

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

<!-- POCKET-FLASHNEXT-BADGE -->

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

<!-- POCKET-Zimage-CPU-BADGE -->

📚 Collections

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

POCKET-26B-GGUF · 한국어

A Gemma4-26B-A4B-based pocket model that loads in any app today — Ollama, LM Studio, PocketPal — with no bleeding-edge runtime needed. Korean-tuned, GPU-optional.

🚀 Try it live on a CPU (no GPU), no install →

Pick your build →

Why this one?

POCKET-26B takes Google's Gemma4-26B-A4B (25.2B total, ~4B active MoE, Apache-2.0) and re-quantizes it with our proprietary Korean-tuned quantization — unpruned, so quality holds. Unlike our Qwen-based POCKET (which needs a very recent llama.cpp build for its qwen35moe architecture), Gemma4 loads in every mainstream runtime today: Ollama, LM Studio, PocketPal, koboldcpp, and the browser.

Quality — GPQA-Diamond, greedy, 198 questions (our harness)

Single greedy pass, 198 items → ±~3 pp noise. Our proprietary Korean-tuned quantization is statistically lossless vs the base.

Files in this repo

Our mixed-precision quantization keeps the most quality-critical weights at higher precision — that is why Q2_K holds 67.2% while a plain uniform Q2 collapses to ~44%.

Quickstart — loads anywhere

# stock llama.cpp — brew / winget / apt, or LM Studio / Ollama / PocketPal
llama-cli -m POCKET-26B-Q2_K.gguf -p "대한민국의 수도는?" -ngl 0 -t 8

No fork, no bleeding-edge build — Gemma4 support has shipped in every mainstream runtime since April 2026.

Lineage (honest)

Based on google/gemma-4-26B-A4B-it (Apache-2.0). We do not re-host it unchanged — we add our proprietary Korean-tuned quantization (VIDRAFT). We deliberately do not prune it: Gemma4's low-bit robustness collapses under pruning (measured), so we keep all 128 experts and win on quality + universal compatibility instead.

Limitations

• For 8 GB phones (~5 GB budget), use POCKET-KR-GGUF (5.1 GB) — Gemma4 cannot be shrunk that far without collapse.
• On-device iPhone/Mac throughput not yet measured by us — community reports welcome.

Learn more

• Why on-device LLMs matter, 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?

License

Apache-2.0 — use, modify, redistribute freely.

---

POCKET is a VIDRAFT model family. Runs anywhere, no GPU.

<!-- POCKET-FAMILY -->

---

🧩 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

<!-- /POCKET-FAMILY -->