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🖼️ POCKET-Image — the POCKET series goes visual: character-perfect text in any language, on-device A new model in VIDRAFT's POCKET family. POCKET put 35B-class models on phones and no-GPU PCs. POCKET-Image carries the same "big capability, small hardware" idea into image generation — and fixes the one thing nearly every image model gets wrong: text. Type "안녕하세요" into a typical model and you get "안ㅐ기." Hangul alone composes 11,172 syllable blocks; Arabic connects its letters; Thai stacks marks. Diffusion models draw scripts as shapes, so they smear. POCKET-Image renders every glyph exactly — 한국어 · 中文 · 日本語 · العربية (RTL) · ไทย · Latin and more — onto any scene you describe. What it is: • 100% accurate text, any language — where global models produce gibberish • Any background from a prompt — text is optional (empty → a pure image) • No GPU, no NPU — runs on plain CPU + RAM via the POCKET-Core engine • Measured footprint: 8.6 GB (RTX 3050/4060) · 4.5 GB (offloaded, 6 GB cards) · 13.4 GB (MacBook, 16 GB+) • Windows · macOS · Linux · fully local, no cloud Built on the open, commercial-friendly Z-Image (Apache-2.0) foundation. Honest note: the text is the guaranteed-correct part — the surrounding scene is ordinary generation, so a busy foreground can crowd the letters. We say so; clean backgrounds stay razor-sharp. 🎨 Studio — generate right here, any language: https://huggingface.co/spaces/FINAL-Bench/POCKET-Image-Studio 🧩 Model card: https://huggingface.co/FINAL-Bench/POCKET-Image-Zimage 📚 The POCKET collection: https://huggingface.co/collections/FINAL-Bench/pocket-models

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FINAL-Bench/POCKET-Image-Studio

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FINAL-Bench/POCKET-Image-Zimage

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4482

🖼️ POCKET-Image — the POCKET series goes visual: character-perfect text in any language, on-device

A new model in VIDRAFT's POCKET family. POCKET put 35B-class models on phones and no-GPU PCs. POCKET-Image carries the same "big capability, small hardware" idea into image generation — and fixes the one thing nearly every image model gets wrong: text.

Type "안녕하세요" into a typical model and you get "안ㅐ기." Hangul alone composes 11,172 syllable blocks; Arabic connects its letters; Thai stacks marks. Diffusion models draw scripts as shapes, so they smear. POCKET-Image renders every glyph exactly — 한국어 · 中文 · 日本語 · العربية (RTL) · ไทย · Latin and more — onto any scene you describe.

What it is:
• 100% accurate text, any language — where global models produce gibberish
• Any background from a prompt — text is optional (empty → a pure image)
• No GPU, no NPU — runs on plain CPU + RAM via the POCKET-Core engine
• Measured footprint: 8.6 GB (RTX 3050/4060) · 4.5 GB (offloaded, 6 GB cards) · 13.4 GB (MacBook, 16 GB+)
• Windows · macOS · Linux · fully local, no cloud

Built on the open, commercial-friendly Z-Image (Apache-2.0) foundation.

Honest note: the text is the guaranteed-correct part — the surrounding scene is ordinary generation, so a busy foreground can crowd the letters. We say so; clean backgrounds stay razor-sharp.

🎨 Studio — generate right here, any language:
FINAL-Bench/POCKET-Image-Studio

🧩 Model card:
FINAL-Bench/POCKET-Image-Zimage

📚 The POCKET collection:
https://huggingface.co/collections/FINAL-Bench/pocket-models

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POCKET-Image Studio

🖼

Perfect text in any language, in any image

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FINAL-Bench/POCKET-Image-Zimage

Text-to-Image • Updated 1 day ago • 24

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POCKET-26B vs Bonsai · CPU (Gemma4-based)

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BONSAI vs POCKET — 26B Gemma4 MoE out-runs a 27B on CPU

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FINAL-Bench/POCKET-26B-GGUF

Text Generation • 25B • Updated 1 day ago • 875 • 33

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Quantum Cryptanalysis on IBM Quantum Hardware: Extending Even--Mansour Period Recovery from N=4 to N=10

Paper • 2607.18340 • Published 8 days ago • 21

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Article

POCKET: a 35-billion-parameter model that runs on your iPhone — and on your PC with no GPU

FINAL-Bench

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5 days ago

• 12

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POCKET vs Bonsai · CPU

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BONSAI vs POCKET — 35B MoE out-runs the top 1-bit 27B on CPU

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POCKET-MODELs

Collection

9 items • Updated 1 day ago • 20

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3983

📱 POCKET — a 35-billion-parameter model that runs on your iPhone, and on your PC with no GPU

We're releasing POCKET, VIDRAFT's flagship Darwin-36B-Opus compressed for on-device use. No fork, no CUDA, no cloud — it runs on stock llama.cpp. It's a sparse Mixture-of-Experts model (256 experts, only 8 active per token), so the file can be large while the work per token stays small. That's what lets a 35B model run on a phone, and generate fast on a CPU with no graphics card.

Measured (POCKET-35B IQ1_M vs Bonsai-27B Q1_0):
• CPU generate (Xeon, 16 threads): 27.0 vs 10.1 tok/s → 2.69× faster
• GPU generate (H100): 197 vs 89 tok/s → 2.22× faster
• GPU prompt processing (H100): 753 vs 1816 → 0.41× (Bonsai wins this one — MoE prefill wakes every expert, so sparsity stops helping there. We say so.)
• Quality (HellaSwag, 400 q): 61.0% vs 60.0% → a tie (confidence intervals overlap)

On a real consumer laptop — MacBook M3 Pro (18 GB) — POCKET wins every axis, prompt processing included:
• Metal generate: 25.4 vs 12.8 → 1.99×
• CPU generate: 13.8 vs 4.4 → 3.13×
• Metal prompt: 240.7 vs 73.4 → 3.28×

One more quiet fact: the same-size, quality-oriented rival Ternary-Bonsai-27B (7.2 GB) fails to load in upstream llama.cpp at all — it needs the PrismML fork. POCKET runs on the tools you already have: LM Studio, Ollama, PocketPal, MLX.

📖 Full story (tech, measurements, recipes): https://huggingface.co/blog/FINAL-Bench/pocket

Models:
📦 POCKET-35B-GGUF (PC / server, no GPU): FINAL-Bench/POCKET-35B-GGUF
🇰🇷 POCKET-KR-GGUF (Android): FINAL-Bench/POCKET-KR-GGUF
🍎 POCKET-KR-MLX (iPhone / Mac): FINAL-Bench/POCKET-KR-MLX
🌍 POCKET-EN-GGUF (English phone / PC): FINAL-Bench/POCKET-EN-GGUF
🖥️ Live demo (answering on a CPU, no GPU): FINAL-Bench/POCKET-35B-CPU
📚 Collection: FINAL-Bench/pocket-models-6a618ee5d23eafb7e185a5c6