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SeaWolf-AI 
posted an update about 16 hours ago
Post
987
No GPU. No cloud. Just your phone.

Most “small” models still beg for a datacenter. Ours fit in your pocket.

VIDRAFT POCKET is a family of GGUF models that run on an iPhone and on a GPU-free PC, straight out of the box with llama.cpp, Ollama, LM Studio, and PocketPal. Seven weeks after launch they crossed 1 million downloads. Today the family is past 2.81 million and still climbing, every day.

Three models, one pocket:

🟢 POCKET-35B — a 35B mixture-of-experts that runs on an iPhone and on a PC with no GPU. Balanced power.
→ FINAL-Bench/POCKET-35B-GGUF

🔵 POCKET-26B — a Gemma-4 based 26B MoE, tuned for on-device Korean. Lightweight and fast.
→ FINAL-Bench/POCKET-26B-GGUF

🟣 POCKET-Darwin-180B — a 180B flagship running on a laptop in 4-bit GGUF. The datacenter, on your desk.
→ FINAL-Bench/POCKET-Darwin-180B-GGUF

The whole family lives here:
FINAL-Bench

Try our models live in the browser: FINAL-Bench/POCKET-Image-Studio

Private by default. Offline by design. Yours to run.

#OnDeviceAI #LocalLLM #llamacpp #GGUF #VIDRAFT #POCKET

The quantization choice makes a huge difference to the phone claim: the 35B repository lists IQ1_M at 8.24 GB and Q4_K_M at 21.2 GB. Those are weight-file sizes, before the KV cache and runtime overhead. MoE reduces how much computation each token needs; the active parameter count isn't the RAM requirement. For a reproducible on-phone example, the exact device, GGUF variant and context length would be more useful than the 35B label alone.