🆕 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-Zimage-CPU — photoreal images in 46 s on a CPU only. No GPU, no CUDA, no Python.
📚 Collections
▶ POCKET Models — this family (on-device, no GPU) Darwin Family · Aether Foundation · VKAE Accelerated
POCKET-EN-GGUF · English
English-focused pocket build using our proprietary quantization — protects the quality-critical layers so quality holds at 5 GB. Runs on iPhone (PocketPal) and any CPU PC. No fork.
🚀 Try it live, no install →
— both answering on a CPU-only box (no GPU). POCKET-26B is Gemma4-based.
Pick your build → 
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The POCKET lineup — pick by your device
| Repo | File | Size | Runs on | Best for | Korean PPL* |
|---|---|---|---|---|---|
| POCKET-35B-GGUF | Q4_K_M | 21 GB | PC / server (32 GB RAM) | top quality | 5.79 |
| POCKET-35B-GGUF | Q2_K ⭐ | 13 GB | mini-PC, no GPU | daily driver | 6.49 |
| POCKET-35B-GGUF | IQ1_M | 8.2 GB | 16 GB RAM box | smallest full model | 9.69 |
| POCKET-KR-GGUF | IQ2_M | 5.1 GB | Android 8 GB+ | 🇰🇷 Korean phone | 7.95 |
| POCKET-KR-MLX | 2-bit | 5.1 GB | 🍎 iPhone / iPad / Mac | 🇰🇷 Korean, Apple-native | 7.95 |
| POCKET-EN-GGUF | iPhone-mix | 5.3 GB | 🍎 iPhone (PocketPal) | 🌍 English phone | — |
| POCKET-EN-GGUF | PC-mix | 6.8 GB | PC / Android | 🌍 English, best quality | — |
| POCKET-Qwen3.8-Flash-Next-GGUF | Q4_K_M | 111 GiB | 💻 laptop, 8 GB VRAM + 32 GB RAM | 180B on a laptop | 6.03† |
*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_K11 GB ·Q4_K_M17 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:
| POCKET-35B IQ1_M | Bonsai-27B Q1_0 | ||
|---|---|---|---|
| CPU generate (Xeon, 16t) | 27.0 tok/s | 10.1 | 🟢 2.69× |
| GPU generate (H100) | 197 tok/s | 89 | 🟢 2.22× |
| GPU prompt (H100) | 753 | 1816 | 🔴 0.41× |
| Quality (HellaSwag, 400q) | 61.0% | 60.0% | ⚪ tie (CI overlaps) |
[measured on a MacBook M3 Pro, 18 GB] — and on a laptop, POCKET wins every axis, including prompt processing:
| POCKET-35B IQ1_M | Bonsai-27B Q1_0 | ||
|---|---|---|---|
| Metal generate (tg64) | 25.4 tok/s | 12.8 | 🟢 1.99× |
| CPU generate (8 threads) | 13.8 tok/s | 4.4 | 🟢 3.13× |
| Metal prompt (pp128) | 240.7 tok/s | 73.4 | 🟢 3.28× |
| CPU prompt (pp128) | 45.5 tok/s | 9.6 | 🟢 4.75× |
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:
| Model | GPQA-Diamond (greedy) |
|---|---|
| Qwen3.6-35B-A3B | 73.2% |
| POCKET-35B Q4_K_M | 68.7% |
| POCKET-35B Q2_K | 60.1% |
[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
| File | Size | Runs on | vs baseline |
|---|---|---|---|
POCKET-EN-iPhone-mix.gguf | 5.3 GB | 🍎 iPhone (PocketPal) | +57% PPL |
POCKET-EN-PC-mix.gguf | 6.8 GB | PC / Android | +36% PPL (near-full) |
Our proprietary mixed-precision quantization protects the most quality-critical weights — which is why English quality holds at this size.
Quickstart
llama-cli -m POCKET-EN-PC-mix.gguf -p "Explain mixture-of-experts in one line." -ngl 0 -t 8
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.
| Component | Origin |
|---|---|
| Starting checkpoint | Darwin-36B-Opus — VIDRAFT, multi-generation Darwin evolution |
| Base architecture | Qwen3.5-family MoE (256 experts, top-8), unchanged |
Quantization (Q4_K_M…IQ1_M) | stock llama.cpp — no custom format |
| Runtime | upstream llama.cpp / Apple MLX — unmodified |
| Proprietary language-specific tuning (KR/EN builds) | ours (VIDRAFT) |
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; useQ2_Kor 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.
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?
🧩 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