---

license: apache-2.0

library_name: llama.cpp

pipeline_tag: text-generation

base_model:

• FINAL-Bench/Darwin-36B-Opus

tags:

• gguf
• llama.cpp
• conversational
• on-device
• mobile
• iphone
• android
• cpu
• local-llm
• edge
• mixture-of-experts
• moe
• quantized
• pocket
• vidraft
• qwen3_5_moe
• darwin

---

> ### 📚 Collections

> ▶ POCKET Models — this family (on-device, no GPU)

> Darwin Family · Aether Foundation · VKAE Accelerated · Metacognition Adapters

!POCKET

POCKET-35B-GGUF

A 35B model that runs on your PC with no GPU — and on your phone. Just stock llama.cpp. No fork, no CUDA, no cloud.

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

  ![No GPU]() ![Base]()

Pick your build →     

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 | — |

*Wikipedia-Korean perplexity, lower is better. Q4_K_M = 5.79 baseline. English builds are tuned on English; see each repo.

> 🍎 Why MLX for Korean but GGUF for English on iPhone? Apple-native MLX only does uniform quantization. Korean survives it (96 experts hold up); 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.

!Speed vs Bonsai

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 | bpw | Runs on | Korean PPL |

|---|---|---|---|---|

| POCKET-35B-Q4_K_M.gguf | 21 GB | 4.5 | PC 32 GB RAM | 5.79 (top) |

| POCKET-35B-Q3_K_M.gguf | 16 GB | 3.4 | PC 24 GB | 6.06 |

| POCKET-35B-Q2_K.gguf ⭐ | 13 GB | 2.6 | mini-PC 16–24 GB | 6.49 (best value) |

| POCKET-35B-IQ1_M.gguf | 8.2 GB | 1.9 | 16 GB RAM | 9.69 (smallest) |

Quickstart — no fork needed

# any recent llama.cpp — brew / winget / apt, or LM Studio / Ollama
llama-cli -m POCKET-35B-Q2_K.gguf -p "안녕하세요" -ngl 0 -t 8
# reproduce our CPU numbers:
llama-bench -m POCKET-35B-IQ1_M.gguf -p 128 -n 64 -ngl 0 -t 16

Use physical-core count for -t (max ~32). Do not pass all threads — it can slow down sharply.

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; 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.

---

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?

<!-- POCKET-FAMILY -->

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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

Demos & tools (Spaces)

• 🖼️ POCKET-Image — character-perfect Korean text in images
• 🖥️ POCKET-35B-CPU — 35B answering on a CPU
• 🖥️ POCKET-26B-CPU — 26B on a CPU

📚 Full POCKET collection

<!-- /POCKET-FAMILY -->