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.
*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
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
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.
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