Total runs:
271.4K
Run Growth:
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Growth Rate:
-22.74%
Sponsored by
APIMart
FINAL-Bench / POCKET-35B-GGUF
Model's Last Updated: September 25 2026
text-generation
Introduction of POCKET-35B-GGUF
Model Details of POCKET-35B-GGUF
๐ Collections
โถ POCKET Models โ this family (on-device, no GPU) Darwin Family ยท Aether Foundation ยท VKAE Accelerated ยท Metacognition Adapters
POCKET-35B-GGUF
A
35B
model that runs on your
PC with no GPU
โ and on your phone. Just stock
llama.cpp
.
No fork
, no CUDA, no cloud.
๐ Try it live, no install โ
โ a 35B model answering on a CPU-only box.
Pick your build โ
-0f6e56){kind=icon}
The POCKET lineup โ 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
โญ
|
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+ | ๐ฐ๐ท Korean phone | 7.95 |
| POCKET-KR-MLX | 2-bit | 5.1 GB | ๐ iPhone / iPad / Mac | ๐ฐ๐ท Korean, Apple-native | 7.95 |
| POCKET-EN-GGUF |
iPhone-mix
|
5.3 GB | ๐ iPhone (PocketPal) | ๐ English phone | โ |
| POCKET-EN-GGUF |
PC-mix
|
6.8 GB | PC / Android | ๐ English, best quality | โ |
*Wikipedia-Korean perplexity, lower is better.
Q4_K_M
= 5.79 baseline. English builds are tuned on English; see each repo.
๐ 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 mixed-precision trick, which only GGUF supports โ so the English iPhone build ships as a GGUF you run with PocketPal . Honest, not lazy.
Benchmarks โ 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 โ 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 | ๐ข 2.69ร |
| GPU generate (H100) | 197 tok/s | 89 | ๐ข 2.22ร |
| GPU prompt (H100) | 753 | 1816 | ๐ด 0.41ร |
| Quality (HellaSwag, 400q) | 61.0% | 60.0% | โช tie (CI overlaps) |
[measured on a MacBook M3 Pro, 18 GB]
โ 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 | ๐ข 1.99ร |
| CPU generate (8 threads) | 13.8 tok/s | 4.4 | ๐ข 3.13ร |
| Metal prompt (pp128) | 240.7 tok/s | 73.4 | ๐ข 3.28ร |
| CPU prompt (pp128) | 45.5 tok/s | 9.6 | ๐ข 4.75ร |
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
โ on an 18 GB Mac, run Q2_K on CPU (
-ngl 0
); its 13 GB exceeds the recommended Metal budget.
[pending โ 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 โ 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
โญ
|
13 GB | 2.6 | mini-PC 16โ24 GB | 6.49 ( best value ) |
POCKET-35B-IQ1_M.gguf
|
8.2 GB | 1.9 | 16 GB RAM | 9.69 (smallest) |
Quickstart โ no fork needed
# any recent llama.cpp โ brew / winget / apt, or LM Studio / Ollama
llama-cli -m POCKET-35B-Q2_K.gguf -p "์๋
ํ์ธ์" -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 โ it can slow down sharply.
Lineage โ where POCKET comes from
POCKET is quantized from Darwin-36B-Opus , VIDRAFT's flagship โ 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 โ VIDRAFT, multi-generation Darwin evolution |
| Base architecture | Qwen3.5-family MoE (256 experts, top-8), unchanged |
Quantization (
Q4_K_M
โฆ
IQ1_M
)
|
stock llama.cpp โ no custom format |
| Runtime | upstream llama.cpp / Apple MLX โ unmodified |
| Expert pruning + domain imatrix (KR/EN builds) | ours (VIDRAFT) |
The CPU/GPU speed comes from the sparse-MoE architecture plus ordinary quantization โ 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 โ community reports welcome.
-
Extreme quants (
IQ1_M) hurt Korean ~2.8ร 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.
Runs of FINAL-Bench POCKET-35B-GGUF on huggingface.co
533.2K
Total runs
0
24-hour runs
-1.6K
3-day runs
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7-day runs
-258.0K
30-day runs
More Information About POCKET-35B-GGUF huggingface.co Model
Url of POCKET-35B-GGUF
POCKET-35B-GGUF huggingface.co Url
Provider of POCKET-35B-GGUF huggingface.co
Other API from FINAL-Bench
Updated:September 25 2026
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