Zhanli-Li/DataWorld-35B-A3B
just landed · new, unvettedmodel
New, unvetted: created or first seen in the last few days, not from a trusted org and not past the signal threshold yet.
Reinforcement learning model from Zhanli-Li (HF pipeline tag; the card has no one-line description). Mixture-of-experts, 35B total, 3B active (256 experts, 8 per token). Context: 256K tokens (config). License: apache-2.0.
- Org
- Zhanli-Li
- Released
- 2026-10-15
- Status
- just landed · new, unvetted
- License
- apache-2.0 (permissive)
- Parameters
- 35.1B total, 3.0B active
- Architecture
- MoE · 256 experts, 8/token · Qwen3_5MoeForConditionalGeneration
- Context
- 262,144 tokens
- Task
- reinforcement-learning · other
- Signal
- 15 HF likes · 80 downloads
Runs on
real quant files; need = weights + FP16 KV cache for 8K tokens + 1GB. A sizing calculation, not tested.
- Q4 ~22.9 GB: 24GB GPU (3090 / 4090), 32GB GPU (5090), 24GB GPU (7900 XTX), Mac 36GB, Mac 48GB, CPU only, 32GB RAM (slow)
- Q8 ~39.2 GB: 48GB GPU (RTX 6000 Ada / A6000), Mac 64GB (M1 Max), CPU only, 64GB RAM (slow)
- FP16 ~71.9 GB: 96GB GPU (RTX Pro 6000 Blackwell), 1× DGX Spark (128GB), 2× DGX Spark (256GB), Strix Halo 128GB unified (~96GB usable), Mac 96GB, Mac 128GB, Mac 192GB, Mac 256GB, Mac 512GB, CPU only, 128GB RAM (slow), CPU only, 256GB RAM (slow)
Links
First published here 2026-10-15 · updated 2026-10-28 · markdown · JSON