PollardWeights/Qwen3-30B-A3B-Pollard

Qwen3-30B-A3B — Pollard

### Pollard shrank this model: 61.06 GB (f16) → 7.54 GB — 88% smaller, 8.1× down. The smallest rung here; larger, higher-fidelity rungs are listed below. | format | this model's size | |---|---:| | f16 | 61.06 GB | | Q80 | ~32.36 GB | | Q6K | ~25.03 GB | | Q4KM | ~17.71 GB | | PollardMix (this repo's IQ1_KT) | 7.54 GB |

Pollard builds of Qwen/Qwen3-30B-A3B made with Pollard Weights — a ladder of measured-allocation quants (bits placed by per-layer sensitivity, not a uniform crush).

Standard GGUF — runs in stock llama.cpp / ik_llama.cpp, Ollama, LM Studio. Trellis (IQ*_KT) files need ik_llama.cpp; the K-quants run anywhere.

Model details

Which file should I choose?

Every rung is the same weights, sized to a different RAM budget by the measured allocation. Pick the largest one that fits your machine with room for context:

• —~27 GB RAM / VRAM → `Q6_K` (25.12 GB). near-lossless
• —~18 GB RAM / VRAM → `IQ4_XS` (16.48 GB). recommended default
• —~15 GB RAM / VRAM → `IQ3_S` (13.46 GB). smaller
• —~10 GB RAM / VRAM → `IQ1_KT` (7.54 GB). flagship — 1-bit mixed-precision MoE trellis

Available files (WikiText-2 raw, ctx 2048, 145 chunks; KLD vs Q6_K base)

The numbers (WikiText-2 raw, ctx 2048, 145 chunks; KLD vs Q6_K base)

PollardMix beats the uniform 1-bit trellis quant on every metric — PPL −4.9%, Mean KLD −14%, Median KLD −20%, top-1 +2.3 pts — at +6.9% size, under the 2-bit ceiling. Same clean sweep as the 7B/14B dense cards, now reproduced on a MoE — the automap policy generalizes (crush cold experts, protect the router / ffn_down_exps / shared experts / attention).

Allocation (the surgery)

Measured notes

PollardMix beats the uniform 1-bit trellis quant on every metric — PPL −4.9%, Mean KLD −14%, Median KLD −20%, top-1 +2.3 pts — at +6.9% size, under the 2-bit ceiling. Same clean sweep as the 7B/14B dense cards, now reproduced on a MoE — the automap policy generalizes (crush cold experts, protect the router / ffn_down_exps / shared experts / attention).

Download a specific file

pip install -U "huggingface_hub[cli]"
hf download PollardWeights/Qwen3-30B-A3B-Pollard \
  --include "Qwen3-30B-A3B-Pollard-IQ4_XS.gguf" --local-dir ./

How to run

These are standard GGUF and run with llama.cpp:

llama-server -hf PollardWeights/Qwen3-30B-A3B-Pollard:IQ4_XS

or from a local file:

llama-cli    -m Qwen3-30B-A3B-Pollard-IQ4_XS.gguf -ngl 99 -p "Explain why the sky is blue."
llama-server -m Qwen3-30B-A3B-Pollard-IQ4_XS.gguf -ngl 99      # OpenAI-compatible API + web UI at :8080

They also work in anything built on llama.cpp — LM Studio, koboldcpp, Jan, ramalama, Ollama (ollama run hf.co/PollardWeights/Qwen3-30B-A3B-Pollard).

ARM / AVX

llama.cpp repacks weights into an interleaved layout at load time for faster inference on ARM and AVX machines — no special file needed, online repacking covers these quants. The old Q4_0_4_4/4_8/8_8 variants are not required.

Errata

• —Trellis (IQ*_KT) quants need ik_llama.cpp to build/run; K-quants run in any recent llama.cpp.
• —Measured allocation places bits by per-layer sensitivity under a size budget.
• —Single machine; replication invited.

Credits & license

• —Base model: `Qwen/Qwen3-30B-A3B`
• —Quantization tooling: llama.cpp (ggml-org)
• —Method + tooling: Pollard Weights — measure first, no claim before a number.
• —License: apache-2.0, inherited from the base model.

Built with [Pollard Weights](https://github.com/WestWaters/pollard-weights) — frontier models, small hardware, no compromise.