Qwen3.8-27B — Pollard
Pollard shrank this model: 55.56 GB (f16) → 12.08 GB — 78% smaller, 4.6× down.
The smallest rung here; larger, higher-fidelity rungs are listed below.
format this model's size f16 55.56 GB Q8_0 ~29.45 GB Q6_K ~22.78 GB Q4_K_M ~16.11 GB PollardMix (this repo's IQ3_S) 12.08 GB
Pollard builds of Qwen/Qwen3.8-27B 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
| Parameter count | ~27.8B |
| Architecture | qwen3_5 |
| Input support | text |
| imatrix | no |
| Perplexity measured | yes — table below |
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:
- ~24 GB RAM / VRAM →
Q6_K(22.43 GB). - ~18 GB RAM / VRAM →
IQ4_XS(15.72 GB). - ~14 GB RAM / VRAM →
IQ3_S(12.08 GB).
Available files
| file | PPL | size | tok/s | Mean KLD | notes |
|---|---|---|---|---|---|
Qwen3.8-27B-Pollard-IQ3_S.gguf | (see repo) | 12.08 GB | (16 GB tier) | — | IQ3_S |
Qwen3.8-27B-Pollard-IQ4_XS.gguf | — | 15.72 GB | 24 GB tier | — | IQ4_XS |
Qwen3.8-27B-Pollard-Q6_K.gguf | — | 22.43 GB | 32 GB tier | — | Q6_K |
tok/s is hardware-specific; the machine it was measured on is stated in the errata.
Multimodal
Vision needs the projector shipped alongside: mmproj-Qwen3.8-27B-bf16.gguf — download it too and pass it with --mmproj. It is not quantized; it is small and the text ladder is where the size lives.
llama-server -m Qwen3.8-27B-Pollard-IQ3_S.gguf --mmproj mmproj-Qwen3.8-27B-bf16.gguf -ngl 99
Download a specific file
pip install -U "huggingface_hub[cli]"
hf download PollardWeights/Qwen3.8-27B-Pollard \
--include "Qwen3.8-27B-Pollard-IQ3_S.gguf" --local-dir ./
How to run
These are standard GGUF and run with llama.cpp:
llama-server -hf PollardWeights/Qwen3.8-27B-Pollard:IQ3_S
or from a local file:
llama-cli -m Qwen3.8-27B-Pollard-IQ3_S.gguf -ngl 99 -p "Explain why the sky is blue."
llama-server -m Qwen3.8-27B-Pollard-IQ3_S.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/Qwen3.8-27B-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/Qwen3.8-27B - 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 — frontier models, small hardware, no compromise.