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Ornith-1.5-35B-A3B APEX GGUF
APEX quantizations of ornith-ai/Ornith-1.5-35B-A3B.
Brought to you by the LocalAI team | APEX Project
These are the standard quants. For versions that bundle the MTP draft head for speculative decoding, see Ornith-1.5-35B-A3B-APEX-MTP-GGUF.
Files
| File | Size | For |
|---|---|---|
| Ornith-1.5-35B-A3B-APEX-Quality.gguf | 22.82 GB | highest quality |
| Ornith-1.5-35B-A3B-APEX-Balanced.gguf | 25.27 GB | general purpose |
| Ornith-1.5-35B-A3B-APEX-Compact.gguf | 16.54 GB | consumer GPUs |
| Ornith-1.5-35B-A3B-APEX-I-Mini.gguf | 13.47 GB | smallest, imatrix only |
| mmproj.gguf | 0.90 GB | vision projector, pair with any of the above |
I- files use an importance matrix built from diverse calibration data (chat, code, reasoning, tool-calling, agentic traces, Wikipedia). Quality, Balanced and Compact also ship without it.
The model
Ornith-1.5-35B-A3B is a 36 B parameter Mixture-of-Experts model with 256 routed experts and 8 active per token, plus a shared expert. It has 40 layers with hybrid attention, interleaving three linear-attention layers per full-attention layer, and a vision tower.
How APEX quantizes it
Routed experts are 89.6% of the weights here but only 8 of 256 fire for any given token, so they tolerate lower precision than the parts every token passes through. APEX classifies each tensor by role and applies a layer-wise precision gradient: the first and last layers keep higher precision, middle layers compress harder, and the always-active shared expert is kept high.
Attention is only 3.6% of the weights on this model (2.8% linear, 0.8% full), so it is not where the size is and is not treated as a lever.
Usage
# text
llama-cli -m Ornith-1.5-35B-A3B-APEX-Balanced.gguf -p "Your prompt" -ngl 99
# vision
llama-mtmd-cli -m Ornith-1.5-35B-A3B-APEX-Balanced.gguf --mmproj mmproj.gguf -ngl 99
Needs a recent llama.cpp with qwen3_5_moe support.
Notes
Sizes and quantization recipes are published in the APEX repository. No throughput benchmarks were run on these files.