I host 30+ free APEX MoE quantizations as independent research. My only local hardware is an NVIDIA DGX Spark (122 GB unified memory), enough for ~30-50B-class MoEs, but bigger ones (200B+) require rented compute on H100/H200/Blackwell, typically $20-100 per quant.
If APEX quants are useful to you, your support directly funds those bigger runs.
🎉 Patreon (Monthly) | ☕ Buy Me a Coffee | ⭐ GitHub Sponsors
APEX quantizations of ornith-ai/Ornith-1.5-35B-A3B.
Brought to you by the LocalAI team | APEX Project
These files bundle the model's MTP / NextN draft head as blk.40, so speculative decoding runs against the file itself with --spec-type draft-mtp. For the same quants without the head, see Ornith-1.5-35B-A3B-APEX-GGUF.
| File | Size | For |
|---|---|---|
| Ornith-1.5-35B-A3B-APEX-MTP-Quality.gguf | 23.72 GB | highest quality |
| Ornith-1.5-35B-A3B-APEX-MTP-Balanced.gguf | 26.17 GB | general purpose |
| Ornith-1.5-35B-A3B-APEX-MTP-Compact.gguf | 17.44 GB | consumer GPUs |
| Ornith-1.5-35B-A3B-APEX-MTP-I-Mini.gguf | 14.37 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.
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.
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.
The MTP head is a full MoE block in its own right, about 2.4% of the weights. On Quality, Balanced and Compact it is pinned to Q8_0, since a drafter that mispredicts the target wastes the speculation it was added for. I-Mini keeps it at tier precision to stay small.
# text
llama-cli -m Ornith-1.5-35B-A3B-APEX-MTP-Balanced.gguf -p "Your prompt" -ngl 99
# vision
llama-mtmd-cli -m Ornith-1.5-35B-A3B-APEX-MTP-Balanced.gguf --mmproj mmproj.gguf -ngl 99
# speculative decoding against the bundled MTP head
llama-cli -m Ornith-1.5-35B-A3B-APEX-MTP-Balanced.gguf --spec-type draft-mtp -ngl 99
Needs a recent llama.cpp with qwen3_5_moe support.
Sizes and quantization recipes are published in the APEX repository. No throughput benchmarks were run on these files.
ornith-ai/Ornith-1.5-35B-A3B