⚡ Each donation = another big MoE quantized
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.
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Qwopus3.6-35B-A3B-Coder — APEX GGUF
APEX (Adaptive Precision for EXpert Models) quantizations of Jackrong/Qwopus3.6-35B-A3B-Coder — a Qwen3.6-35B-A3B MoE tuned for coding.
Brought to you by the LocalAI team | APEX Project | Technical Report
This model ships an MTP head — for self-speculative decoding out of the box, see the MTP-bundled repo: mudler/Qwopus3.6-35B-A3B-Coder-APEX-MTP-GGUF.
Available Files
| File | Profile | Best For |
|---|---|---|
| Qwopus3.6-35B-A3B-Coder-APEX-I-Balanced.gguf | I-Balanced | Best overall — imatrix-enhanced |
| Qwopus3.6-35B-A3B-Coder-APEX-I-Quality.gguf | I-Quality | Highest quality with imatrix |
| Qwopus3.6-35B-A3B-Coder-APEX-Quality.gguf | Quality | Highest quality (no imatrix) |
| Qwopus3.6-35B-A3B-Coder-APEX-Balanced.gguf | Balanced | General purpose |
| Qwopus3.6-35B-A3B-Coder-APEX-I-Compact.gguf | I-Compact | Consumer GPUs, imatrix-enhanced |
| Qwopus3.6-35B-A3B-Coder-APEX-Compact.gguf | Compact | Consumer GPUs |
| Qwopus3.6-35B-A3B-Coder-APEX-I-Mini.gguf | I-Mini | Smallest viable, fastest inference |
| mmproj.gguf | Vision projector | Required for image understanding |
What is APEX?
APEX is a quantization strategy for Mixture-of-Experts (MoE) models. It classifies tensors by role (routed expert, shared expert, attention) and applies a layer-wise precision gradient — edge layers (first/last 5) get higher precision, middle layers get more aggressive compression. I-variants use diverse imatrix calibration (chat, code, reasoning, tool-calling, agentic traces, Wikipedia).
See the APEX project for full details.
Architecture
- Model: Qwopus3.6-35B-A3B-Coder (Qwen3_5MoeForConditionalGeneration, Qwen3.6-35B-A3B base)
- Layers: 40 · Experts: 256 routed + 1 shared (8 active) · Total/Active: ~35B / ~3B
- Attention: Hybrid (full attention every 4th layer, linear otherwise)
- Vision: Built-in vision encoder (mmproj included)
- Calibration: v1.3 diverse dataset
Run with LocalAI
local-ai run mudler/Qwopus3.6-35B-A3B-Coder-APEX-GGUF@Qwopus3.6-35B-A3B-Coder-APEX-I-Balanced.gguf
Credits
APEX is brought to you by the LocalAI team. Built on llama.cpp. Base model by Jackrong.