Qwen3.6-35B-A3B-APEX-MTP-GGUF

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Qwen3.6-35B-A3B — APEX-MTP GGUF

APEX (Adaptive Precision for EXpert Models) quantizations of Qwen/Qwen3.6-35B-A3B, with the MTP (multi-token prediction) head bundled for in-the-box self-speculative decoding.

Brought to you by the LocalAI team | APEX Project | Technical Report

What's different from the plain APEX repo?

These GGUFs bundle the model's MTP (multi-token prediction) head alongside the trunk in a single file, courtesy of llama.cpp PR #22673. With a recent llama.cpp (>= commit 255582687) you can enable self-speculative decoding using just this one file — no separate draft model needed:

llama-server -m Qwen3.6-35B-A3B-APEX-MTP-I-Balanced.gguf --draft-mtp

The non-MTP version is still available at mudler/Qwen3.6-35B-A3B-APEX-GGUF — slightly smaller, but no self-spec.

File sizes

Each quant is ~2.5% larger than its non-MTP counterpart (one extra transformer-block worth of weights, no embedding duplication since MTP shares the trunk's embed_tokens).

MTP draft head precision

The bundled MTP head (blk.40.* including the nextn.* projection + norms) is quantized to Q8_0 (near-lossless) on every tier except I-Nano. I-Nano keeps the trunk-tier precision on the MTP block (Q3_K routed experts, Q4_K attention) but pins blk.40.nextn.eh_proj to Q4_K — see the explainer below.

This keeps draft accuracy high (important for spec-decode acceptance rate) at a modest ~1 GB cost per file vs. trunk-tier precision.

Why the MTP head doesn't use imatrix

llama-imatrix runs normal forward passes that only activate the trunk (blk.0..blk.39). The MTP head only fires during --draft-mtp spec decoding, so its tensors get no imatrix activation data. We work around this by quantizing the MTP head with static K-quant / Q8_0 which doesn't require imatrix.

(A patch to llama-imatrix that records MTP activations during collection is in progress at mudler/llama.cpp#mtp-imatrix — once upstream this will let us push the drafter to lower bit-widths cleanly.)

What is APEX?

APEX is a MoE-aware mixed-precision quantization strategy. Per-tensor-role gradient: routed experts compress hardest, shared experts kept high (always active), attention/Mamba uniform; 5+5 symmetric edge gradient across the 40 trunk layers + MTP layer 40 at edge precision. I-variants use diverse imatrix calibration (chat, code, reasoning, tool-calling, agentic traces, Wikipedia).

See the APEX project for full details.

Architecture

• Base: Qwen 3.6 35B-A3B family (Qwen3_5MoeForCausalLM)
• Layers: 40 trunk + 1 MTP (bundled)
• Experts: 256 routed + 1 shared (8 active per token)
• Hidden size: 2048
• Calibration: v1.3 diverse dataset

Credits

• APEX quantization: LocalAI team
• MTP support: llama.cpp PR #22673 by Aman Gupta + ggerganov
• Built on llama.cpp