Two quantizations of Qwen/Qwen3.8-27B, plus the vision projector.

Brought to you by the LocalAI team | APEX Project

Both include the MTP / NextN head as blk.64, so speculative decoding runs against the file itself. The head is kept at Q8_0: a drafter that mispredicts the target wastes the speculation, so it does not get compressed with the rest.

Needs a recent llama.cpp with qwen3_5 support. The architecture landed after the model shipped on 2026-08-13, and older builds segfault on load.

The bit allocation in these files comes from a measurement rather than a hand-picked recipe, and the measurement said the approach only helps below about 15.2 GB.

The same allocation was built at four sizes and compared against a flat per-role allocation, the kind unsloth, bartowski and ggml-org ship. Each of ours came out slightly smaller than its control, so the comparison interpolates the flat curve to each file's exact size:

It depends on how much damage there is to move around. A flat allocation's KL against BF16 runs 0.0021 at Q6_K, 0.0099 at 17.6 GB, 0.0617 at 13.7 GB, and 0.1448 at 11.0 GB. At Q4 and above the model is already close to lossless, so there is little for a smarter allocation to redistribute. By Mini there is roughly six times as much.

A Quality or Balanced tier from us would be the same flat recipe you can already download elsewhere, so it is not here.

(Those sizes are the trunk-only files used for the comparison. The published ones are about 0.45 GB larger because they carry the MTP head.)

Qwen3.8-27B has no experts, so the usual APEX trick does not apply: its FFN is 62.6% of the parameters and every weight runs for every token. It also interleaves three linear-attention layers per full-attention layer (64 layers, full attention at i%4==3), which splits attention into two groups that behave differently.

Dropping one tensor group at a time from Q6_K to Q3_K and dividing the KL increase by the gigabytes saved gives:

A 15.3x spread. Three things stand out. output and token_embd have identical shape, 5120 x 248320, yet output is 15.3x more sensitive, where most recipes place them one step apart. FFN edge layers cost 2.63x more per byte than middle layers. And the three linear-attention tensors range from 0.00880 to 0.01474, so compressing them uniformly loses to a flat baseline.

Throughput was not measured. Different quant types select different kernels, so speed is a separate question. The +35-52% tok/s figure quoted for draft-mtp on this model comes from a third-party report.

These numbers are specific to this architecture. The linear and full attention split is unusual, so they should not be assumed to carry over to other dense models.

token_embd has a floor. It is an embedding lookup rather than a matmul, so llama-imatrix collects nothing for it, and very-low-bit types that need imatrix data fail at quantize time. IQ2_S works, IQ2_XXS does not.

Imatrix from diverse calibration data (chat, code, reasoning, tool-calling, agentic traces, Wikipedia), 200 chunks. KL measured against BF16 reference logits on wikitext-2-raw, n_ctx 512, 200 chunks. Every arm shares one imatrix and one set of reference logits, so the numbers above are directly comparable. 19 arms in total.

Perplexity is not quoted per file because it does not separate them: all four land within about 0.07 of each other, well inside the error bars.