TelperionAI

Qwen3.8-27B-INT4-AWQ-GPTQ-gdn4

README

License: apache-2.0

Recipe

Table with columns: component, precision
component	precision
`mlp.{gate,up,down}_proj`, layers 0–55	INT4, group-32, asymmetric (~4.6 effective bits)
`mlp.{gate,up,down}_proj`, layers 56–63	INT8 W8A16
`self_attn.{q,k,v,o}_proj`	INT8 W8A16
`linear_attn.{in_proj_qkv,in_proj_z,out_proj}` (GDN)	INT4, group-32, asymmetric
`lm_head`, `embed_tokens`, norms, GDN state params, vision tower	BF16

Two passes:

AWQ — per-input-channel scaling on post_attention_layernorm → {gate_proj, up_proj} and up_proj → down_proj. Gate and up share one input, so the reciprocal scale folds into the norm weights: zero size and zero throughput cost.
GPTQ — Hessian-based error compensation, actorder="static", dampening_frac=0.01.

Calibration: 924 sequences × 1024 tokens of a balanced Nemotron-v2 blend (25% code, 25% math, 20% STEM, 20% chat, 10% multilingual).

lm_head and embed_tokens stay BF16, matching Qwen's own official FP8 release.

Benchmarks

Against the BF16 base on 142,727 tokens of self-distilled thinking-mode output plus 200 free greedy generations. vLLM 0.27.1, TP=2, 2×B300.

Table with columns: checkpoint, size ↓, top-1 ↑, near-tie ↓, moderate ↓, confident ↓, certain ↓, divmed ↑, tok/s ↑
checkpoint	size ↓	top-1 ↑	near-tie ↓	moderate ↓	confident ↓	certain ↓	divmed ↑	tok/s ↑
`Qwen/Qwen3.8-27B-FP8` (8-bit ref)	30.9 GB	96.15%	22.70%	3.48%	1.45%	0.08%	47	8711

Bold marks the best value in each column among the ~21–23 GB checkpoints; the FP8 row and our own 25.1 GB sibling sit in different size classes and are shown for reference only.

Columns. top-1 is raw argmax agreement with BF16. The four bucket columns are disagreement rates, split by how confident the base model was at that position (top1−top2 logprob margin): near-tie <0.5, moderate 0.5–2, confident 2–5, certain >5. Only confident and certain are real damage — a flip where the base model was itself nearly tied is numerical noise. divmed is the median token index at which free greedy generation first diverges from BF16 (higher is better).

Perplexity is deliberately excluded. On this model it is anti-correlated with quality — the FP4 checkpoint with the best perplexity we measured also had the worst certain-bucket damage. Do not rank quantizations of this model by perplexity.

What the GDN precision costs

Moving the GDN projections from 8-bit to 4-bit is what takes this build from 25.1 GB to 22.6 GB. It costs confident 0.93% → 1.15% and certain 0.09% → 0.13% — real but modest. Note the two are not a pure bit-width comparison: at 4-bit the GDN projections also move into GPTQ's error compensation, where at 8-bit they were plain round-to-nearest, so the true cost of 4-bit GDN in isolation is likely a little larger than shown.

At matched size against cyankiwi (21.0 GB), this build is still ahead on confident (1.15% vs 1.35%) and level on certain — so the AWQ+GPTQ recipe, not the extra bits, accounts for most of the margin.

Why INT4 rather than NVFP4

At ~4.6 effective bits, INT4 group-32 asymmetric gives 16 uniformly-spaced levels plus a per-group zero point. NVFP4 gives 8 non-uniform FP4 levels at 4.5 bits with no zero point. On this model the zero point matters — MLP weight distributions are skewed — and the same recipe in NVFP4 lands at 1.85% confident versus 0.93% here.

NVFP4's advantage is hardware, not numerics: on Blackwell it decodes at 10590 tok/s against 4617 here, because native NVFP4 tensor-core paths beat Marlin INT4. Pick NVFP4 if you are throughput-bound on Blackwell; pick this if you want fidelity, or you are on Ampere/Ada where FP4 and FP8 do not exist.

Usage

python
from vllm import LLM
llm = LLM("TelperionAI/Qwen3.8-27B-INT4-AWQ-GPTQ-gdn4", tensor_parallel_size=2)

W4A16 needs only compute capability ≥ 7.5 (Turing), so this runs on 3090/4090/A100/H100 as well as Blackwell. Throughput above was measured on B300 and is not representative of Ampere, where Marlin INT4 is comparatively better tuned.

Speculative decoding (MTP)

The model's MTP (multi-token prediction) head is included, in BF16, and works with vLLM's mtp speculative decoding:

python
from vllm import LLM
llm = LLM("TelperionAI/Qwen3.8-27B-INT4-AWQ-GPTQ-gdn4", tensor_parallel_size=2,
          speculative_config={"method": "mtp", "num_speculative_tokens": 2})

Qwen3_5ForConditionalGeneration does not carry mtp.* in its state dict, so llm-compressor never sees it and it is silently dropped, even though config.json still declares mtp_num_hidden_layers: 1. It is grafted back in here from the base checkpoint and excluded from quantization (re:.*mtp.* in quantization_config.ignore; without that exclusion the quantization target regexes also match mtp.layers.0.mlp.* and vLLM fails to load). Draft quality drives acceptance rate, so it is kept at full precision rather than quantized.

Acceptance rate has not been measured; the head is verified to load and generate.

Limitations

22.6 GB is still tight on a single 24 GB card once KV cache is accounted for. Quantizing lm_head would free a further ~1.3 GB but was not measured here.
Throughput on Blackwell is ~2.3× below NVFP4. This checkpoint trades speed for fidelity.
Single evaluation corpus. All numbers come from one self-distilled corpus. The margins over FP8 and cyankiwi are statistically solid but have not been replicated on a second distribution, nor on downstream task benchmarks.
Vision tower untouched (BF16); evaluated as a text model.

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Model Details

Model Provider

TelperionAI

Model Tree

Base

Qwen/Qwen3.8-27B

Quantized

this model

Input Modalities

TextImageVideo

Output Modalities

Text

Supported Functionality