This GGUF is built to be served with xyntetik-runner, a single-binary, dependency-free C11 inference engine (CPU / CUDA / Metal, OpenAI-compatible server). Its one differentiator you cannot get from another engine:

When a tool call runs past its token budget, Runner closes it to the smallest schema-legal document, so the arguments still parse.

Most OpenAI-compatible engines return finish_reason: "length" with an empty or malformed tool_calls the caller has to repair or retry. Runner does forced-truncation recovery: once a JSON document starts, it emits a legal ending when the budget expires. On local models — tight context, slow generation — that is the difference between an agent loop that finishes and one that retries from scratch.

Measured — what each engine hands the caller when the token budget cuts a tool call short (same box, same tool schema, same prompt, tool_choice:"required", temperature 0, budgets 1→64):

engine	budget too small (1–16 tokens)	enough budget (64, control)
Runner	executable `tool_calls`, arguments parse	completes
vLLM 0.27.1	no call; protocol framing leaks into `content`	completes
llama.cpp b10488	no call; leak, then `tool_calls` with unparseable args	completes
Ollama 0.32.14	no call; empty content, then HTTP 500	completes
TensorRT-LLM 1.2.1 †	no call; `<tool_call>` leak, then empty content	completes
SGLang 0.5.17 †	no call; `<tool_call>` leak, then empty content	completes

Every engine completes at 64 tokens — the failure is truncation, not misconfiguration. Below that, only Runner returns an executable call. This is the behaviour across every OpenAI-compatible engine measured, not a claim about engines not measured. † TensorRT-LLM and SGLang were measured on a Qwen3-1.7B substitute; truncation recovery is a property of the runtime, not the model.

Constrained decoding also holds the shape of a tool call under quantization: on a full quant ladder, schema conformance and tool selection stayed 100% down to Q4_0 (argument agreement decayed to 50%) — it guarantees the shape of a call at any quantization, not its contents.

Quickstart

bash

# build the single binary (no deps), then serve this GGUF:
runner -m Qwen3-30B-A3B-expq4_0-attnq8_0.gguf --serve
# point any OpenAI-compatible client at http://localhost:8080/v1

Full recipe, raw responses, and the per-release regression gate: the truncation benchmark.

Run it

This file is 18.0 GB, so it wants a machine with roughly 20 GB of usable memory or more.

# Apple Silicon Mac
curl -L https://github.com/Joakimpalm-Zen/xyntetik-runner/releases/latest/download/runner-macos-arm64.tar.gz | tar xz
curl -L -o Qwen3-30B-A3B-expq4_0-attnq8_0.gguf \
  https://huggingface.co/Joakimpalm-Zen/Qwen3-30B-A3B-selective-attnQ8_0-expQ4_0-GGUF/resolve/main/Qwen3-30B-A3B-expq4_0-attnq8_0.gguf
./runner-macos-arm64 -m Qwen3-30B-A3B-expq4_0-attnq8_0.gguf -i

Linux and Windows builds are on the same release page. Any GGUF runtime with qwen3moe support should load it: every tensor is an ordinary GGUF type, only the per-tensor assignment is unusual.

This is not a uniform quantization. It is a precision PLAN: the expert banks (the bulk of a MoE's bytes) are Q4_0 while attention, embeddings and every shared tensor stay Q8_0. Category-wise:

Model	Expert tensors	Attention/shared	Size	Category
Qwen3-30B-A3B Q8_0 (Qwen)	Q8_0	Q8_0	32.48 GB	official, the source
Qwen3-30B-A3B Q4_K_M (Qwen)	Q4_K/Q6_K	Q4_K class	18.56 GB	official uniform quant
this file	Q4_0	Q8_0	17.99 GB	selective-precision derivative

File: Qwen3-30B-A3B-expq4_0-attnq8_0.gguf (17,988,417,024 bytes)
SHA256: df02efa815a74e6207f9e01f2d38272c1e2718bb23aec544709a02a2c7d5b206
Source: Qwen/Qwen3-30B-A3B-GGUF -> Qwen3-30B-A3B-Q8_0.gguf (SHA256 4ad960d180b16f56024f5b704697e5dd5b0837167c2e515ef0569abfc599743c, verified byte for byte against the repo's LFS record before quantizing)

Why this split: the methodology

Produced with the open tooling in Xyntetik Runner:

Plan: --type-plan applies per-tensor precision rules while rewriting; first matching rule wins. The exact plan for this file:

{"default": "keep", "rules": [{"match": "_exps.weight", "type": "q4_0"}]}

With a Q8_0 source, "keep" leaves attention, embeddings and shared tensors at Q8_0; the single rule sends every stacked expert bank to Q4_0. 2. Build: runner -m Qwen3-30B-A3B-Q8_0.gguf --quantize out.gguf --type-plan plan.json. The quantizer's integrity gate asserts every tensor outside the rules is byte-for-byte identical to the source, not merely that the file loads. 3. Gate: zero-point self-check first (exact: 0.0 KLD / 100% top-1), then 400 teacher-forced positions against the Q8_0 source (scripts/kld-compare-raw.py, greedy, KLD over the union of both sides' top-20 logprobs).

A note on granularity: per-EXPERT precision is not representable in GGUF. Experts are stored stacked, one tensor per layer, and a GGUF tensor carries exactly one type. Per tensor CLASS is the finest split the format allows, and it turns out to be the effective one.

Validation: it passes where the uniform quant fails

Publication bar (this project's, adopted 2026-08-14): margin-qualified top-1 at or above 97% AND mean KLD at or below 0.05 vs the source, over 400 positions. Plain top-1 is always reported beside it.

artifact	size	plain top-1	margin-qualified top-1	mean KLD	verdict
this file	17.99 GB	90.75%	99.50%	0.034	PASS
official uniform Q4_K_M	18.56 GB	79.00%	94.75%	0.114	FAIL

Same source, same protocol, same day, same machine. The selective plan is smaller than the uniform k-quant and 3.3x closer to the source: for a MoE model, precision should not be spent uniformly. The expert bulk tolerates four bits; attention does not.

MoE KLD on this harness carries small cache-state variability in the fourth decimal, so KLD is quoted to three decimals (raw gate JSONs, with per-position records, are in the Xyntetik suite evidence).

Scope and limits

The gate measures agreement with the source model's own distribution, not downstream capability. This artifact has not been benchmarked on tasks.
17.99 GB does not fit a 16 GB machine. Getting under that envelope needs the expert banks at three bits (Q3_K), which this quantizer does not write yet; that is recorded as the open item, not implied.
Expert COUNT is untouched: all 128 experts, top-8 routing, exactly the source's geometry. This is precision reallocation, not pruning (this model measurably does not tolerate pruning; see the account's Qwen3-Coder keep-120 card for the model that does).

Publication policy

This project publishes only artifacts that passed its quality gate, and re-measures published claims under its current standard: one earlier artifact (gpt-oss-20b keep-30) was re-gated under this bar, failed it, and its card now says so plainly. This file passes the same bar that superseded that one. An optimization does not pass because the file got smaller; it has to preserve the model and produce a configuration worth running.

Measured envelope

Every artifact this account publishes ships a measured-envelope sidecar (Qwen3-30B-A3B-expq4_0-attnq8_0.gguf.envelope.json, in this repo) — the same file xyntetik-runner reads at load: download it next to the GGUF and the runner reports the measured state at load. A measured envelope is a dated observation, not a standing quality claim — it records exactly what this file is, where it came from, and what was measured, so the claims above stay traceable.


artifact sha256	`df02efa815a74e6207f9e01f2d38272c1e2718bb23aec544709a02a2c7d5b206`
what changed	selective precision via `--type-plan`: attention+embeddings Q8_0, expert banks Q4_0
reference (Q8_0 source) sha256	`4ad960d180b16f56024f5b704697e5dd5b0837167c2e515ef0569abfc599743c`
fidelity vs parent	PASSES the bar — margin-qualified top-1 99.50%, mean KLD 0.034 (table above)
tool calling	generic constrained envelope (truncation-safe; see the tool-calling section above)
measured	2026-08-19
verdict	experimental — loads with a banner; never a standing certification

Joakimpalm-Zen/Qwen3-30B-A3B-selective-attnQ8_0-expQ4_0-GGUF

Qwen3-30B-A3B selective precision, attention Q8_0 / experts Q4_0 (GGUF)

Serve with xyntetik-runner — tool calls that still parse when the budget runs out