Datasets:
The quality king, quantized: Gemma 4 31B QAT + MTP on a 32GB card
Rig: RTX 5090 32GB (capsule, llama.cpp b9562) · Date: 2026-06-08 · Task: local-ai-roadmap t039
Arms: gemma-4-31B-it-qat-q4_0 (QAT) vs naive Q4_0 vs Q6_K (the prior quality king) · MTP drafter gemma-4-31B-it-MTP-Q8_0
The setup
Gemma 4 31B was already this rig's quality king — but at Q6_K it OOM'd at 32K context on a 32GB card. Two June-2026 drops promised to fix both ends: Google's QAT checkpoints (near-BF16 quality at Q4) and MTP merged into llama.cpp (a draft head for ">2x" decode). So: does QAT hold the crown at a third the VRAM, does MTP deliver, and does the lighter model finally handle long context? Three things to settle on our own hardware.
1. Quality — Q4 barely costs anything, and QAT ≈ naive Q4
q_avg (5 tasks, think-off, 50% MMLU/HellaSwag, full ARC/GSM8K/HumanEval):
| MMLU | ARC-C | HellaSwag | HumanEval | GSM8K | q_avg | |
|---|---|---|---|---|---|---|
| QAT-Q4_0 | 87.25 | 97.61 | 91.60 | 97.56 | 97.27 | 94.26 |
| naive Q4_0 | 87.46 | 97.18 | 90.94 | 97.56 | 97.19 | 94.07 |
| Q6_K (king) | 87.82 | 97.61 | 91.95 | 96.34 | 97.50 | 94.24 |
All three land within 0.19 q_avg of each other. QAT-Q4 (94.26) ties Q6_K (94.24) and beats a dumb Q4_0 by a noise-level +0.19. The honest read: Gemma 4 31B is remarkably quantization-robust — Q4 costs essentially nothing on standard benchmarks, and QAT's specific edge over naive Q4 doesn't show up here. QAT's value isn't a better benchmark number; it's that Q4 at no quality cost is what unlocks the next two wins.
2. Speed — MTP is real: 1.67x decode (2.3x vs the old king)
Decode throughput (tok/s):
- Q6_K: 55 · QAT-Q4: 76 (smaller quant alone) · QAT-Q4 + MTP: 125
MTP (draft head, --spec-type draft-mtp --spec-draft-n-max 4, 33% draft acceptance) lifts QAT-Q4 from 76 to
125 tok/s — a 1.67x decode speedup. Short of the headline ">2x" (acceptance-rate bound at our settings), but
substantial and verified. Stacked against the Q6_K king's 55 tok/s, the QAT-Q4 + MTP combo is 2.3x faster —
the quant shrink and the draft head compounding.
3. Long context — the king finally clears 128K
QAT-Q4 server load at depth, vs Q6_K's prior wall:
| context | QAT-Q4 | Q6_K (prior) |
|---|---|---|
| 32K | ✅ 23.8 GB | ❌ OOM |
| 64K | ✅ 26.4 GB | — |
| 128K | ✅ 31.5 GB | — |
QAT-Q4 loads the full 128K context (31.5 of 32GB) where Q6_K couldn't even reach 32K. This is the real payoff of the VRAM cut: not a quality story, a capability story. The quality king couldn't do long context on this card; quantized, it can.
The wedge (why QAT matters even though it doesn't win on quality)
QAT's benchmark edge over naive Q4 is negligible — but that's not the point. Q4 (either kind) holds Gemma 4 31B's quality and halves the footprint, and that freed VRAM is exactly what buys the 128K context and the headroom for the MTP draft head. Lighter isn't the feature; lighter is what makes faster-and-longer possible on one card.
Feasibility notes (Blackwell + the MTP gotcha)
- llama.cpp rebuilt 9365 → 9562 to land Gemma4 MTP (PR #23398); Donald (the resident agent) verified
on the new binary before proceeding. The
mistral4, gpt-oss, gemma archs all still load. - The drafter gotcha: the first community MTP GGUF (
...-assistant, archgemma4_assistant, underscore) is rejected as "unknown architecture." The working one is unsloth'sMTP/gemma-4-31B-it-MTP-Q8_0.gguf(archgemma4-assistant, hyphen). Post-merge the MTP-head GGUF format is still settling — match the arch the build expects. - 31B Q4/Q6 fit 32GB fully (
-ngl 999 -fa on), no offload.
Harness: the rig's bench.py quality+speed + scripts/mtp_speed.sh (MTP via llama-server /completion timings)
- a long-context load sweep. Raw scores:
results/. think-off, 50% sample — matches the prior Q6_K methodology.