Quantized Gemma 4 31B nearly doubles throughput at half memory

What the tweet revealed

Quantization matters only when speed gains survive contact with real deployment constraints. In its April 13, 2026 X post, Red Hat AI boiled the pitch down to one line:

“nearly 2x tokens/sec, half the memory, 99%+ accuracy retained.”

The comparison is simple but useful: the same Gemma 4 31B base model, but a quantized version that Red Hat AI says can serve almost twice the throughput while cutting memory use in half. If that claim holds across common inference setups, it changes which hardware tiers can realistically run a 31B-class model and how much headroom teams have for batching and latency control.

Open-source context

The Red Hat AI account usually posts open model serving, quantization, and vLLM ecosystem work rather than consumer product teasers. This thread fits that pattern, but it is more concrete than a generic “faster inference” claim because the comments point directly to the LLM Compressor project and a set of published Gemma 4 checkpoints on Hugging Face. GitHub describes LLM Compressor as an easy-to-use library for optimizing models for deployment with vllm, including weight-only and activation quantization, Hugging Face integration, and a safetensors format compatible with vLLM.

Red Hat AI added more evidence in a follow-up reply, saying its team ran 500,000 evaluations on quantized models and tied the results to the paper “Give Me BF16 or Give Me Death?”. The claim there is not merely that quantization can shrink a checkpoint, but that carefully chosen formats can recover 99%+ of baseline accuracy while unlocking materially cheaper serving.

What to watch next is reproducibility on real workloads. Throughput screenshots travel fast on X, but practitioners will want side-by-side measurements across GPUs, prompt lengths, tool-calling traces, and chat-template-sensitive tasks. If independent builders confirm that the open Gemma 4 variants preserve quality while relieving memory pressure, this post will matter because it lowers the cost of running a capable open model instead of just making a benchmark slide look better.

Sources: Red Hat AI X post · LLM Compressor · quantization paper · Red Hat AI Hugging Face models