Gemma 4 31B and 26B A4B: Architecture and Memory Consumption

2026-03-12 · Source: The Kaitchup – AI on a Budget · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cloud Computing & IT Infrastructure · Depth: Advanced, medium

Summary

Google has released Gemma 4, including the 31B dense model and the 26B A4B Mixture-of-Experts (MoE) variant. The 31B model is an evolution of Gemma 3 27B, featuring a 5 local:1 global attention pattern with specialized global layers using unified Keys/Values and Proportional RoPE (p-RoPE) for long-context efficiency. The 26B A4B is a sparse MoE with 8 active experts out of 128 total, plus 1 shared expert. Memory consumption for the 31B model is 61 GB (16-bit) and 20.5 GB (AWQ 4-bit), while the 26B A4B consumes 50 GB (16-bit) and 17.2 GB (AWQ 4-bit). Additionally, Arcee released Trinity-Large-Thinking, a 400B total parameter sparse MoE model with 13B active parameters, featuring a "thinking" stage for improved multi-turn tool use and a 3:1 local/global attention pattern with sliding-window attention (4096 tokens) to reduce KV cache size to 15.70 GiB at max context.

Key takeaway

For AI engineers evaluating large language models for deployment, carefully assess the memory requirements of Gemma 4 31B and 26B A4B, especially with long contexts. Your choice between dense and MoE models, and the level of quantization (e.g., 4-bit AWQ), will dictate necessary GPU hardware. Consider Trinity-Large-Thinking for agentic applications requiring robust multi-turn tool use, noting its optimized KV cache for long contexts.

Key insights

New Gemma 4 and Trinity-Large-Thinking models offer diverse architectures and memory footprints for large language model deployment.

Principles

• MoE architectures can significantly reduce active parameters.
• Quantization is crucial for memory-constrained LLM deployment.
• Hybrid attention patterns optimize KV cache size for long contexts.

Method

Gemma 4 models use a 5 local:1 global attention pattern with Proportional RoPE. Trinity-Large-Thinking employs a 3:1 local/global attention pattern with sliding-window attention in local layers.

In practice

• Use 4-bit AWQ quantization for Gemma 4 to fit on 20GB GPUs.
• Consider MoE models like Gemma 4 26B A4B for lower KV cache needs.
• Evaluate Trinity-Large-Thinking for improved multi-turn agent performance.

Topics

• Gemma 4
• Trinity-Large-Thinking
• Mixture-of-Experts
• GPU Memory Consumption
• Quantization

Best for: MLOps Engineer, AI Engineer, NLP Engineer, AI Scientist, Machine Learning Engineer, AI Architect

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Editorial summary, takeaway, and curation by AIssential. Original article published by The Kaitchup – AI on a Budget.