About Gemma 4 26B-A4B
Gemma 4 26B-A4B is the mixture-of-experts entry in Google DeepMind's open Gemma 4 family — a design its model card summarises as a large expert pool with a small active set per token, trading total memory footprint for markedly fast inference. It carries the family's Apache 2.0 licence, hybrid attention and multimodal input, with the extended context window of the larger tiers.
Google positions it as the fast-inference option: flagship-adjacent output quality at the latency of a much smaller dense model, for users whose hardware can hold the full expert pool in memory.
Gemma 4 26B-A4B is Google's 26B-parameter model released in April 2026, with a 256K-token context window. It is a mixture-of-experts model: all 26B parameters must sit in memory, but only ~4B are active per token, which is what makes it fast for its size. It uses a hybrid-attention design — only a fraction of its layers cache the full context, so long conversations cost far less VRAM than a classic dense model: its KV cache is about 0.5 GB at an 8K context, 5.6 GB at 128K, and 10.9 GB at the full 256K window (FP16 cache).
The MoE arithmetic is the whole story: every expert must be resident, so VRAM planning uses the full parameter count in the tables above, while the small active set is what makes generation quick once loaded. That puts it a clear step above the dense mid-size model in memory terms for similar-or-better speed — the classic MoE trade offered in an open package. The hybrid attention layout keeps cache growth gentle at long context, so the threshold question is simply whether the quantised weights fit your card; where they don't, the dense tiers below give more capability per resident gigabyte.
For most people Q4_K_M is the sweet spot — the most popular quality/size trade-off — while Q8 is near-lossless if you have the memory. Totals above include the KV cache and a realistic framework overhead, so they are what you should expect to see in practice rather than just the download size. Weight sizes are calibrated against real GGUF files — see the methodology.
Frequently asked questions
How much VRAM does Gemma 4 26B-A4B need?
At Q4_K_M with an 8K context, Gemma 4 26B-A4B needs about 19 GB (weights 16 GB + KV cache + overhead). The smallest common hardware that fits is a RX 7900 XTX.
Can an RTX 4090 (24GB) run Gemma 4 26B-A4B?
Yes. An RTX 4090's 24 GB runs Gemma 4 26B-A4B at Q5_K_M (about 22 GB at 8K context).
Can a Mac run Gemma 4 26B-A4B?
Yes — Apple Silicon with 32 GB of unified memory or more (macOS lets the GPU use ~75% of it, ~24 GB) runs Gemma 4 26B-A4B at Q4_K_M.
How much VRAM does Gemma 4 26B-A4B need at its full 256K context?
The KV cache grows with context: about 0.5 GB at 8K tokens versus 10.9 GB at the full 256K window (FP16 cache). Add that to the weights (16 GB at Q4_K_M) plus overhead — long contexts can cost more than a whole quantisation step.
Related
VRAM calculator for any model ·Token counter
Similar-size local models: Qwen3.6 27B (27B) · Qwen3 Coder 30B-A3B (30.5B) · Gemma 4 31B (31B) · GPT-OSS 20B (21B) · Qwen3.6 35B-A3B (35B)
Last updated 2026-09-03. Architecture figures from the model's published config.json; see the methodology.