Gemma 4 26B A4B Large Context Guide

Gemma 4 26B A4B Large Context Guide | Generated by AI

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Question: Can we set the context size to 16384 for the Gemma 4 26B A4B running on llama-server with -ngl 25 and a 12GB VRAM GPU?

Answer:

Short Answer: Yes — But You Need KV Cache Quantization

Good news: Gemma 4 26B A4B has a huge architectural advantage for large context sizes thanks to its Sliding Window Attention (SWA) design. Going from 2048 → 16384 context will cost much less VRAM than you’d expect compared to a regular model.

Why Gemma 4 26B A4B Is Good for Large Context

Gemma 4 26B A4B uses a 5:1 local/global attention pattern:

25 local layers → use SWA with only a 1024-token sliding window (not the full context!)
5 global layers → full attention over the entire context

The KV cache formula for Gemma 4 26B-A4B works out to approximately 5.20 GiB at max context in bf16, because the sliding layers only cache 1024 tokens, not the full sequence.

This means scaling from 2048 → 16384 context mostly only grows the KV cost for those 5 global layers, not all 30 layers.

VRAM Estimate at 16384 Context

Your current state:

Model weights on GPU (25 layers): ~9.5 GB
Available headroom: ~2.5 GB

KV cache at 16384 tokens (FP16, no quantization):

5 global layers × full 16384 tokens = relatively modest
25 local layers × only 1024-token window (SWA) = fixed small cost

According to community testing on the 31B model (which has similar architecture), llama.cpp shows about 22.3 GB VRAM usage at 16384 context with FP16 KV cache — but that is the much larger 31B dense model. For the 26B MoE which is far more KV-efficient, the cost is significantly lower.

The risk is that FP16 KV at 16384 context may still OOM on 12GB. The safe approach is to quantize the KV cache.

Recommended Config

./llama-server \
  -m ./models/gemma-4-26B-A4B-it-UD-IQ3_S.gguf \
  -ngl 25 \
  -c 16384 \
  --cache-type-k q8_0 \
  --cache-type-v q8_0 \
  --host 0.0.0.0 \
  --port 8081 \
  --path ./tools/server/public

Dropping KV cache from FP16 to Q4 saves about 1.8 GB of VRAM at 16K context with essentially no perceptible quality degradation (BLEU score impact around -0.3%).

If q8_0 still OOMs, go more aggressive:

  --cache-type-k q4_0 \
  --cache-type-v q4_0 \

Optional: Limit Parallel Sequences

If you are the only user, add -np 1 to your llama.cpp config — this reserves space for a single SWA KV instead of the default 4, saving significant VRAM.

./llama-server \
  -m ./models/gemma-4-26B-A4B-it-UD-IQ3_S.gguf \
  -ngl 25 \
  -c 16384 \
  --cache-type-k q8_0 \
  --cache-type-v q8_0 \
  -np 1 \
  --host 0.0.0.0 \
  --port 8081 \
  --path ./tools/server/public

What to Watch For

Signal	Meaning
OOM on startup	Reduce to `-c 8192` or use `q4_0` KV cache
OOM mid-inference	Add `-np 1`, reduce `-ngl` to 23
Slower speed	Expected — larger context = more compute per token
Still ~38 t/s	Great sign — SWA keeps speed relatively stable

TL;DR Strategy

Try first: -c 16384 --cache-type-k q8_0 --cache-type-v q8_0 -np 1
If OOM: switch to --cache-type-k q4_0 --cache-type-v q4_0
If still OOM: reduce -ngl from 25 to 22–23 and retry

The SWA architecture of Gemma 4 26B A4B is specifically designed to make large context affordable — it’s one of the best models for this on limited VRAM.

References:

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