Maximum Context for Gemma 4 on 12GB VRAM

Maximum Context for Gemma 4 on 12GB VRAM | Generated by AI

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Question: What is the maximum context length I can try for Gemma 4 26B A4B on an RTX 4070 (12GB VRAM) with my current IQ3_S setup?

Answer:

Short answer: neither 160K nor 80K is realistic on your 12GB VRAM with IQ3_S. Here’s the full breakdown of why, and what you can actually push to.

Why 80K and 160K Are Out of Reach

Your setup uses -ngl 25 to stay within VRAM, and you’re already at ~10.7GB/12GB at 16K context with Q8_0 KV cache. Context length growth directly inflates the KV cache:

Context	KV Cache (Q8_0 est.)	Total VRAM est.	Verdict
16,384 (current)	~4GB	~10.7GB	✅ Stable
32,768	~8GB	~14.7GB	❌ OOM
65,536	~16GB	~22.7GB	❌ Way OOM
80K / 160K	—	—	❌ Not even close

A 24GB GPU (RTX 3090 or 4090) is what you need to run the full 256K context window with room to spare. Your 12GB card simply doesn’t have the headroom.

What’s Actually Achievable on Your RTX 4070

For a real-world 12GB VRAM setup, the practical recommendation is a 128K context with fit-based GPU layer placement for text, and 64K context for vision workloads. However, those figures assume a Q5_K_XL quantization (larger/higher quality than your IQ3_S), which means the model weights use less VRAM proportionally — but the KV cache math still hurts.

With your IQ3_S (10.4GB model on disk), your practical ceiling with Q8_0 KV cache is roughly:

Config	KV Cache type	Estimated Max Context	Notes
Current (`-ngl 25`)	Q8_0	~16K–20K	Stable
Drop to `--cache-type-k q4_0`	Q4_0	~28K–32K	Some quality loss
`-ngl 23` + Q4_0 KV	Q4_0	~32K	More GPU headroom
Switch to Q4_K_M GGUF + Q4_0 KV	Q4_0	~40K–48K	Would need re-download

Recommended Next Steps to Push Context

Step 1 — Try Q4_0 KV cache to halve KV memory:

./build/bin/llama-server \
  -m ./models/gemma-4-26B-A4B-it-UD-IQ3_S.gguf \
  -ngl 25 \
  -c 32768 \
  --cache-type-k q4_0 \
  --cache-type-v q4_0 \
  -np 1 --host 0.0.0.0 --port 8081

Step 2 — If that OOMs, reduce GPU layers: Drop -ngl to 22–23 to free another ~1.5GB for the compute buffer.

Step 3 — For anything beyond 32K, you’d need to switch to Q4_K_M quantization (~13GB disk) and offload more layers to CPU, accepting slower generation speed.

Summary

160K / 80K → Not possible on 12GB VRAM, full stop.
32K → Likely achievable by switching to Q4_0 KV cache.
48K → Possible only with a better quantization (Q4_K_M) + aggressive layer offloading + Q4_0 KV.
As a general guideline, start with 32K in practice for good responsiveness, and only increase context if your use case genuinely requires long documents.

References:

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