Can I Run Gemma 4 31B on RTX 4090 24GB? VRAM & Context Scaling Guide
Model weights, initial KV-cache, and CUDA context buffers.
Available safety margin for OS compositing and context expansion.
Throughput determined by memory bus bandwidth.
Hardware Physics & Bottleneck Analysis
Gemma 4 31B features 64 layers governed by a 5:1 hybrid ratio: 53 local sliding-window attention layers (1024 tokens) and 11 global full-attention layers. At 8k context, this local sliding architecture saves over 75% of typical KV memory compared to full MHA. But once context scales to 32k or 64k tokens, the 11 global layers alone consume 7.56 GB in FP16, pushing total memory past the RTX 4090's 24GB physical capacity unless quantized.
Set `OLLAMA_FLASH_ATTENTION=1` and `OLLAMA_KV_CACHE_TYPE=q8_0` (or add `-ctk q8_0 -ctv q8_0` in llama.cpp). This halves the KV-cache from 10.08 GB to 5.04 GB at 32k, keeping full 32k multi-turn reasoning inside 22.78 GB on your RTX 4090 without spending $1 on upgrades.
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Frequently Asked Questions
Why does Gemma 4 31B crash on RTX 4090 when opening long PDF documents?
When processing long prompts, the global full-attention layers expand memory usage dynamically. If Windows desktop compositing or browser tabs reserve 1.0–1.5 GB VRAM, an 8k prompt can trigger a sudden CUDA allocation failure. Power-limiting desktop display usage or switching to Linux recovers ~1.2 GB.
Is the RTX 5090 32GB necessary for running Gemma 4 31B?
Not for standard 8k–16k chat or coding. The RTX 5090 32GB is only necessary if you require full 64k–128k context with uncompressed FP16 KV-cache or parallel multi-stream batching.
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