# Can I Run Gemma 4 31B on RTX 4090 24GB? VRAM & Context Scaling Guide

> **Direct Compatibility Verdict**: Yes, Google Gemma 4 31B runs comfortably on a 24GB RTX 4090 at Q4_K_M, requiring 21.44 GB total memory at 8,192 context (leaving +2.56 GB headroom) and generating ~42 tok/s. However, scaling to 32k context with uncompressed FP16 KV-cache triggers instant CUDA OOM (29.0 GB demand). You must pass `-ctk q8_0` or use vLLM FP8 KV-cache to stay within 22.78 GB.

## Key Memory Physics
| Metric | Value |
|---|---|
| Target AI Model | Google Gemma 4 31B Multimodal |
| Target Hardware | NVIDIA GeForce RTX 4090 (24GB) |
| Q4_K_M VRAM Required | 21.44 GB |
| Available Headroom | +2.56 GB |
| Estimated Generation Speed | ~42 tok/s |

## 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.

## Tier 1 Free Software Optimization ($0)
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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*Interactive Sizing Calculator available at: https://www.gpugarden.com/can-i-run/gemma-4-31b-on-rtx-4090/*