Google Gemma 4 31B Multimodal VRAM Requirements Guide
Google DeepMind's late-2026 open-weights multimodal flagship. Pairs 60 transformer layers (5:1 1024-token sliding-window + global full-context attention) with native vision-language reasoning and 256k context.
>98% perplexity retention. Standard Ollama/llama.cpp quantization.
Virtually identical to FP16 (~99.9% fidelity). Demands high-capacity VRAM.
Uncompressed weights. Requires multi-GPU clusters or datacenter cards.
Architectural Memory Breakdown & KV-Cache Scaling
Gemma 4 31B (`30.7B` parameters) employs Google DeepMind's 5:1 hybrid sliding/global attention geometry across 60 transformer layers: 50 local sliding-window layers (`sliding_window = 1024`, `16` KV heads × `256` headDim) interleaved with 10 global full-context layers (`4` global KV heads × `512` headDim, scaling up to `262,144` tokens). Because 50 of its 60 layers cap their KV ring buffer at 1,024 tokens, Gemma 4 requires just `1.41 GiB` of FP16 KV-cache at 8,192 tokens (`18.14 GB` Q4_K_M weights + `1.41 GB` KV + `1.89 GB` activation/driver = `21.44 GB`) and only `10.78 GiB` FP16 KV (`5.39 GiB` in FP8 -> `30.96 GB` total) at 131,072 tokens!
Running Gemma 4 31B on a 24GB RTX 3090/4090? Enable `-fa on -ctk q8_0 -ctv q8_0` in llama.cpp (`OLLAMA_FLASH_ATTENTION=1 OLLAMA_KV_CACHE_TYPE=q8_0` in Ollama) to run 32k context in `22.78 GB` VRAM, or deploy `gemma-4-26b-a4b` MoE (`17.14 GB` at 8k) for +4.3 GB extra headroom.
Provides sufficient headroom for weights, 8k+ context KV-cache, and CUDA runtime buffers without Out-Of-Memory (OOM) crashes.
Run Google Gemma 4 31B Multimodal Locally
ollama run gemma4:31b-instruct-q4_K_Mvllm serve google/gemma-4-31b-it --max-model-len 32768 --kv-cache-dtype fp8 --gpu-memory-utilization 0.95Frequently Asked Questions
How does Gemma 4 31B keep 128k–256k KV-cache VRAM so low?
Only 10 of Gemma 4 31B's 60 layers cache the full sequence (`4` KV heads × `512` headDim), while the other 50 sliding-window layers overwrite a fixed 1,024-token ring buffer—slashing 128k FP16 KV-cache to 10.78 GiB instead of ~60 GiB.
What is the best GPU for Gemma 4 31B with long 64k–128k multimodal context?
A 24GB RTX 4090/3090 comfortably handles 8k–32k context (`21.44–22.78 GB`), while the 32GB NVIDIA RTX 5090, 36GB+ Apple M3/M4 Max, or Dual RTX 3090 (48GB) unlocks 128k+ multimodal vision-language context with zero swap.
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