Google Gemma 2 9B Instruct VRAM Requirements Guide
Alternating local sliding window and global attention. Outstanding punch for its size.
>98.2% retention. 5.5 GB weights, leaving +2.0 GB headroom on Intel Arc B580 12GB.
Near-lossless 8-bit precision requiring 14.6 GB VRAM for benchmark evaluations.
Uncompressed FP16 weights taking 23.4 GB. Demands multi-GPU or datacenter accelerators.
Architectural Memory Breakdown & Attention Scaling
Google's Gemma 2 9B features 42 layers with 16 query heads and 8 GQA key-value heads (`headDim = 256`) using alternating 4,096-token sliding window and global attention. Requiring 2.6 GiB of FP16 KV-cache at 8k context and 5.8 GB of Q4_K_M weights, its 9.1 GB baseline footprint fits comfortably on 12GB–16GB graphics cards.
Because Gemma 2 features a wide `headDim = 256`, KV-cache consumes more memory than standard models. Pass `-fa on -ctk q8_0` in llama.cpp to compress cache tensors and ensure smooth execution on 12GB GPUs.
Intel Arc B580 12GB provides 12.0 GB physical VRAM (+2.0 GB operational margin at 8k context).
Run Google Gemma 2 9B Instruct Locally
ollama run gemma-2-9b:q4_K_Mvllm serve Google DeepMind/gemma-2-9b --max-model-len 8192 --gpu-memory-utilization 0.95Frequently Asked Questions
Can Gemma 2 9B run on an 8GB GPU?
Only at aggressive 3-bit quantization (IQ3_M, ~7.2 GB) with context clamped to 4k tokens. For standard Q4_K_M with 8k context, a 12GB GPU is recommended.
Why does Gemma 2 9B punch above its weight on reasoning benchmarks?
Gemma 2 was distilled from a colossal 27B teacher model, retaining high mathematical and logical density despite its compact 9B footprint.
Test Custom Hardware & Context Windows
Simulate Google Gemma 2 9B Instruct across all 31 GPUs, adjust context up to 8,192 tokens, or calculate RunPod break-even costs.
Open Google Gemma 2 9B Instruct in GPU Garden Matchmaker →