Google Gemma 2 27B Instruct VRAM Requirements Guide
Distilled from Gemini models; approaches early 70B benchmarks.
>98.2% retention. 16.1 GB weights, leaving +3.2 GB headroom on Dual NVIDIA RTX 3060 Rig (24GB VRAM).
Near-lossless 8-bit precision requiring 34.5 GB VRAM for benchmark evaluations.
Uncompressed FP16 weights taking 60.3 GB. Demands multi-GPU or datacenter accelerators.
Architectural Memory Breakdown & Attention Scaling
Gemma 2 27B is Google's dense powerhouse featuring 46 transformer layers with 32 query heads and 16 GQA key-value heads (`headDim = 128`). Consuming 3.1 GiB of FP16 KV-cache at 8k context and 16.5 GB of Q4_K_M weights, its 20.8 GB baseline footprint fits inside 24GB GPUs like the RTX 3090 and 4090, rivaling 70B models on analytical benchmarks.
In llama.cpp, pass `-fa on -ctk q8_0 -ctv q8_0` to compress key-value cache memory by 50%. This frees over 1.5 GB of VRAM, allowing 16k context window allocations inside 24GB graphics cards.
Dual NVIDIA RTX 3060 Rig (24GB VRAM) provides 24.0 GB physical VRAM (+3.2 GB operational margin at 8k context).
Run Google Gemma 2 27B Instruct Locally
ollama run gemma-2-27b:q4_K_Mvllm serve Google DeepMind/gemma-2-27b --max-model-len 8192 --gpu-memory-utilization 0.95Frequently Asked Questions
Can Gemma 2 27B fit on a 16GB GPU?
At standard Q4_K_M (20.8 GB), it overflows 16GB cards. It requires tight 3-bit quantization (IQ3_M, ~15.2 GB) with clamped context to fit inside 16GB.
What token generation speed does Gemma 2 27B reach on an RTX 4090?
With 1,008 GB/s bandwidth, the RTX 4090 generates approximately 45 tok/s on Gemma 2 27B at Q4_K_M precision.
Test Custom Hardware & Context Windows
Simulate Google Gemma 2 27B Instruct across all 31 GPUs, adjust context up to 8,192 tokens, or calculate RunPod break-even costs.
Open Google Gemma 2 27B Instruct in GPU Garden Matchmaker →