How much VRAM does Gemma 2 27B need at Q4_K_M? Single GPU performance
Gemma 2 27B is the dark horse of local inference. The 19.2 GB footprint at Q4_K_M leaves room for context expansion or batched serving, and the model quality outperforms its size class. A genuinely good pick for a 24 GB GPU.
Gemma 2 27B at Q4_K_M with 8K context needs about 19.2 GB of VRAM. That's a comfortable fit on any 24 GB card with substantial headroom for longer contexts or larger batch sizes. Gemma 2 punches above its weight on benchmarks, often matching 70B-class models on instruction following and chat quality.
By TechCompare · Updated
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Estimated VRAM required
19.2 GB
27B params at Q4_K_M, 8,192 token context, batch 1, inference.
Estimate accuracy: Weights within ~2%. KV cache within ~5% for standard GQA models, ~10% for MLA or hybrid state estimates. Real VRAM may vary with framework (vLLM vs llama.cpp vs Transformers), Flash Attention, and driver overhead.
Sliding-window attention applied: This model caps 1 of every 2 layers at a 4096-token window. KV cache estimate is 25% smaller than naive full-attention math at this context length.
Hardware that fits
Apple entries show installed unified memory separately from a model budget based on an 8 GB reserve. That budget is a planning heuristic, not a fixed hardware limit.
How this is calculated
Gemma 2 27B has 46 layers and a 4608 hidden size, slightly different aspect ratios from Llama-style models which makes its KV cache lighter per token. At 8K context you're looking at about 15.1 GB of weights and 2.3 GB of KV cache. The architecture also features sliding-window attention which can be exploited at longer contexts to reduce KV memory further if your inference engine supports it.
Verdict
The 19.2 GB total breaks down as 15.1 GB of Q4 weights and 2.3 GB of KV cache at 8K context, with the lighter per-token KV cost coming from Gemma 2's 46-layer / 4608-hidden architecture. The sliding-window attention is the structural lever: 5 of every 6 layers cap the KV cache at 4096 tokens regardless of context, which means you can push context to 32K or 64K and the cache cost grows roughly 6x slower than a fully-global model would. That's what makes Gemma 27B a productive use of a 24 GB card: it leaves real headroom for longer contexts or batched serving while punching above its parameter weight on instruction-following and chat-quality benchmarks versus peers like Qwen 2.5 32B at the same memory budget.
More Gemma scenarios
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Frequently asked questions
Is Gemma 2 27B better than Qwen 2.5 32B?
Does Gemma 2 use sliding-window attention?
How does sliding-window attention affect long-document Q&A?
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