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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

Total VRAM required
19.2 GB
Gemma 2 27B at Q4_K_M
Weights
15.1 GB
27B params
KV cache
2.3 GB
8K tokens, FP16 KV

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Estimated VRAM required

19.2 GB

27B params at Q4_K_M, 8,192 token context, batch 1, inference.

Weights
15.1 GB
KV cache
2.3 GB
Overhead
1.7 GB

Estimate accuracy: Weights within ~2%. KV cache within ~5% for standard GQA models, ~10% for MLA (DeepSeek). 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

RTX 3090
Consumer
24 GB
80% used
RTX 5090
Consumer
32 GB
60% used
A100 40GB
Datacenter
40 GB
48% used
Apple M3 Max 64GB
Unified
48 GB
40% used
Same 27B @ Q4_K_M, 8,192 context scenario, pre-loaded. Change the quant, shrink the context, or try a different preset.
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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

DeepSeek V4 Pro 1.6T (MoE) at Q4_K_M
DeepSeek V4 Pro 1.6T at Q4_K_M with the full 1M-token context needs about 1012 GB of VRAM with every expert resident - this is the real shape of the model and the number to plan a deployment against.
View details ➜
Llama 4 Scout (17B/109B) at Q4_K_M
Llama 4 Scout at Q4_K_M with its native 10M context needs about 2231 GB of VRAM with all 109B params resident - that's the number you size hardware against.
View details ➜
gpt-oss 20B (MoE) at Q4_K_M
gpt-oss 20B at Q4_K_M with native 128K context needs about 19.4 GB of VRAM with all experts resident, dropping to roughly 9.3 GB with active-only weight loading.
View details ➜

Related guides

Prompt optimization
Cutting tokens is the cheapest way to cut cost.
View guide ➜

Frequently asked questions

Is Gemma 2 27B better than Qwen 2.5 32B?
They trade benchmarks. Gemma 2 27B is stronger on instruction following and chat quality, Qwen 2.5 32B is generally better on code and math. Both fit on a 24 GB GPU at Q4_K_M.
Does Gemma 2 use sliding-window attention?
Yes, alternating sliding-window and full attention layers. Inference engines that exploit this can reduce KV cache significantly at long contexts, llama.cpp and vLLM both support it.
Does Gemma 2 use sliding-window attention?
Yes, Gemma 2 27B uses sliding-window attention on alternating layers (5:1 ratio of sliding to full attention). This caps the KV cache size at 1024 tokens per layer regardless of context, which makes long-context inference much cheaper. Combined with the 27B parameter count, it's a competitive choice if you need both reasoning and long context under one memory budget.
How does sliding-window attention affect long-document Q&A?
It makes the context cheap but narrows which tokens see which. Sliding-window layers only attend to the most recent 1,024 tokens, so a detail early in a long document only surfaces if one of the full-attention layers carries it forward. In practice the alternating layers route enough global context through for most Q&A, but needle-in-a-haystack retrieval at extreme context is where SWA models show their seam.

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