Gemma 2 27B VRAM Calculator
Estimate GPU memory to run Gemma 2 27B — weights, KV cache and overhead at FP16/INT8/INT4, with a fits-on-which-GPU verdict.
Gemma 2 27B sits in the sweet spot between 9B and 70B classes — FP16 weights of ~54 GB fit a single H100/A100-80, or a single 24 GB card at 4-bit with care.
Formula
Disclaimer: This tool is for general informational and estimation purposes only and is not professional financial, tax, accounting or legal advice. All figures are estimates — verify with a qualified professional before making decisions. Read the full disclaimer.
About Gemma 2 27B VRAM Calculator
This calculator estimates how much GPU memory (VRAM) you need to run Gemma 2 27B locally or in production. It sums the three real costs of inference: the model weights at your chosen precision (FP16, INT8 or INT4), the key-value attention cache that grows with context length and concurrent sequences, and ~10% runtime overhead for CUDA buffers and fragmentation. Gemma 2 27B sits in the sweet spot between 9B and 70B classes — FP16 weights of ~54 GB fit a single H100/A100-80, or a single 24 GB card at 4-bit with care. Use the precision and context sliders to find the cheapest GPU that actually fits your workload instead of guessing from the parameter count alone.
How to use Gemma 2 27B VRAM Calculator
- 1Enter your values into Gemma 2 27B VRAM Calculator — sensible, domain-typical defaults are pre-filled so you see a real result immediately.
- 2The result recomputes live using the formula shown on the page; there is no button to press.
- 3Adjust any input to compare scenarios, then read the worked example to see the substituted numbers.
Why use Gemma 2 27B VRAM Calculator?
- ✓Computes Gemma 2 27B VRAM instantly in your browser — no sign-up, no upload, no server round-trip.
- ✓100% free and unlimited, with the exact formula shown: VRAM ≈ 1.1 × (P×bytes(precision) + 2×layers×kv_heads×head_dim×ctx×batch×bytes(kv)) — Gemma 2 27B: P=27.2B, layers=46, .
- ✓Runs entirely client-side, so every value you enter stays private on your device.
- ✓Live recompute as you type, with a worked example and authoritative references for trust.
Frequently asked questions
Is Gemma 2 27B a single-GPU model?+
At FP16 it needs ~54 GB — an 80 GB A100/H100 hosts it with cache headroom. At 4-bit (~14–16 GB) it squeezes onto a 24 GB RTX 3090/4090 for local use, making it one of the strongest models that fits prosumer hardware.
Why 16 KV heads here when Gemma 2 9B uses 8?+
The 27B model doubles query heads to 32 and keeps a 2:1 query-to-KV ratio rather than 9B's 2:1 at smaller scale. More KV heads cost cache memory but preserve attention capacity at the larger hidden size of 4608.
How accurate is this Gemma 2 27B VRAM estimate?+
It uses the exact architecture from the model's config.json (layers, heads, KV heads, head dimension) and standard serving math, so weight and cache figures are typically within a few percent. Real usage varies with your inference engine's allocator, paged-attention block size and activation buffers.
Does quantizing the KV cache hurt quality?+
INT8/FP8 KV cache is widely used in production (vLLM, TensorRT-LLM) with negligible quality loss on most tasks, and it halves cache memory. It matters most for long contexts, where the cache rivals or exceeds the weight memory.
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