Gemma 2 9B VRAM Calculator
Estimate GPU memory to run Gemma 2 9B — weights, KV cache and overhead at FP16/INT8/INT4, with a fits-on-which-GPU verdict.
Gemma 2 9B has an unusually large 256K-token vocabulary — its embedding table alone is ~1.8 GB at FP16, a bigger share of total memory than in any peer model.
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 9B VRAM Calculator
This calculator estimates how much GPU memory (VRAM) you need to run Gemma 2 9B 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 9B has an unusually large 256K-token vocabulary — its embedding table alone is ~1.8 GB at FP16, a bigger share of total memory than in any peer model. 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 9B VRAM Calculator
- 1Enter your values into Gemma 2 9B 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 9B VRAM Calculator?
- ✓Computes Gemma 2 9B 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 9B: P=9.24B, layers=42, k.
- ✓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
Why is Gemma 2 9B bigger on disk than other 9B models?+
Its 256,000-entry vocabulary. Embeddings cost 256000 × 3584 × 2 bytes ≈ 1.8 GB at FP16 (tied input/output), versus ~0.9 GB for Llama 3's 128K vocab — that is weight you pay regardless of context length.
Does Gemma 2's alternating local/global attention change this estimate?+
Gemma 2 alternates 4096-token sliding-window layers with global layers. Serving stacks that exploit it cache only the window for local layers, roughly halving long-context cache; this calculator shows the conservative full-cache figure.
How accurate is this Gemma 2 9B 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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