Gemma 2 9B KV-Cache Calculator
Per-token and total key-value cache memory for Gemma 2 9B across context length, batch size and cache precision.
Gemma 2 9B: ~294.0 KB per token at FP16. 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 KV-Cache Calculator
The KV cache is the hidden memory cost of serving Gemma 2 9B: every generated or prompted token stores its attention keys and values for reuse, and at long contexts this cache can rival the model weights themselves. This calculator uses Gemma 2 9B's exact attention geometry (8 KV heads × 224-dim heads × 42 layers) to give per-token, per-sequence and whole-batch cache sizes at FP16, FP8 and INT4 precision. Use it to size batch limits for your GPU or to see what a 128K-context request really costs.
How to use Gemma 2 9B KV-Cache Calculator
- 1Enter your values into Gemma 2 9B KV-Cache 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 KV-Cache Calculator?
- ✓Computes Gemma 2 9B KV-Cache instantly in your browser — no sign-up, no upload, no server round-trip.
- ✓100% free and unlimited, with the exact formula shown: cache/token = 2(K,V) × layers × kv_heads × head_dim × bytes = 2 × 42 × 8 × 224 × bytes.
- ✓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.
Why does the KV cache matter more than weights for serving throughput?+
Weights are paid once per GPU; cache is paid per concurrent request and per token of context. Batch size — and therefore throughput — is capped by how many sequence caches fit in the VRAM left after weights, which is exactly what this tool computes.
What does paged attention change?+
PagedAttention (vLLM) allocates the cache in fixed-size blocks on demand instead of reserving the full context up front, eliminating fragmentation and letting you overcommit. The per-token cost shown here is unchanged — you just stop paying for unused reservation.
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