Llama 2 7B KV-Cache Calculator
Per-token and total key-value cache memory for Llama 2 7B across context length, batch size and cache precision.
Llama 2 7B: ~512.0 KB per token at FP16. Llama 2 7B uses full multi-head attention (32 KV heads), making its KV cache 4× larger per token than Llama 3 8B — a useful contrast when planning long-context workloads.
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 Llama 2 7B KV-Cache Calculator
The KV cache is the hidden memory cost of serving Llama 2 7B: 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 Llama 2 7B's exact attention geometry (32 KV heads × 128-dim heads × 32 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 Llama 2 7B KV-Cache Calculator
- 1Enter your values into Llama 2 7B 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 Llama 2 7B KV-Cache Calculator?
- ✓Computes Llama 2 7B 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 × 32 × 32 × 128 × 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 the Llama 2 7B KV cache so large compared to newer 7–8B models?+
Llama 2 7B caches keys and values for all 32 attention heads (multi-head attention). Newer models like Llama 3 8B or Mistral 7B use grouped-query attention with 8 KV heads, cutting cache memory 4× for the same context length.
Is Llama 2 7B still worth deploying in 2026?+
Mostly for legacy fine-tunes. Newer 7–9B models beat it on quality per parameter and use far less KV-cache memory. If you already have a Llama 2 LoRA in production, this calculator helps you compare its true serving cost against migrating.
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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