Llama 3 8B KV-Cache Calculator
Per-token and total key-value cache memory for Llama 3 8B across context length, batch size and cache precision.
Llama 3 8B: ~128.0 KB per token at FP16. Meta's Llama 3 8B uses grouped-query attention (8 KV heads vs 32 query heads), which cuts KV-cache memory 4× compared with Llama 2 7B at the same context length.
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 3 8B KV-Cache Calculator
The KV cache is the hidden memory cost of serving Llama 3 8B: 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 3 8B's exact attention geometry (8 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 3 8B KV-Cache Calculator
- 1Enter your values into Llama 3 8B 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 3 8B KV-Cache Calculator?
- ✓Computes Llama 3 8B 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 × 8 × 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
Can Llama 3 8B run on a 12 GB GPU?+
Yes, in 4-bit quantization. The INT4 weights need roughly 4.5 GB, leaving headroom for the KV cache and CUDA overhead on a 12 GB card such as an RTX 3060. FP16 inference needs about 16 GB of weights alone, so it will not fit unquantized.
Why does Llama 3 8B need less KV-cache memory than Llama 2 7B?+
Llama 3 uses grouped-query attention with only 8 key-value heads, while Llama 2 7B caches all 32 heads. Per token per layer that is 4× fewer cached vectors, so an 8K-token context costs about 1.1 GB instead of over 4 GB at FP16.
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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