Llama 3 70B KV-Cache Calculator
Per-token and total key-value cache memory for Llama 3 70B across context length, batch size and cache precision.
Llama 3 70B: ~320.0 KB per token at FP16. Llama 3 70B is the workhorse open-weights model for multi-GPU nodes: FP16 weights alone are ~141 GB, so single-GPU use is only practical at 4-bit on an 80 GB card.
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 70B KV-Cache Calculator
The KV cache is the hidden memory cost of serving Llama 3 70B: 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 70B's exact attention geometry (8 KV heads × 128-dim heads × 80 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 70B KV-Cache Calculator
- 1Enter your values into Llama 3 70B 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 70B KV-Cache Calculator?
- ✓Computes Llama 3 70B 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 × 80 × 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
How many GPUs do I need to serve Llama 3 70B at FP16?+
FP16 weights are about 141 GB, so you need at least 2× 80 GB (A100/H100) GPUs with tensor parallelism just for weights, plus KV-cache per concurrent request. Most production deployments use 4× 80 GB for comfortable batch sizes.
Does Llama 3 70B fit on a single A100 80 GB?+
Only when quantized. At 4-bit (GPTQ/AWQ) the weights drop to roughly 35–40 GB, which fits with room for an 8K context KV cache. At FP16 or even INT8 (~71 GB) the margin is too small for real workloads.
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.
Related ML & AI tools
ROC-AUC Calculator (from TPR/FPR points)
Trapezoidal area under the ROC curve from your (FPR, TPR) operating points — the threshold-independent ranking score.
● LiveClassification Threshold Cost Calculator
Find the probability cutoff that minimizes expected cost given your false-positive and false-negative penalties.
● LiveSilhouette Score Calculator
Cluster cohesion vs separation for one point — the building block of the silhouette metric for choosing K.
● Live