Mistral 7B KV-Cache Calculator
Per-token and total key-value cache memory for Mistral 7B across context length, batch size and cache precision.
Mistral 7B: ~128.0 KB per token at FP16. Mistral 7B combines grouped-query attention (8 KV heads) with a 4096-token sliding attention window, so effective cache use can stay bounded even at its 32K context.
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 Mistral 7B KV-Cache Calculator
The KV cache is the hidden memory cost of serving Mistral 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 Mistral 7B'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 Mistral 7B KV-Cache Calculator
- 1Enter your values into Mistral 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 Mistral 7B KV-Cache Calculator?
- ✓Computes Mistral 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 × 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
Does Mistral 7B's sliding window reduce KV-cache memory?+
In vLLM-style serving with rolling-buffer cache, yes: the window caps attention at 4096 tokens per layer, so cache memory stops growing past the window. Naive implementations that keep the full 32K cache still pay the full cost this calculator shows.
What GPU runs Mistral 7B comfortably?+
A 16 GB GPU (RTX 4060 Ti 16 GB, T4) runs FP16 weights (~14.5 GB) tightly; 24 GB (RTX 3090/4090) is comfortable with long contexts. At 4-bit it runs on 8 GB cards with the cache being the limiting factor.
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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