Qwen2.5 7B KV-Cache Calculator
Per-token and total key-value cache memory for Qwen2.5 7B across context length, batch size and cache precision.
Qwen2.5 7B: ~56.0 KB per token at FP16. Qwen2.5 7B pushes GQA further than most peers with only 4 KV heads, making it one of the cheapest 7B models to serve at long (128K) contexts.
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 Qwen2.5 7B KV-Cache Calculator
The KV cache is the hidden memory cost of serving Qwen2.5 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 Qwen2.5 7B's exact attention geometry (4 KV heads × 128-dim heads × 28 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 Qwen2.5 7B KV-Cache Calculator
- 1Enter your values into Qwen2.5 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 Qwen2.5 7B KV-Cache Calculator?
- ✓Computes Qwen2.5 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 × 28 × 4 × 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 can Qwen2.5 7B offer 128K context on consumer GPUs?+
Its 4 KV heads (vs 28 query heads) shrink the per-token cache to ~57 KB at FP16. A 32K-token session costs roughly 1.8 GB of cache — small enough that a 24 GB card can hold FP16 weights plus a genuinely long context.
What is the trade-off of using only 4 KV heads?+
Fewer KV heads compress what attention can store per token; Qwen compensates during training. Quality on retrieval-heavy long-context tasks remains competitive, and the 7× cache saving versus full MHA usually wins on serving economics.
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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