Paralleliq

KV Cache & Context Window Cost

Long context sounds great — until it fills your VRAM and crashes your cluster at 3am.

See exactly how context length consumes GPU memory, crushes concurrency, and drives up your cost per token — before it shows up on your cloud bill.

Model & Hardware
Context Window & Traffic
32K
2K4K8K16K32K 64K128K256K1M
RPS
tok
KV Cache Per Request
—
—
—
Model weights VRAM
—
KV cache per request
—
Max concurrent requests
—
Est. cost / 1M context tokens
VRAM Breakdown (per replica)
Model weights
KV cache (max batch)
System overhead (~2 GB)
Free VRAM
Context Length Impact
Context KV Cache / Req Max Concurrent vs. 4K baseline Cost / 1M ctx tokens Feasible?

Running long-context workloads at scale?
Enter your email and we'll send a KV cache optimization report with recommendations for your specific model and traffic profile.

✓ Got it — we'll be in touch.
Something went wrong — email us at info@paralleliq.ai

Paralleliq Scanner (piqc) scans your Kubernetes cluster in seconds.
No agents, no instrumentation, nothing changes in your cluster.

* KV cache sizes are computed from published model architectures (layers, KV heads, head dimension). Actual VRAM usage varies with vLLM's PagedAttention block size, system overhead, and activation memory. Cost estimates assume continuous operation; reserved/spot pricing reduces GPU costs 30–70%.