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Estimate LLM VRAM from parameter count, weight precision, context, concurrency and KV-cache architecture.
$ model: parameters + quantization $ memory = weights + KV cache + runtime $ context x concurrency => KV growth $ output: safe VRAM target
Weight memory is only the starting point. Long context, concurrent requests, KV cache, runtime workspaces and model architecture increase total GPU memory. This tool separates weights, KV cache and runtime headroom.
This tool produces a starting estimate for capacity planning; validate production decisions with load tests and real monitoring data.
Change the inputs and calculate again.
A useful baseline is parameters × bits/8. Quantization metadata, alignment and runtime copies can raise real use.
A coarse decoder-only formula is 2 × layers × KV heads × head dimension × context × concurrency × bytes per element.
Two GPUs only combine effectively when the runtime and model-parallel strategy support it.
Peak generation, batching, CUDA graphs and kernel workspaces can cause OOM even if model loading succeeds.
The target weight precision is around four bits, but group scales and metadata add overhead.
KV cache generally grows roughly linearly with context and concurrency for a fixed architecture.
No. Training also needs activations, gradients, optimizer states and batch memory.
Some runtimes support it, trading GPU memory for latency and throughput.
Share your workload, traffic profile and growth target so we can determine the appropriate VPS or GPU server class.
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