tokcalc MCP Server

tokcalc

The open-source LLM serving capacity planner

Plan your LLM deployment before you rent the GPUs.

Switch model → multi-GPU → long-context capacity planner → Build vs Buy → Reference catalog

tokcalc turns your LLM traffic, context length, latency SLOs, cache behavior, and model choice into a defensible serving topology and cost plan — with transparent formulas and cited benchmarks.

It's the engineering-grade pre-deployment decision layer for LLM inference. Not another static "tokens per second" calculator.

Try it now

tokcalc.dev — no signup, no tracking, no paywall.

Pick a model, a GPU, and a workload. Get an instant capacity plan:

• Generation speed (tok/s)
• Time-to-first-token (TTFT) + inter-token latency (ITL)
• VRAM budget with KV-cache sizing
• Multi-GPU topology recommendation (Single GPU → TP×2/4/8 → Context Parallel)
• Monthly cost + break-even vs API pricing
• Shareable URL — your config encoded in the URL hash, send to colleagues

Why tokcalc?

The market has dozens of "tokens per second" calculators and self-host-vs-API break-even tools (induwara.lk, gigagpu, kickllm, cloudparity, curlscape, profitable.ai). None of them are unified capacity planners.

What tokcalc answers that competitors can't

"Can I serve Qwen 2.5 72B at 128K context on 2× H100 with 20 concurrent users?"

"How many H200s do I need for 1,000 req/min with P95 TTFT < 2s?"

"Does FP8 or AWQ save more money once quality, KV cache, and engine support are included?"

"At what daily volume does an H100 beat GPT-4o pricing?"

"What happens to cost and latency if an agent makes 8 model calls, has 3 tool calls, and its context grows by 5K tokens each turn?"

"Would prefix caching, continuous batching, or PD disaggregation save more for this workload?"

Features

Calculator tab

Build vs Buy tab

Independent calculator (separate state) that compares:

• Self-host: model + GPU + quant + utilization + batch → $/M tokens + monthly cost
• API: 13 providers (OpenAI / Anthropic / Gemini / Groq / DeepSeek / Mistral / Together)
• Verdict: Self-host cheaper / API cheaper / Not enough volume — with break-even reqs/day

Reference tab

Shortened. The full README is on GitHub.