tokcalc MCP Server

tokcalc

The open-source LLM serving capacity planner

Plan your LLM deployment before you rent the GPUs.

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

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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.vercel.app](https://tokcalc.vercel.app) — no signup, no tracking, no paywall.

tokcalc is an open-source LLM serving capacity planner with 44 models, 31 accelerators, and 16 quantization formats. It estimates model fit, KV/state memory, throughput, latency, topology, and cost through a shared catalog and calculation engine used by the website, the MCP server, and the CLI.

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

• Generation speed (tok/s)
• Estimated first-token latency + 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
• Live GPU pricing across Azure + AWS + GCP + Vast.ai marketplace (fetched in parallel)
• Shareable URL — your config encoded in the URL hash, send to colleagues
• Copy as Markdown — paste the full result into GitHub issues / Slack / docs

GitHub Action — plan on every PR

If your repo declares a serving plan, tokcalc can check it. Drop a .tokcalc.json at the repo root:

{

"model": "llama3-70b", "gpu": "h100-sxm", "quantization": "fp8", "numGpus": 2, "contextTokens": 32768, "concurrency": 16 }

Then add this to .github/workflows/plan.yml:

name: tokcalc

on: pull_request: paths: [".tokcalc.json"]

jobs: plan: runs-on: ubuntu-latest steps:

• uses: stevecrates489-commits/tokcalc@v0.3.4 with: fail-on-infeasible: true

Every PR that touches the plan gets a comment with the VRAM budget, KV-cache sizing, throughput, TTFT/ITL, and cost — so a reviewer can check the model/GPU choice without leaving the diff.

Pin to a release tag (as above) rather than @main, so the action can't

change under you between runs.

The action runs tokcalc's own src/lib/token-calc.ts from this repository, so the numbers it reports are the same ones the site and the MCP tools produce. There is no second implementation to drift.

Any field can be overridden per-run, or you can skip the config file entirely:

- uses: stevecrates489-commits/tokcalc@v0.3.4

with: model: mixtral-8x7b gpu: h200-sxm quant: fp8 gpus: 2 context: 32768 concurrency: 32

Run the same thing locally without the action:

npx @tokcalc/mcp-server plan --config .tokcalc.json

npx @tokcalc/mcp-server plan --model llama3-70b --gpu h100-sxm --quant fp8 --format json

Exit code is 0 when the plan fits, 2 when it does not, 1 on a usage error — so it works as a CI gate.

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 — and none show their math.

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's the cheapest H100 right now across Azure / AWS / GCP / Vast.ai?"*

*"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

A tab on the homepage (tokcalc.vercel.app → Reference), not a separate route. 6 sub-tables — fully transparent, every record source-linked where available:

• Models (44 entries: Llama 4 Scout/Maverick, Qwen 3 family, DeepSeek V3/R1, Pixtral, BGE-M3, GPT-OSS 20B/120B, Kimi K3, GLM-5.3, ...)
• Accelerators (31 entries: H100/H200/B200/B300, AMD MI300X/MI325X, Intel Gaudi 3, TPU v5p/Trillium, Groq LPU, Cerebras WSE-3, Apple M2/M3/M4 Ultra, ...)
• Quantization (16 formats: FP16/BF16, GGUF Q2_K→Q8_0, GPTQ, AWQ, EXL2, FP8, NVFP4)
• API pricing (13 models with input/cached/output + retired/current status)
• Cloud GPU pricing (all accelerators with $/hr > 0 + typical providers — static curated)
• 🔴 Live pricing (real-time fetch from Azure + AWS + GCP + Vast.ai in parallel — LIVE badges + timestamps)

The long-context capacity planner (the differentiator)

This is the formula the Perplexity research brief called "the most important tokcalc should visibly expose":

$$ \text{KV bytes/request} = 2 \cdot L \cdot T \cdot H_{\text{kv}} \cdot D_h \cdot B $$

For dense attention, prefill cost grows superlinearly with context length:

$$ \text{prefill FLOPs} = \underbrace{2 \cdot N \cdot T}_{\text{linear}} + \underbrace{T^2 \cdot H_{\text{kv}} \cdot D_h \cdot L}_{\text{attention}} $$

tokcalc shows you:

• Max concurrent users at 4K / 8K / 16K / 32K / 64K / 128K / 256K / 512K / 1M context
• KV memory per request at each context length
• Prefill time (with superlinear attention correction beyond 32K)
• Required topology (Single GPU → TP×2/4/8 → TP×8 + Context Parallel)
• RingAttention citation when CP is needed

Live GPU pricing (NEW)

The 🔴 Live pricing sub-tab in Reference fetches real-time GPU prices from 4 providers in parallel via Promise.allSettled:

Each provider shows a LIVE badge with timestamp + cache state. The comparison table shows the cheapest price per GPU across all 4 providers (highlighted in emerald) + per-provider breakdown.

If any provider fails (e.g., GCP_API_KEY not set), the others still work — graceful degradation per provider.

The math, transparently

Every number above comes from a formula you can inspect. No black boxes.

Decode tokens/sec (memory-bandwidth bound)

$$ \text{decode tok/sec} \approx \frac{\text{HBM BW} \cdot \eta_{\text{mem}} \cdot \text{quant\_eff}}{\text{model size}} $$

Where:

• HBM BW = GPU memory bandwidth (e.g., 3350 GB/s for H100 SXM)
• η_mem = 0.65 = typical real-world memory utilization (35% overhead)
• quant_eff = dequantization efficiency multiplier (1.0 for FP16, 1.5 for FP8 on H100, 0.85 for INT4)
• model size = active_params × bytes_per_param (uses ACTIVE params for MoE, not total)

Refs: PagedAttention paper (arxiv.org/abs/2309.06180)

Prefill tokens/sec (compute bound)

$$ \text{prefill tok/sec} \approx \frac{\text{GPU FLOPS} \cdot \eta_{\text{compute}}}{2 \cdot \text{active params}} $$

Where:

• GPU FLOPS = dense FP16/BF16 TFLOPS (sparse values not used)
• η_compute = 0.50 = typical compute utilization
• Factor of 2 = one multiply + one add per parameter per token

For long context (>32K), the superlinear attention correction above applies.

Continuous batching multiplier (workload-specific)

$$ \text{aggregate tok/sec} = \text{decode tok/sec} \cdot \text{batch size} \cdot \text{continuous batching multiplier} $$

Critical caveat: There is no universal continuous batching multiplier. vLLM reported 14–24× vs HF Transformers (extreme), 2.2–2.5× vs TGI. SOSP paper finds 2–4× typical vs FasterTransformer/Orca. tokcalc defaults to a conservative 1.5× and lets you tune.

Refs:

• vLLM blog (2023-06-20)
• PagedAttention paper
• Anyscale continuous batching study

Prompt caching economics (Anthropic / OpenAI)

For shared prefix of length $T_p$, suffix of length $T_u$, output $O$, cache hit rate $h$:

$$ \text{API input cost} = N \cdot \left[ (1-h) \cdot T_p \cdot P_{\text{write}} + h \cdot T_p \cdot P_{\text{read}} + T_u \cdot P_{\text{input}} \right] $$

Anthropic multipliers (verified 2025-2026):

• 5-minute cache write: 1.25× base input
• 1-hour cache write: 2.0× base input
• Cache read: 0.1× base input (90% savings)

OpenAI: cached input discounted 50%, no separate write fee.

Refs:

• Anthropic prompt caching docs
• OpenAI prompt caching guide

Multi-GPU topology recommendation

$$ \text{total needed} = \text{model weights} + (\text{KV per request} \cdot \text{batch size}) $$

Walk the smallest topology that fits:

• Single GPU: total ≤ VRAM × 1
• Tensor Parallel ×2/4/8: total ≤ VRAM × N (weights + KV split evenly)
• TP×8 + Context Parallel: total > VRAM × 8 — use RingAttention to shard KV across nodes

Refs: RingAttention paper