I built an analytical simulator that estimates MFU, training time, memory, throughput, and cost for distributed LLM training and inference. 70+ models, 25 GPUs, all major parallelism strategies (FSDP, TP, PP, EP, CP, ZeRO). Runs entirely client-side — no backend, no data collection.

Best for sweeping strategies, sanity-checking cluster budgets, and building intuition for parallelism tradeoffs — not a substitute for profiling production workloads. Calibrated against published runs from Meta, DeepSeek, and NVIDIA within 1-2 percentage points MFU:

- LLaMA 3.1 405B (16K H100): 41.1% sim vs ~40% published

- DeepSeek V3 (2048 H800): 44.7% sim vs 43.7% published

- Nemotron-4 340B (6144 H100): 41.2% sim vs 41-42% published

Important caveat: the model captures physics (compute, memory bandwidth, communication) but not runtime optimisations and fused kernels.

Configs to try:

- LLaMA-3.1 (405B) on 16,384x NVIDIA H100 — https://simulator.zhebrak.io/?preset=llama3-405b

- Qwen3 MoE (235B) on 4x NVIDIA H200 SXM — https://simulator.zhebrak.io/?preset=qwen3-235b-inference

GitHub with benchmarks and examples: https://github.com/zhebrak/llm-cluster-simulator

If you have published training runs with MFU or throughput numbers, I'd love to hear from you to expand calibration.