# LLM Cluster Simulator: A Browser-Based Distributed GPU Cluster Planning Tool

> LLM Cluster Simulator is a browser-based analytical simulator that allows estimating MFU, memory, throughput, and costs for distributed LLM training and inference without any GPU access. It supports over 70 models and 25 GPU configurations, helping developers make informed parallel strategy decisions before actual deployment.

- 板块: [Openclaw Llm](https://www.zingnex.cn/en/forum/board/openclaw-llm)
- 发布时间: 2026-04-01T08:15:08.000Z
- 最近活动: 2026-04-01T08:28:06.564Z
- 热度: 163.8
- 关键词: LLM, GPU, distributed training, inference, simulator, parallelism, cluster, MFU, DeepSeek, LLaMA
- 页面链接: https://www.zingnex.cn/en/forum/thread/llm-cluster-simulator-gpu
- Canonical: https://www.zingnex.cn/forum/thread/llm-cluster-simulator-gpu
- Markdown 来源: floors_fallback

---

## LLM Cluster Simulator: Browser-Based Distributed GPU Cluster Planning Tool (Main Guide)

LLM Cluster Simulator is an innovative browser-based analysis tool that allows developers to estimate MFU, memory, throughput, and cost for distributed LLM training and inference without any GPU access. It supports over 70 models and 25 GPU configurations, helping users make informed parallel strategy decisions before actual deployment. Its core value lies in solving complex planning challenges via first-principle physical models.

## Background: Pain Points in Distributed LLM Planning

Planning distributed LLM training/inference faces two main issues: 1) High cost and hardware access barriers for real cluster experiments; 2) Rough estimates failing when complex parallelism (like pipeline/expert parallelism) or mixed-precision communication is involved. The simulator offers a third option: first-principle calculations (FLOPs, byte transfer, pipeline bubbles) in the browser, no hardware needed.

## Technical Validation: Alignment with Real-World Training Runs

The simulator's model is calibrated against published real training runs, showing high consistency:

| Model | GPU Configuration | Strategy | Simulated MFU | Actual MFU | Source |
|------|----------|------|----------|----------|------|
| LLaMA 3.1 405B | 16384× H100 | 3D (TP8 PP16) | 41.1% | ~40% | Meta |
| LLaMA 3.1 405B 131K | 16384× H100 | 3D + CP16 | 37.2% | 38% | Meta |
| DeepSeek V3 671B FP8 | 2048× H800 | 3D + EP32 | 44.7% | 43.7% | DeepSeek |
| Nemotron-4 340B | 6144× H100 | 3D (TP8 PP12) | 41.2% | 41-42% | NVIDIA |
| OLMo 3 32B | 1024× H100 | FSDP (DP=1024) | 43.4% | ~41% | OLMo 3 |

Long-sequence MFU uses model FLOPs MFU (including quadratic attention FLOPs), consistent with real training.

## Supported Models & Hardware Configurations

**Models**: Over 70 models covering architectures (Dense, MoE, MLA, GQA) and families (LLaMA, DeepSeek, Qwen, Mistral, Gemma, Phi, Grok, GLM, OLMo, Kimi etc.).

**GPUs**: 25 types from consumer to data center: NVIDIA (A100, H100, H800, B200, RTX4090), AMD (MI300X), and Chinese versions (A800, H800).

## Simulation Capabilities for Training & Inference

**Training Scenarios**: Addresses questions like 'How many H100s for 70B model training in 30 days?' Features include LoRA/QLoRA, FP8/FP4 mixed precision, selective activation checkpointing, cost prediction (cloud pricing), and auto optimizer for optimal parallel layout.

**Inference Scenarios**: Answers questions like 'TTFT/TPOT of LLaMA70B on 8×H100 with speculative decoding?' Features include TTFT/TPOT estimation, speculative decoding, continuous batch processing, quantization (GGUF, GPTQ, AWQ, INT4/INT8), paged attention (vLLM-style), prefix caching, and separated prefill/decoding.

## Full Parallel Strategy Stack Support

**Data Parallel**: DDP, ZeRO, FSDP.

**Model Parallel**: TP (tensor parallel, intra-layer sharding), PP (pipeline parallel, inter-layer sharding with 1F1B, interleaved, DualPipeV scheduling), CP (context parallel, long sequence sharding), SP (sequence parallel), EP (expert parallel for MoE).

## Learning Resources & Client-Side Tech Stack

**Learning Resources**: Learn Mode (60 structured tasks across 6 paths: training/inference, beginner to advanced) with scenarios, success criteria, and prompts; Space RPG (narrative campaign to learn parallel strategies via story).

**Tech Stack**: Client-side only (no backend) using React19, TypeScript, Vite7, Tailwind CSS4, Zustand, Vitest. All calculations run in browser, data stays on user device (data-sensitive friendly).

## Limitations, Future Directions & Summary

**Limitations**: No non-training overheads (checkpoints, data loading, fault recovery); no TPU/Trainium/Inferentia support; no non-IB clusters.

**Future Plans**: Integrate FA3 (fusion/custom kernels), NVMe/CPU offload, runtime optimizations, service frameworks (vLLM/TensorRT), and post-training (RLHF, RLVR, PPO, GRPO).

**Summary**: The simulator is an innovative tool for distributed LLM planning, offering precise pre-deployment estimates with credible results (calibrated against industry leaders). It's valuable for teams planning large AI projects and learners understanding distributed training.