LLM Inference Performance Analytic Tool for Moe Models (DeepSeek/etc.)

Hacker News

I built this to answer "what-if" questions about LLM deployment without spinning up expensive infrastructure. The tool models inference physics - latency, bandwidth saturation, and PCIe bottlenecks for large MoE models like DeepSeek-V3 (671B), Mixtral 8x7B, Qwen2.5-MoE, and Grok-1. Key features: - Independent Prefill vs Decode parallelism config (TP/PP/SP/DP) - Hardware modeling: H100, B200, A100, NVLink topologies, IB vs RoCE - Optimizations: Paged KV Cache, DualPipe, FP8/INT4 quantization - Experimental: Memory Pooling (TPP, tiered storage) and Near-Memory Computing - offload cold experts and cold/warm KV-cache to system RAM, node-shared or global-shared memory pool Live demo: https://llm-inference-performance-calculator-1066033662468.u... Built with React, TypeScript, Tailwind, and Vite. Disclaimer: I've calibrated the math models but they're not perfect. Feedback and PRs welcome.

Visit product View on Hacker NewsNov 27, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: model, models, grok · Missing: mac, agents, macos

76%76% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Indie HackersFits the IH revenue-focused audience · Strong signals: para · Missing: supports, reddit linkedin, podcasting

57%57% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive

54%54% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: pipe, io · Missing: https docs, excited, just released

42%42% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRLess likely to generate early MRR · Strong signals: para · Missing: mobile apps, ios, personal

32%32% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

16%16% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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