LLM planner - pick a rig for your use-case/model/budget, or pick models for your rig. 60+ builds, 50+ models, 130+ cited t/s sources, 150+ reviewer YouTube videos, idle+active watts, multi-region prices, regular updates.

TL;DR: Sourced internet info into llm model/hardware choise guide. Two directions:

• "What rig should I buy for use-case / model /budget?"
• "I have a 3090 / M3 Max / DGX Spark / Strix Halo / R9700. What runs well on it?"

Plus a side-by-side compare mode for rigs and LLMs. Tokens/sec numbers cite a source; every build links the actual reviewer YouTube videos.

Why I built it: Needed to pick what I buy, 5090, spark or strix halo. Ended up with spark made by asus. I was building it to the point where I didnt need to exit the site and go google something.

What's actually in it:

• 60+ specific build configs of all sorts, plug and play on-off switch, datacentre on-off switch
• Decode tok/s + prompt-processing tok/s at Q2/Q4/Q5/Q8 per model
• 100K promt processing time to the first token
• Idle + active power draw in watts
• Used + new prices, multi-region
• 150+ reviewer YouTube videos linked across the builds (so you can watch the review to make an opinion)
• 130 cited sources across leaderboards, model cards, llama.cpp benchmark threads, Tom's Hardware, and this sub
• Reverse mode: paste hardware -> see open-weights that fit, ranked across chat/coding/agents/reasoning, with the closed-frontier four (Gemini 3.1 Pro, GPT-5.5, Sonnet 4.6, Opus 4.7) shown as ceiling reference
• Data is updated at least once a week.

What it does NOT do:

• Gives a link to a cheapest price in your region.
• Gives absolute best tps for can get for your hardware. Mileage may vary based on quant/software, patches and updates

Link: https://llmrequirements.com

All the data is exported into public repo
https://github.com/Trenin-Labs/LlmRequirements
There's link on the website to submit benchmark or report inaccuracies using issues on github for this public repo.