How to establish resource needed to run Large Language Models like DeepSeek, LLAMA, QWEN2.5-Coder etc

Following resource requirement is needed before you try to setup your own machine

1. GPU configuration with VRAM
2. Processors
3. System RAM
4. HardDrive Capacity
5. Mother Board
6. Power Supply
7. Cooling Fan

Following videos can be watched to get precise information like how did I figure out the optimal & cost effective system resources to run Large Language Models locally.

NVIDIA RTX 3060 12GB GDDR6 RAM — Perfect choice for DeepSeekR1–14 Billions Parameters to Run locally

NVIDIA RTX 3060 12GB GDDR6 RAM — Inference Speed is slower for DeepSeekR1–32Billions Parameters
Reason1 — Seems related to System memory (Action : Increase system RAM to 64GB to see if inference speed gets improved.)
Reason2 — GPU dedicated Memory (Action : Upgrade GPU from RTX 3060 to RTX 4060 with 12GB or 16 GB or 24 GB.)

How one can established the system configuration required for running Large Language Models locally : NVIDIA RTX 3060 12GB GDDR6 I7 12TH Generation Processor with 64 GB RAM — LLAMA3.3 latest 70 Billions needs 64GB System RAM with slow inference speed. Action items to increase inference speed
Action1: Will upgrade GPU with 4060 to see if inference speed gets increased or not.
Action2: If not then will try with 5070 ti GPU that should improved inference speed.

NVIDIA GPU Upgrade from RTX3060 12GB to RTX4060 8GB — Worth It? (To observe impact on inference speed for LLMs)

Unboxing RTX4060 8GB to upgrade RTX3060 12GB

Will keep updating this page based on my identified action items. so stay tune & keep watching my videos to get latest information for setting up your own system.