16GB VRAM is sweet spot for local LLMs; 24GB+ needed for larger models

For users running large language models locally, 16GB of VRAM is generally sufficient for 7B and most 13B parameter models, especially when using quantization techniques. However, running larger models like 34B parameters requires at least 24GB of VRAM, with the RTX 4090 being a recommended option. For 70B parameter models, a single consumer GPU is often impractical, necessitating dual GPUs or cloud solutions, though future cards like the RTX 5090 aim to address this. AI

IMPACT Guides users on selecting appropriate hardware for running local LLMs, impacting the accessibility and performance of AI models for individuals.

RANK_REASON The article provides practical advice on hardware requirements for running local LLMs, focusing on VRAM, rather than announcing a new model or research breakthrough.

Read on dev.to — LLM tag →

• CodeLlama 13B
• CodeLlama 34B
• GeForce RTX 4060 Ti 16GB
• Gemma 7B
• Llama 2 13B
• Llama 3-70B
• Llama 3 8B
• Minimax M3
• mistral:7b
• RTX 3060 12GB
• RTX 4090
• RTX 5070
• VRAM

• infra

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