70B LLM VRAM Requirements: Inference vs. Fine-Tuning Detailed

Running and fine-tuning large language models, specifically 70 billion parameter models, requires significant GPU memory. For inference, a 70B model needs approximately 141 GB of VRAM at 16-bit precision, 73 GB at FP8, and 40 GB at 4-bit, in addition to memory for the KV cache. Fine-tuning demands more, with QLoRA requiring around 50 GB and full fine-tuning needing up to 1.1 TB. Practical configurations suggest that a single 80 GB GPU can handle 4-bit inference and QLoRA fine-tuning, while FP8 inference may require an H200 or multiple H100 GPUs, and full fine-tuning necessitates a substantial setup like an 8x B200 machine. AI

IMPACT Determines the specific GPU hardware and configurations needed to deploy and train large language models efficiently.

RANK_REASON Detailed technical analysis of hardware requirements for running and fine-tuning large language models. [lever_c_demoted from research: ic=1 ai=1.0]

Read on dev.to — LLM tag →

• 70B Model
• AdamW
• CUDA
• graphics processing unit
• H200
• Hugging Face
• Llama 3.3-70B
• LoRA+
• Modal
• Nvidia B200
• NVIDIA H100
• QLoRA
• vLLM
• VRAM

• infra

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