Cost analysis reveals fine-tuning 70B models ranges from $1-2 per million tokens

Fine-tuning a 70 billion parameter model can cost between $1 and $2 per million training tokens, with a 20 million token LoRA run estimated at around $20. The cost is primarily determined by the number of tokens trained and the number of experimental runs, with GPU utilization and hourly rates also playing significant roles. Factors like LoRA, QLoRA, and gradient checkpointing affect the computational operations per token, influencing the overall expense. While full fine-tuning involves more computation and memory, the cost difference compared to LoRA is less significant than the impact of GPU speed and hourly pricing, with newer GPUs like the Nvidia B200 offering the cheapest math per dollar. AI

IMPACT Provides cost-planning data for researchers and developers fine-tuning large language models.

RANK_REASON Analysis of computational costs and methods for fine-tuning large language models. [lever_c_demoted from research: ic=1 ai=1.0]

Read on dev.to — LLM tag →

• A100
• AdamW
• H200
• Hugging Face
• Kaplan et al.
• Llama 3.1 70B
• Llama 3.3-70B
• LoRA+
• Meta*
• node
• Nvidia B200
• NVIDIA H100
• QLoRA

• infra

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →