Llm-Numbers

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llm-numbers is a GitHub repository providing essential metrics and calculations for LLM developers. It offers insights into LLM costs, GPU memory requirements, and optimization strategies for large language models.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is llm-numbers?

llm-numbers is a comprehensive GitHub repository curated by ray-project, offering critical numerical insights for Large Language Model (LLM) developers. This resource details various metrics, such as the cost ratios between different OpenAI models (GPT-4 vs. GPT-3.5 Turbo, embedding costs), the cost-effectiveness of self-hosting embeddings versus using commercial APIs, and the financial implications of fine-tuning versus training models from scratch. It also provides crucial information on GPU memory capacities and requirements for serving LLMs, including the impact of batching on throughput. The repository aims to equip developers with the knowledge needed for back-of-the-envelope calculations, enabling informed decisions on infrastructure, cost optimization, and model deployment.

Best used for

Ideal for developers and data scientists who need to perform quick calculations for LLM projects, understand the cost implications of different models and deployment strategies, and optimize GPU resource allocation. Especially valuable for making informed decisions on model selection, fine-tuning, and inference infrastructure.

Common actions

optimize LLM costs

understand LLM performance

plan LLM infrastructure

compare LLM models

estimate GPU needs

"AI Agents"face swappinggithub copilotdeepfakelow-code/no-codeworkflowsopen-sourceautomated workflowcollaboration

Capabilities

Key features

LLM cost ratios
GPU memory requirements
Fine-tuning cost analysis
Batching throughput insights
Token-to-word ratios

Target Audience

developerdata scientiststartup founder

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What kind of numbers does llm-numbers provide?

llm-numbers offers critical data points such as cost ratios for different LLM APIs (e.g., GPT-4 vs. GPT-3.5 Turbo), cost comparisons for self-hosting embeddings, and estimates for training and fine-tuning LLMs. It also details GPU memory requirements for model serving and the impact of batching on throughput.

Is llm-numbers specific to OpenAI models?

While many examples and cost figures in llm-numbers are based on OpenAI models due to their prevalence, the principles and calculations provided are broadly applicable to other LLMs and providers like Anthropic and Cohere. It encourages users to check prices from other providers as well.

How can llm-numbers help with GPU resource planning?

The repository provides insights into typical GPU memory capacities (e.g., V100, A10G, A100) and the memory requirements for serving LLMs based on their parameter count. It also discusses the memory needed per token of output and the throughput improvements gained from batching requests, aiding in efficient resource allocation.

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