# ray-project/llm-numbers

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_How this analysis was created: the description and tags below were written by an AI model that read this project's README and public documentation pages; stars, license and language come straight from the GitHub API. The model does not read the source code._

4,310 stars · 140 forks

## Links

- GitHub: https://github.com/ray-project/llm-numbers
- awesome-repositories: https://awesome-repositories.com/repository/ray-project-llm-numbers.md

## Description

llm-numbers is a set of calculation tools and benchmarks used to predict hardware requirements, token usage, and operational costs across various model tiers. It provides a cost and resource calculator based on formulas and benchmarks to estimate tokens, GPU memory, and operational expenses for large language models.

The project includes a hardware requirement planner for calculating the VRAM and GPU memory needed to host models based on parameter counts. It also features a token estimator that converts word counts into token estimates to predict API billing and context window usage, alongside pricing benchmarks that compare costs and throughput trade-offs between different hosting methods.

The toolset covers AI model benchmarking and cost forecasting, GPU resource planning, and performance analysis for measuring throughput gains from batching. It utilizes deterministic formulas and static benchmark datasets to map parameters to memory and calculate the cost-benefit ratios between base models and fine-tuning.

## Tags

### Artificial Intelligence & ML

- [Model Benchmarks](https://awesome-repositories.com/f/artificial-intelligence-ml/large-language-models/model-benchmarks.md) — Provides comparative pricing and throughput benchmarks for different generative AI model tiers and hosting methods.
- [GPU Resource Planning](https://awesome-repositories.com/f/artificial-intelligence-ml/gpu-resource-planning.md) — Calculates the required video memory for serving models based on parameter counts and output limits.
- [Heuristic Token Estimators](https://awesome-repositories.com/f/artificial-intelligence-ml/heuristic-token-estimators.md) — Approximates token counts from raw text lengths using predefined ratios based on common tokenizer behavior.
- [LLM Resource Calculators](https://awesome-repositories.com/f/artificial-intelligence-ml/llm-resource-calculators.md) — Provides a comprehensive set of formulas and benchmarks for estimating tokens, GPU memory, and operational expenses.
- [LLM Token Counters](https://awesome-repositories.com/f/artificial-intelligence-ml/llm-token-counters.md) — Predicts token counts in text to estimate billing costs and verify context window compatibility.
- [Deterministic Resource Estimators](https://awesome-repositories.com/f/artificial-intelligence-ml/deterministic-resource-estimators.md) — Uses deterministic mathematical formulas to produce predictable hardware and cost projections instead of runtime simulations.
- [LLM Performance Optimization Libraries](https://awesome-repositories.com/f/artificial-intelligence-ml/llm-performance-optimization-libraries.md) — Measures throughput gains from batching and compares efficiency between base and fine-tuned models.
- [Model Performance Analysis](https://awesome-repositories.com/f/artificial-intelligence-ml/model-performance-analysis.md) — Analyzes model throughput gains from batching and evaluates the cost-benefit of fine-tuning versus base models. ([source](https://github.com/ray-project/llm-numbers/blob/main/README.md))

### DevOps & Infrastructure

- [ML Resource Estimators](https://awesome-repositories.com/f/devops-infrastructure/infrastructure-requirements/memory-capacity-estimators/ml-resource-estimators.md) — Calculates the GPU memory and compute requirements needed to serve large language models. ([source](https://github.com/ray-project/llm-numbers#readme))

### Operating Systems & Systems Programming

- [Model VRAM Estimators](https://awesome-repositories.com/f/operating-systems-systems-programming/kernel-core-internals/process-and-memory-management/memory-management/allocation-strategies/dynamic-memory-allocation/custom-memory-allocators/memory-footprint-estimators/model-vram-estimators.md) — Estimates GPU VRAM requirements by calculating the memory footprint of model parameters and activation tensors.

### Scientific & Mathematical Computing

- [Operational Cost Estimators](https://awesome-repositories.com/f/scientific-mathematical-computing/research-analysis-workflows/economic-analysis-tools/operational-cost-estimators.md) — Provides tools for projecting total operational expenses and infrastructure costs for large language models. ([source](https://github.com/ray-project/llm-numbers#readme))

### Software Engineering & Architecture

- [LLM Fleet Capacity Planners](https://awesome-repositories.com/f/software-engineering-architecture/capacity-planning/llm-fleet-capacity-planners.md) — Plans the required GPU memory and VRAM needed to host models based on their parameter counts.

### System Administration & Monitoring

- [AI Model Cost Forecasting](https://awesome-repositories.com/f/system-administration-monitoring/ai-model-cost-forecasting.md) — Compares pricing across different model tiers and hosting methods to forecast AI operational budgets.
- [Token Cost Calculators](https://awesome-repositories.com/f/system-administration-monitoring/usage-monitoring/token-usage-analytics/token-cost-calculators.md) — Computes usage expenses by applying model-specific pricing to estimated token counts. ([source](https://github.com/ray-project/llm-numbers/blob/main/README.md))

### Data & Databases

- [Static Benchmark Datasets](https://awesome-repositories.com/f/data-databases/static-benchmark-datasets.md) — Provides immutable datasets of model performance metrics for consistent cross-model throughput and latency analysis.

### Part of an Awesome List

- [Natural Language Processing](https://awesome-repositories.com/f/awesome-lists/ai/natural-language-processing.md) — Listed in the “Natural Language Processing” section of the FunNLP awesome list.