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GPU Sizer Review

GPU Sizer is an online tool that calculates appropriate GPU hardware for machine learning models based on deterministic VRAM calculations and validated throughput predictions.

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GPU Sizer — product screenshot

Why it matters

1Offers deterministic VRAM calculations for LLM inference.
2Provides validated throughput predictions against real hardware with an 11.3% out-of-sample median error.
3Tracks over 100 GPUs and 90+ models, with auto-updated lists.
4Includes a free tier for all functionalities.

About GPU Sizer

Business Model
Freemium SaaS
Headquarters
Singapore
Target Audience
Data scientists, machine learning engineers

Pricing Plans

Free Tier
$0 / forever
  • • Free account, no card required
  • • Real math engine
  • • Answers in milliseconds

Leadership

Sriram SivakumarLinkedIn
GitHub

Specs

API Available

Yes, public API

overview

What is GPU Sizer?

GPU Sizer is a machine learning infrastructure tool developed by Sriram Sivakumar that enables machine learning practitioners, AI/ML engineers, and infrastructure planners to accurately determine the optimal Graphics Processing Unit (GPU) for running Large Language Model (LLM) inference workloads. It aims to eliminate the costly trial-and-error process of renting or purchasing GPUs that may not fit a specific model's requirements by providing deterministic VRAM calculations and validated throughput predictions against real hardware. The tool's methodology page discloses any misses in its predictions, ensuring transparency.

features

Key Features of GPU Sizer

GPU Sizer provides a comprehensive suite of features designed to assist in the precise selection and configuration of GPUs for LLM inference. These functionalities are built upon deterministic VRAM calculations and throughput predictions validated against real-world hardware benchmarks, claiming an 11.3% out-of-sample median error.

  • Calculator: Input HuggingFace model ID, quantization, context length, and desired concurrency to receive exact VRAM requirements and validated tokens per second (tok/s) for compatible GPUs, ranked by value, performance, and efficiency.
  • Node Designer: Visually construct multi-GPU clusters, including interconnects, to assess workload scalability across multiple cards.
  • Token Factory: Convert a serving configuration into a profit and loss (P&L) statement, detailing cost per million tokens, potential margins, and break-even volumes for financial planning.
  • AI Advisor: An AI model provides recommendations, trade-offs, and framework-specific launch commands based on user-specific numerical inputs.
  • Deterministic VRAM Breakdown: Offers precise VRAM calculations for LLM inference, accounting for model parameters, precision (e.g., FP32, FP16, INT8, INT4), KV cache, and overhead.
  • Validated Throughput Predictions: Provides real decode speed (throughput) predictions for LLM inference, benchmarked against actual hardware data.
  • GPU and Model Tracking: Continuously auto-updates its database, tracking over 100 GPUs and 90+ models to ensure current and relevant data.

use cases

Who Should Use GPU Sizer?

GPU Sizer is primarily designed for professionals and teams involved in the deployment and optimization of machine learning models, particularly Large Language Models. Its functionalities address critical needs in hardware selection, cost estimation, and infrastructure planning.

  • Machine Learning Practitioners & AI/ML Engineers: For calculating exact VRAM requirements and predicting real decode speed (throughput) for LLM inference, ensuring optimal GPU selection before deployment.
  • Infrastructure Planners & Procurement Teams: For comparing and ranking GPUs by value, performance, and efficiency for a given workload, and for visually building and sizing multi-GPU clusters.
  • Researchers & Developers: For validating GPU selections prior to purchasing or renting, and for estimating cost per million tokens and profitability for LLM serving configurations.
  • Businesses Deploying LLMs: For sizing workloads accurately and without guesswork, thereby reducing the financial risk associated with incorrect hardware provisioning.

how to use

How to Use GPU Sizer

GPU Sizer provides an intuitive web interface for users to input model specifications and receive GPU recommendations. The process involves selecting a model, defining parameters, and reviewing the generated hardware insights.

  • 1Navigate to the GPU Sizer website (gpu-sizer.com).
  • 2Access the 'Calculator' feature to begin sizing a model.
  • 3Input a HuggingFace model ID, specify quantization (e.g., FP16, INT4), context length, and desired concurrency.
  • 4Review the generated list of compatible GPUs, ranked by value, performance, and efficiency, along with exact VRAM requirements and validated tokens per second.
  • 5Utilize the 'Node Designer' to visually construct multi-GPU clusters for scaling analysis.
  • 6Employ the 'Token Factory' to generate a profit and loss statement for LLM serving configurations.

pricing

GPU Sizer Pricing & Plans

GPU Sizer operates on a freemium business model, offering full access to its features without any cost. This allows users to leverage its deterministic VRAM calculations, validated throughput predictions, and other tools for free.

  • Free Tier: $0 (Provides access to all core functionalities, including the Calculator, Node Designer, Token Factory, and AI Advisor, without any subscription fees or usage limits.)

Pros

  • +Provides deterministic VRAM calculations and validated throughput predictions for LLM inference, reducing guesswork.
  • +Offers a comprehensive free tier with access to all core features, including advanced tools like Node Designer and Token Factory.
  • +Continuously updates its database of over 100 GPUs and 90+ models, ensuring current and relevant data.
  • +Features an AI Advisor that provides personalized recommendations and framework-specific launch commands.
  • +Enables visual construction of multi-GPU clusters and detailed cost analysis for LLM serving configurations.
  • +Transparently discloses an 11.3% out-of-sample median error for its predictions, fostering trust.

Cons

  • −Does not currently offer an API for programmatic integration into other systems.
  • −Focuses primarily on LLM inference, potentially limiting utility for other machine learning tasks like training or different model types.
  • −While validated, predictions are still estimates and real-world performance can vary based on specific software stacks and optimizations.
  • −The tool's utility is dependent on the accuracy and completeness of its auto-updated GPU and model database.

Similar Tools

GPU Sizer vs Competitors

GPU Sizer differentiates itself in the market by offering precise, validated predictions specifically for LLM inference, aiming to reduce the guesswork and financial risk associated with GPU procurement. It stands apart from generic hardware specifications and manual benchmarking efforts.

1

Skorppio's VRAM Calculator for AI & ML

Calculates VRAM requirements for AI/ML models based on parameters, precision, and task, mapping estimates to GPU configurations.

Visit→
2

Model GPU Calculator (SIAM AI Cloud)

Estimates the GPU count required for Hugging Face models across popular GPU options, considering precision and overhead.

Visit→
3

LLM RAM Calculator (Token-Calculator.net)

Estimates GPU memory needed to load and serve large language models based on parameter count, precision, and runtime overhead.

Visit→
4

GPU Memory Calculator for LLMs

A web-based tool to calculate approximate GPU memory required for serving LLMs based on model parameters and quantization bits.

Visit→

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