Local GPU SLA Profiler: A Local GPU Performance Benchmarking Tool

This article introduces Local GPU SLA Profiler, a Python benchmarking tool designed specifically for local GPU systems. It analyzes GPU memory usage, vector search latency, and LLM inference speed, with optimizations for consumer GPUs like the RTX 3090.

• Original Author/Maintainer: sajad-bana-zadeh
• Source Platform: GitHub
• Original Title: local-gpu-sla-profiler
• Original Link: https://github.com/sajad-bana-zadeh/local-gpu-sla-profiler
• Publication Date: June 11, 2026

With the popularity of Large Language Models (LLMs) and Computer Vision (CV) technologies, more and more developers and researchers are choosing to run AI models locally. Compared to cloud APIs, local deployment offers advantages such as better data privacy, no network latency, and lower long-term costs. However, local deployment also brings new challenges: how to accurately evaluate system performance to ensure it meets the Service Level Agreement (SLA) requirements of applications?

Local GPU SLA Profiler was created to address this issue. It is an independent Python benchmarking tool designed specifically for single-GPU systems (e.g., workstations equipped with RTX 3090), used to comprehensively analyze three key performance dimensions:

1. GPU Memory (VRAM) Usage
2. Vector Search Latency
3. Local LLM Inference Speed

In MVP stages or offline AI systems, computer vision tasks, RAG (Retrieval-Augmented Generation) retrieval, and local LLM inference often run on the same machine, competing for limited GPU resources. This resource competition can lead to:

• Memory Overflow: Insufficient memory when multiple models are loaded simultaneously, causing program crashes
• Performance Fluctuations: Unstable inference latency due to concurrent tasks
• Unpredictability: Difficulty in estimating system performance under actual load without benchmark data

Although consumer GPUs like the RTX 3090 offer high cost-effectiveness, they lag behind professional GPUs (such as A100 and H100) in terms of memory bandwidth and number of computing units. Benchmarking tools designed for data center GPUs often fail to accurately reflect the actual performance of consumer GPUs.

Memory is one of the biggest bottlenecks in local deployment. This tool can:

• Peak Memory Measurement: Record the maximum memory usage during model loading and inference
• Memory Growth Curve: Track changes in memory usage over time
• Multi-Model Scenarios: Test memory competition when multiple models are loaded simultaneously

The performance of RAG systems largely depends on the speed of vector retrieval. The tool supports:

• Comparison of Different Vector Databases: Such as FAISS, Chroma, Milvus, etc.
• Impact of Index Types: Test performance differences between different index structures like HNSW and IVF
• Data Scale Expansion: Performance changes from thousands to millions of vectors

For local LLM inference, the tool can measure:

• First Token Latency: Time from input to the generation of the first output token
• Throughput: Number of tokens generated per second
• Concurrent Performance: Performance when handling multiple requests simultaneously