A final LLM benchmark for 8GB VRAM 16GB RAM

Comprehensive Benchmarking of Small-Scale Large Language Models for Consumer Hardware

In recent years, large language models (LLMs) have revolutionized natural language processing, but their impressive capabilities often come with hefty hardware requirements. For enthusiasts and hobbyists working on consumer-grade systems—such as PCs equipped with modest GPUs and limited RAM—the challenge is locating models that strike a balance between performance and feasibility. This article presents a thorough, practical benchmarking study tailored to such setups, specifically focusing on models that can run effectively on an RTX 4060 with 8GB VRAM and 16GB system RAM.

Purpose and Scope

This analysis aims to identify the best-performing local LLMs for individuals constrained by hardware resources. Unlike the high-profile, massive models like Qwen 3.8 27B or GPT-4, my focus is on models that can operate efficiently on consumer hardware without prolonged inference times or prohibitive costs. While I’m not a professional AI researcher, my experience as an LLM hobbyist has given me insights into what works well in this context.

Benchmarking Methodology

Over the past few weeks, I conducted comprehensive tests to evaluate various models across multiple dimensions, including accuracy, inference speed, and overall efficiency. The tests involved 18 questions divided into six categories:

General Knowledge
Mathematics
Coding Tasks
Endurance (ability to handle lengthy or complex prompts)
Logical Puzzles
Spatial Reasoning

All models were tested with the following considerations:

Inference was performed with “thinking” disabled to streamline benchmarking. (Enabling reasoning or chain-of-thought processing significantly increases response time.)
Response length was capped at 4,096 tokens to maintain consistency.
The total benchmarking process spanned approximately 2.5 hours, with notable time spent on the Muse Glimmer 30B model—highlighting its impressive but resource-intensive performance.
Responses and scores were manually evaluated to ensure accuracy.

The codebase, along with detailed questions and results, is openly available on GitHub.

Results and Insights

Visualizing Performance

The benchmarking results are best understood through several visualizations:

Per-question performance heatmap: Highlights strengths and weaknesses across models and question categories.
Aggregated score graph: Summarizes overall model effectiveness.
Average Time-to-Completion (TTC): Measures inference latency.
Efficiency metric (“intelligence per second”): Balances accuracy against speed to indicate real-world usability.
Score vs. Speed Plot: Combines quality and efficiency for quick comparison.

Note: A notable outlier was Muse Glimmer 30B, which, despite excellent accuracy, required so much time to generate responses that its performance was visually compressed in the graphs.

Key Findings

The models tested cover a broad spectrum, from lightweight options to more capable variants suitable for consumer hardware. The detailed metrics are available in the shared visualizations, but some highlights include:

Most balanced model: GPT-OSS 20B offers a compelling mix of speed and accuracy.
Best for casual experimentation: Bonsai 27B is fun to tinker with and offers decent performance.
High-precision inquiries: Ministral 4 14B excels in accuracy, making it suitable for detailed questions.
Fast and relatively intelligent: Ling 3.0 Tiny provides quick responses with reasonable understanding.

Practical Recommendations

Depending on your specific use case, here are some tailored suggestions:

For casual exploration and fun: Bonsai 27B
For accurate, detailed answers: Ministral 4 14B
For rapid, lightweight inference: Ling 3.0 Tiny
For a balanced compromise: GPT-OSS 20B

Remember, the “best” model depends on your primary needs—whether it’s speed, accuracy, or a mix of both.

Helpful Resources

Below are direct links to the models tested, hosted on Hugging Face:

Final Thoughts

Selecting the right language model for personal, hardware-limited environments involves balancing accuracy, speed, and resource requirements. Benchmarking such models provides valuable guidance for enthusiasts seeking effective solutions without costly infrastructure.

Whether you aim to experiment, answer complex questions, or build lightweight AI tools, the insights shared here aim to help you make informed choices aligned with your hardware constraints.

For further details, visualization charts, and the benchmarking code, visit the Github repository.

Disclaimer: These results are tailored for models running on modest consumer hardware and may vary based on specific system configurations and workloads.

Author: A hobbyist exploring accessible AI solutions on consumer hardware

Date: October 2023