VRAMGlass
Find the right GPU for local LLMs. Compare VRAM, GPU prices and model memory requirements to choose hardware that fits your models and budget.
A startup from Hong Kong that is founded by voltwake.
Find the right GPU for local LLMs. Compare VRAM, GPU prices and model memory requirements to choose hardware that fits your models and budget.
A startup from Hong Kong that is founded by voltwake.
This page is designed to help you find out whether VRAMGlass is good and if it is the right choice for you.
VRAMGlass is a free web tool for planning hardware for local large language models. Compare GPU specifications, VRAM capacity, memory bandwidth, power and market-specific price references in one place.
Explore model memory requirements and estimate whether a model fits your hardware. Compare asking-price trends across supported markets, with sample counts, observation dates and stale-price labels to explain the data. Market selection and display currency are separate controls.
VRAMGlass also provides methodology notes and practical guides on VRAM, system RAM, quantization and local deployment. It is designed for developers, hardware enthusiasts and people choosing a local AI setup.
Prices are median asking prices, not completed sales. Model fit and performance estimates are planning aids, not guaranteed real-world benchmarks.
Built by voltwake.
Listed in
GPU comparison
Compare VRAM, bandwidth, power and price references.
Model memory planning
Explore quantized weight sizes and estimated hardware fit.
Market price trends
Market-specific asking prices with sample counts and freshness labels.
VRAMGlass brings GPU specifications, model memory planning and market-specific price references into one free web tool for local AI. You can compare VRAM capacity, memory bandwidth and power, explore quantized model weight sizes, and review price histories with sample counts, observation dates and freshness labels.
US used listings on eBay, Chinese used listings on Xianyu and Japanese new offers on Amazon.co.jp are kept as separate market references. Display currency is a separate choice. Methodology notes explain the calculations and their limits: asking prices are not completed sales, and model-fit estimates are planning aids rather than guaranteed benchmarks.
VRAMGlass is designed for developers running large language models locally, hardware enthusiasts comparing GPUs, and people planning a local AI workstation or upgrade. It helps users connect model size and quantization with VRAM and system RAM requirements, then compare hardware specifications and market-specific price references.
Practical guides also help people who are new to local AI understand VRAM, quantization, context memory and offloading.
VRAMGlass is a useful choice if you want to connect model memory requirements with GPU specifications and hardware budgets in one place. Start with a model and quantization, estimate the memory you need, compare GPUs, and review the relevant market price references.
It is free to use and available in English, Simplified Chinese, Traditional Chinese and Japanese. The focus is on transparent planning: source methodology, sample dates and price freshness help you judge the data before making a decision. Use it alongside workload-specific testing, since actual performance depends on your model, runtime and configuration.
VRAMGlass is an independent one-person project founded in August 2026 and maintained by voltwake in Hong Kong. It focuses on a practical question: how can someone choose hardware for the models they want to run locally?
The project brings GPU comparisons, model memory estimates and market-specific price references together, supported by methodology notes and practical guides. Its purpose is to make local AI hardware planning easier to understand while being clear about the limits of price data and estimates.
VRAMGlass is built with Astro and TypeScript, with Cloudflare Workers providing the web runtime and Cloudflare D1 providing database storage. It is delivered as a browser-based tool.
We have collected here some useful links to help you find out if VRAMGlass is good.
Check the traffic stats of VRAMGlass on SimilarWeb. The key metrics to look for are: monthly visits, average visit duration, pages per visit, and traffic by country. Moreoever, check the traffic sources. For example "Direct" traffic is a good sign.
Check the "Domain Rating" of VRAMGlass on Ahrefs. The domain rating is a measure of the strength of a website's backlink profile on a scale from 0 to 100. It shows the strength of VRAMGlass's backlink profile compared to the other websites. In most cases a domain rating of 60+ is considered good and 70+ is considered very good.
Check the "Domain Authority" of VRAMGlass on MOZ. A website's domain authority (DA) is a search engine ranking score that predicts how well a website will rank on search engine result pages (SERPs). It is based on a 100-point logarithmic scale, with higher scores corresponding to a greater likelihood of ranking. This is another useful metric to check if a website is good.
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Is VRAMGlass good? This is an informative page that will help you find out. Moreover, you can review and discuss VRAMGlass here. The primary details have been verified within the last quarter. So they could be considered up to date. If you think we are missing something, please use the means on this page to comment or suggest changes. All reviews and comments are highly encouranged and appreciated as they help everyone in the community to make an informed choice. Please always be kind and objective when evaluating a product and sharing your opinion.
