This product was not featured by Product Hunt yet. It will not be visible on their landing page and won't be ranked (cannot win product of the day regardless of upvotes).
Honest benchmarks for running open-weights AI models on your own GPU. 84 models × 27 consumer GPUs — from RTX 3060 to Apple Silicon — each pair gets an honest verdict: runs, tight fit, or won't fit, with real speed and VRAM numbers. Plus 800+ step-by-step install recipes (llama.cpp, Ollama, ComfyUI, MLX) and a GPU Advisor. No invented numbers, no signup, free under CC BY-SA.
Hi Product Hunt 👋
Every time I wanted to run a new open model locally I hit the same wall: "will this actually fit on my GPU?" The real numbers were out there — but scattered across Reddit threads, blog posts and GitHub issues, half of them untested. Mostly you'd just get vendor marketing, guesses, or "just buy a 4090."
So I built Smeltcore: those numbers pulled into one place, each linked to its source and sanity-checked.
Pick your GPU (RTX 3060 → 4090, or Apple Silicon) and see every model it can run — with real speed and peak-VRAM numbers and a plain verdict: runs, tight fit, or won't fit. 84 models, 27 GPUs, 800+ step-by-step install recipes for llama.cpp, Ollama, ComfyUI and MLX. Free, no signup, CC BY-SA.
No invented numbers. Every benchmark links to its source, "doesn't fit" is a first-class answer, and community submissions go through a review gate.
It started as a spreadsheet for myself and turned into this. Would love your feedback — especially which model × GPU pairs you're missing. And if you've benchmarked something, there's a /contribute form 🙏
the "tight fit" verdict is genuinely useful, like knowing exactly when you'll be sweating at 99% VRAM is way more honest than those blanket "it runs" claims. and no signup for the full thing under CC BY-SA is a really solid move.
Finally a benchmark site that doesn't oversell performance. Checked the RTX 3060 vs 7B models section and the VRAM numbers matched what I actually get running llama.cpp locally. That kind of honesty is refreshing.
finally a benchmark site that doesn't oversell. checked my 4070 against a few models and the VRAM numbers actually matched what ollama shows locally. the install recipes are a nice touch too.
About Smeltcore on Product Hunt
“Which AI models actually run on your GPU”
Smeltcore was submitted on Product Hunt and earned 6 upvotes and 5 comments, placing #160 on the daily leaderboard. Honest benchmarks for running open-weights AI models on your own GPU. 84 models × 27 consumer GPUs — from RTX 3060 to Apple Silicon — each pair gets an honest verdict: runs, tight fit, or won't fit, with real speed and VRAM numbers. Plus 800+ step-by-step install recipes (llama.cpp, Ollama, ComfyUI, MLX) and a GPU Advisor. No invented numbers, no signup, free under CC BY-SA.
Smeltcore was featured in Developer Tools (516.2k followers) and Artificial Intelligence (474.3k followers) on Product Hunt. Together, these topics include over 185.6k products, making this a competitive space to launch in.
Who hunted Smeltcore?
Smeltcore was hunted by Vadim Leavitskiy. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
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Vadim Leavitskiy