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The 500MB Club Challenge
2 CPUs. 500MB RAM. One real Pi. Prove your language.
Most backend benchmarks run on generous cloud boxes nobody deploys to. This one doesn't: a write-heavy telemetry service (GPS/battery/accel data) has to run — LB, 3 API replicas, storage — inside 2 CPUs and 500MB RAM total, on a real Raspberry Pi. Open, language-agnostic: fork it, implement the API, ship a Docker image, submit a PR. Scored on efficiency, capacity, tail latency, resilience, stability. Sponsored by Ardan Labs, JetBrains, GopherCon Latam. Deadline: July 26.
Hey! I built this because every backend benchmark I could find ran on
hardware nobody actually ships services to — 4, 8, 16 vCPUs, gigabytes of
RAM. That's not what edge or cost-constrained deployments look like, and
it hides exactly the stuff I care about as a backend engineer: how a
runtime behaves when it doesn't have room to hide its mistakes.
So I put the whole stack — load balancer, 3 API replicas, storage — on a
single Raspberry Pi with a hard 2 CPU / 500MB ceiling, and picked a domain
that's realistically annoying: ingesting GPS/battery/accelerometer data
from thousands of couriers in real time, the kind of write-heavy,
tail-latency-sensitive workload that sits behind any delivery or mobility
app's live map.
Anyone can submit in any language — Go, Rust, Zig, Node, Python, Java,
whatever you want to prove a point with. The repo has the OpenAPI
contract, the load scripts, and the full scoring breakdown.
Huge thanks to Ardan Labs, JetBrains, and GopherCon Latam for sponsoring
prizes. Deadline is July 26 — if you don't have time to build something,
an upvote or a share to someone who'd enjoy this goes a long way. Happy to
answer anything about the scoring, the hardware setup, or why I picked
this domain.
How is the scoring actually weighted — does efficiency matter more than raw throughput, or do they get balanced somehow when one tanks the other?
Genuinely curious how scoring works when language runtimes vary so wildly. Like would a Rust implementation get penalized because it leaves headroom that a Go or Node setup just can't, or is the scoring pure black-box against the four metrics?
finally a benchmark that actually reflects what production looks like on tiny boxes. the rpi constraint makes it way more honest than synthetic cloud tests, and i love that the scoring includes resilience not just raw speed.
running the full stack on a real pi is the kind of constraint that actually exposes the sloppy stuff in your code. love that it's open and language agnostic, makes it feel like a real community challenge rather than a marketing stunt.
love how it forces you to actually respect the constraints of a pi instead of pretending your laptop is prod. the scoring on tail latency and stability is a nice touch too.
About The 500MB Club Challenge on Product Hunt
“2 CPUs. 500MB RAM. One real Pi. Prove your language.”
The 500MB Club Challenge was submitted on Product Hunt and earned 5 upvotes and 7 comments, placing #74 on the daily leaderboard. Most backend benchmarks run on generous cloud boxes nobody deploys to. This one doesn't: a write-heavy telemetry service (GPS/battery/accel data) has to run — LB, 3 API replicas, storage — inside 2 CPUs and 500MB RAM total, on a real Raspberry Pi. Open, language-agnostic: fork it, implement the API, ship a Docker image, submit a PR. Scored on efficiency, capacity, tail latency, resilience, stability. Sponsored by Ardan Labs, JetBrains, GopherCon Latam. Deadline: July 26.
The 500MB Club Challenge was featured in Open Source (68.6k followers), Developer Tools (515.4k followers) and GitHub (41.3k followers) on Product Hunt. Together, these topics include over 112.7k products, making this a competitive space to launch in.
Who hunted The 500MB Club Challenge?
The 500MB Club Challenge was hunted by Carlos Henrique Guardão Gandarez. 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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Carlos Henrique Guardão Gandarez