Computra
Get the right GPU for your AI workload and budget
What it does
Computra recommends the right GPU for your AI workload using public benchmarks, proven usage patterns, and conservative safety margins. It considers workload type, model size, traffic, and priorities to deliver clear, reliable hardware choices that you can deploy quickly. Built for teams from prototype to production, Computra saves time, aligns with cost, value, or performance goals, and helps your infrastructure scale as needs grow.
Does the same job
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General ComputeMay 2026 · generalcompute.com · ▲315AI models that run on an inference cloud optimized for speed
- TCTensorDock Core GPU Cloud – GPU servers from $0.29/hr2022 · tensordock.com · ▲147
Hello HN! I’m Jonathan from TensorDock. After 7 months in beta, we’re finally launching Core Cloud, our platform to deploy GPU virtual machines in as little as 45 seconds! https://www.tensordock.com/product-core Why? Training machine learning workloads at large clouds can be extremely expensive. This left us wondering, “how did cloud ever become more expensive than on-prem?” I’ve seen too many ML startups buy their own hardware. Cheaper dedicated servers with NVIDIA GPUs are not too hard to find, but they lack the functionality and scalability of the big clouds. We thought to…
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Hey HN, Henry from Cactus here! We previously released Cactus Needle, a 14MB agentic LLM for tool call, device use, and structured extraction for phones, wearables, smart homes, small robots and microcontrollers. We got really great feedback here, and have now incorporated the suggestions to release Needle 2. The whole model is a single 14MB binary that runs a full session in 28MB of RAM; 45m parameters at 2bit compression. Needle hits 500 tokens/sec decode speed on a Raspberry Pi 5, sits between 400-1,500 tokens/sec on VR devices like Meta Quest 3S and Apple Vision Pro, and ranges…
AI · 27d ago · cactuscompute.com
Launched alongside, March 2026
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Switch from ChatGPT to Claude with import memory feature
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