Compare Groq and Llama-3-70B with GPT-4 Turbo
Just added support for Llama-3 models to our AI app platform Promptly. We decided to try Groq cloud for powering these models and the results have so far been pretty good comparing Llama-3-70B with GPT-4 Turbo. Put an app together to compare these models. Check it out at https://trypromptly.com/a/groq-llama-3-70b-vs-gpt-4-turbo. https://trypromptly.com/s/iQG7EoJ4Pm is a sample output comparison between Llama-3-70B and GPT-4 turbo. https://youtu.be/1UChY6EDwFA shows the inference speed of Groq compared to GPT-4.
Does the same job
all alternatives →- GGGetting GLM 5.2 running on my slow computerJul 2026 · github.com · ▲937
A few days ago I found myself trying out GLM 5.2 and was really positively impressed. The capabilities and security I was getting from this LLM are similar to those I've gotten from models like Claude or GPT, and this really surprised me. But then I thought, "I wonder how it would work on a normal computer like mine," and above all, "I wonder if it would work without going into OOM on a computer like mine." So I started working with the help of agents to test this possibility. I started converting the model to int4, understanding MTP usage, and if possible implementing DSA for long context.…
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I trained a 125M-parameter transformer to autocomplete piano performances in real time (~108 notes/sec on an iPhone 15). The idea is basically GitHub Copilot or Tabnine, except instead of prompting it with code, you prompt it by playing a few notes on a MIDI piano. The model then continues what you played, entirely on-device. The app is free if anyone wants to try it. Happy to answer questions about the model, training, Core ML, or the many things that didn't work.
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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…
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