It'd be hard to tell whether it's open-weight or open-source because they may both use the same license like MIT, Apache V2 etc. (But the definition of open-source is tricky, some only call it open source if training code is available under open source licenses as well)
@rasbt
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Exploring Redbubble for LLM Architecture Gallery poster printing
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Also looked into alternative poster print places and also added a Redbubble page (redbubble.com/i/poster/LLM-A…) I am new to this so I don't know which one is better quality-wise. Ordered one from there too so I might be able to tell in a couple of days.
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Upcoming AI Model Updates: Gemma 4 and DeepSeek V4
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I'll try to update it with the major ones. E.g., Gemma 4 next week (likely), and DeepSeek V4 any day now
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LLM Architecture Gallery Poster Now Available on Zazzle
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Upon popular request, you can now also get this as a physical poster via Zazzle zazzle.com/llm_architecture_… This is based on a 56 MB PNG file with 182 megapixels. I just ordered one myself but please be aware that I haven't been able to verify the quality, yet.
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LLM Architecture Gallery with YAML Metadata Available
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I can offer you the metadata in YAML format: https://
github.com/rasbt/llm-arch
itecture-gallery
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New LLM Architecture Gallery Collecting All Figures in One Place
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I (finally) put together a new LLM Architecture Gallery that collects the architecture figures all in one place! https://
sebastianraschka.com/llm-architectu
re-gallery/
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Using LLMs to Expand Unit Tests and Run Benchmarks
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No, that's my code. But I used LLMs to help expand unit tests and run benchmarks outside the book etc.
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Impressions initiales positives, mais performance ralentie avec llama.cpp
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I have a good first impression, but it's still a tad slow for me (using llama.cpp). About 2x slower than gpt-oss 120B on the same hardware. I think I need to look for the NVIDIA-optimized stack.
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TPUs vs Groq LPU: Training-Inference Trade-offs in Hardware Design
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I agree with you. But I think TPUs are still trying to do a training-inference trade-off. I think Groq LPU would be an example that is purely designed for inference.
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Trade-offs between training throughput and inference efficiency in chips
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I think it's about trade-offs. As with so many things, there's no free lunch. Ie I don't think you can have a chip that is sota for training throughput but also sota in terms of inference efficiency at the same time
That being said, there are companies focused on inference chips