NEMOTRON 3 ULTRA IS HERE
MoE – 550B, 55B Active – Base and Instruct in BF16
– NVFP4 Instruct (350GB)
– 1M Context Window
– Supports MTP Spec Decoding This release IS NOT just about the Weights
It's an Opensource Frontier Intelligence Handbook – Training Data (Pre & Post)
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@theahmadosman
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NEMOTRON 3 ULTRA: Open Source Frontier Intelligence Handbook with 1M Context
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Dario sees open-source AI catching up; Anthropic’s Claude Code boom over
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Dario when he realizes Opensource AI is catching up and Anthropic has no moat + last Christmas Claude Code boom is never going to happen again
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Everything You Need to Know About Inference Engines and Local LLMs
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Everything You Need To Know About
Inference Engines and Running LLMs Locally at Home Explains why Inference Engines exist in the first place
– Prefill is not Decode
– VRAM is not bandwidth
– Fit is not speed
– KV Cache is the real memory problem
– Quantization only matters if -
Step-by-step LLM engineering projects roadmap
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Step-By-Step LLM Engineering Projects Roadmap – Build a tokenizer
– Learn embeddings
– Implement RoPE / ALiBi
– Hand-wire attention
– Build MHA
– Build a Transformer block
– Train a mini-former
– Compare objectives
– Build sampling
– Speculative decoding
– KV cache
– MQA / GQA / -
User keeps 8GB RTX 3060 for RAG instead of streaming
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heck, I am keeping my 8gb 3060 that I used to stream on for RAG purposes
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Local AI hardware: capacity, bandwidth, and software stack
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Local AI hardware = capacity × bandwidth × software stack – Capacity tells you what fits
– Bandwidth tells you how hard the box can breathe
– The software stack tells you how much of the spec sheet you can actually cash out. Hardware by Memory Bandwidth
– Mac Studio M3 Ultra: -
This alone convinces enterprises to host LLMs on-premise
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I mean, look at this, this alone is enough to get every enterprise out there into hosting their LLMs on-premise
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DGX Spark wins on energy, 4x 3090s on performance
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Low energy/heat footprint: DGX Spark wins Performance: 4x 3090s win