Yesterday Anthropic released Claude Sonnet 3.7; Dylan Patel had the joke of the day about Anthropic’s seeming aversion to the number “4”, which means “die” in Chinese.

Jokes aside, the correction on this post by Ethan Mollick suggests that Anthropic did not increment the main version number because Sonnet 3.7 is still in the GPT-4 class of models as far as compute is concerned. I love Mollick’s work, but reject his neutral naming scheme: whoever gets to a generation first deserves the honor of the name. In other words, if Gen2 models are GPT-4 class, then Gen3 models are Grok 3 class.

And, whereas Sonnet 3.7 is an evolution of Sonnet 3.5’s fascinating mixture of personality and coding prowess, likely a result of some Anthropic special sauce in post-training, Grok 3 feels like a model that is the result of a step-order increase in compute capacity, with a much lighter layer of reinforcement learning with human feedback (RLHF). Its answers are far more in-depth and detailed (model good!), but frequently becomes too verbose (RLHF lacking); it gets math problems right (model good!), but its explanations are harder to follow (RLHF lacking). It is also much more willing to generate forbidden content, from erotica to bomb recipes, while having on the surface the political sensibilities of Tumblr, with something more akin to 4chan under the surface if you prod.  Grok 3, more than any model yet, feels like the distilled Internet; it’s my favorite so far. — Read More

NVIDIA Senior Research Manager Jim Fan recently shared his in-depth evaluation of DeepSeek R1 on social media. As the co-founder of GEAR Lab, lead of Project GR00T, Stanford Ph.D., and OpenAI’s first intern, Fan’s perspectives carry significant weight in the industry. He particularly emphasized DeepSeek’s outstanding contributions to AI open-source development as a non-US company.

In his commentary, Fan noted: “We are living in a timeline where a non-US company is keeping the original mission of OpenAI alive – truly open, frontier research that empowers all. It makes no sense. The most entertaining outcome is the most likely.” He particularly appreciated that DeepSeek not only open-sources a barrage of models but also spills all the training secrets. — Read More

Fan’s Post

#nvidia

When you own the platform, you own the experience. That’s why Apple invests so much in the iPhone. That’s what NVIDIA is aiming for with Project DIGITS, unveiled at CES 2025.

Project DIGITS democratizes access to advanced AI computing by introducing a compact and powerful personal AI supercomputer. It’s designed to make it possible for AI researchers, data scientists, students, and even hobbyists to develop, prototype, and fine-tune AI models directly from their desks. While professionals could fine-tune models locally before, they were often constrained by hardware limitations, high costs, or scalability issues. Project DIGITS eliminates these barriers by delivering computing power in a desktop form factor.

As Jensen Huang, founder and CEO of NVIDIA, said in a press release, “AI will be mainstream in every application for every industry. With Project DIGITS, the Grace Blackwell Superchip comes to millions of developers. Placing an AI supercomputer on the desks of every data scientist, AI researcher and student empowers them to engage and shape the age of AI.”

Project DIGITS is also a precursor for how personal computing could fuel the uptake of AI into consumers’ everyday lives in a way that VR devices cannot seem to do – perhaps not today, but sooner than we know. — Read More

In 1980 IBM, under pressure from its customers to provide computers for personal use, not just mainframes, set out to create the IBM PC; given the project’s low internal priority but high external demand they decided to outsource two critical components: Microsoft would provide the DOS operating system, which would run on the Intel 8088 processor.

Those two deals would shape the computing industry for the following 27 years. Given that the point of the personal computer was to run applications, the operating system that provided the APIs for those applications would have unassailable lock-in, leading to Microsoft’s dominance with first DOS and then Windows, which was backwards compatible.

… It follows, then, that if the U.S. wants to make Intel viable, it ideally will not just give out money, but also a point of integration. Given this, if the U.S. is serious about AGI, then the true Manhattan Project — doing something that will be very expensive and not necessarily economically rational — is filling in the middle of the sandwich. Saving Intel, in other words. — Read More

The MLCommons consortium on Wednesday posted MLPerf Inference v4.1 benchmark results for popular AI inferencing accelerators available in the market, across brands that include NVIDIA, AMD, and Intel. AMD’s Instinct MI300X accelerators emerged competitive to NVIDIA’s “Hopper” H100 series AI GPUs. AMD also used the opportunity to showcase the kind of AI inferencing performance uplifts customers can expect from its next-generation EPYC “Turin” server processors powering these MI300X machines. “Turin” features “Zen 5” CPU cores, sporting a 512-bit FPU datapath, and improved performance in AI-relevant 512-bit SIMD instruction-sets, such as AVX-512, and VNNI. The MI300X, on the other hand, banks on the strengths of its memory sub-system, FP8 data format support, and efficient KV cache management. — Read More

#nvidia

Engineering researchers at the University of Minnesota Twin Cities have demonstrated a state-of-the-art hardware device that could reduce energy consumption for artificial intelligent (AI) computing applications by a factor of at least 1,000.

The research is published in npj Unconventional Computing, a peer-reviewed scientific journal published by Nature.

… The CRAM architecture enables the true computation in and by memory and breaks down the wall between the computation and memory as the bottleneck in traditional von Neumann architecture, a theoretical design for a stored program computer that serves as the basis for almost all modern computers.

  — Read More

The Paper

Google is stepping up its competition with Nvidia in the artificial intelligence (AI) chip market by developing custom hardware solutions.

Google has unveiled a new lineup of custom chips designed to bolster its position in the rapidly evolving artificial intelligence (AI) market. The tech giant introduced the Tensor Processing Units (TPUs) and an Arm-based central processing unit (CPU) named Axion, showcasing its commitment to innovate in AI hardware.

While the TPUs offer a competitive alternative to Nvidia’s AI chips, they are exclusively accessible through Google Cloud and unavailable for direct purchase.  — Read More