Dear Zuckerberg, Size Doesn't Matter 💔

Brought to you by Dzambhala Finance.

We are tiny and independent. Submit review and win $200 worth of Perplexity Pro and my gratitude ❤️ 

There are two key requirements for the success of any digital product — ease of access and a user friendly interface and experience.

If this wasn’t the case, Linux-based OS systems would trump MacOS and Windows.

This is also the reason why GPT, Claude and Grok models are triumphing over open-source LLMs like the Llama and Mistral series, even when the latter provide some incredible utility with great customizations.

I think OpenAI and DeepSeek present interesting case-studies here.

DeepSeek is making open-source foundational models but if it didn’t also provide services also directly through its web interface and the iPhone app — there is fat chance it could disrupt the U.S. markets the way it did.

Like DeepSeek was doing before, it would have remained a product that nerdy devs talked about as the “real deal” in their small circles, as was happening in months preceding the mania that surrounded the Chinese company.

OpenAI similarly built open-source models in shadows for roughly seven years between 2015 and 2022 — until it launched ChatGPT. I’m sure you remember what that was like.

To Llama, Or Not To Llama

One constraint in more widespread utility of open-source models is technical skills — that one is more obvious.

But when it comes to LLMs — it’s actually also money. 💸

You see, running open-source models require GPUs. Some small open-source models can run on consumer GPUs like the one I have in my top-of-the-line M3 Max Macbook Pro with 36 GB memory.

But others require dedicated ones.

Meta this weekend dropped the Llama 4 series of models including the Maverick and Scout LLMs and announced plans to release Behemoth at a later date.

There are no updates yet on the reasoning model from the fourth series, except for a a nerdy-looking Llama telling us it is “coming soon.”

Here is the most eyebrow-raising bit about Llama 4: as Meta keeps chasing more and more parameters in its LLMs, the cost to acquire the hardware to run these models gets ridiculous.

It’s a bit early and I haven’t analyzed every information available on developers trying to run the models on different devices but it seems that the minimum requirement to comfortably run the lower-end Scout model is a single Nvidia H100 GPU, which costs roughly $40,000 — provided you can manage to get your hands on one.

If Sam Altman with his hundreds of billions of dollars struggles to find GPUs, so does this poverty-struck startup founder.

Mixture of Experts

Having said that, there is one interesting thing that makes it possible to run the Llama 4 line on Apple products — possibility the Mac Studio with 128 GB memory or above.

That is Mixture of Experts.

Some of the earlier LLMs were actually a single model trained on a whole swaths of data from across domains like GPT-3 or the original Llama. But companies are rapidly switching to a mixture of experts concept.

This means that even though we see Llama 4 Scout as a single model that we are talking to, it is actually deciding between 16 separate trained models on which one will respond to the query, based on whether we asked it a math question or asked it to spark creativity.

This is different from the traditional dense models that operated on single monolithic networks, where all of the parameters of the LLMs were activated for every query. So even if you asked it “what’s 2+2,” it would active all of its knowledge on Socrates and Plato’s philosophies.

Dear Zuck, Size Doesn’t Matter

Setting aside difficulties of running the Llama 4 series, even the ones that have tried it (mostly through Groq/OpenRouter) are less than impressed.

The Llama 4 series isn’t doing great at coding or deep questions — but seems to love emojis (and me ❤️).

So here goes, even as companies keep obsessing over increasing the parameters in training of foundational LLMs, that doesn’t seem to be improving things.

In fact, it may have opened a key business opportunity that we thought of as closed so far. That of training more domain-specific niche models.

As noted by AI researcher Andriy Burkov, If your business idea isn't in the math and coding or factual question-answering domains, there is great opportunity in building your business-specific dataset.

The potential increase in generalist models' skills will no longer be a threat.

So, is now the time we make our own LLM at Dzambhala Finance? Perhaps, but we need enough revenue to sustain a bigger database.

Best From Around The Web

This one from Nathan Lambert is pretty funny and to-the-point! Very relevant given our theme of today’s newsletter.

Llama 4 Maverick is ranking second on LMArena right now but it turns out Meta has listed an entirely different model there than the one that has been launched this weekend!

From Artificially Boosted Family

We are growing at a rapid pace! And Artificially Boosted is upgrading from a simple newsletter to a suit of AI products!

I just moved the web app away from being a static page to a full-fledged Django project!

We would be launching more games, more content, better ability to utilize AI and revamp our builder’s corner, possibly through YouTube videos!

I spent over 6 hours researching and writing this newsletter for our community. If you can spare a minute to help spread the word, or drop us a review, it means the world to me! 💞