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Tongyi DeepResearch – open-source 30B MoE Model that rivals OpenAI DeepResearch

tongyi-agent.github.io

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Re: Tongyi DeepResearch – open-source 30B MoE Model that rivals OpenAI DeepResearch

#21

Post by alephnerd »

zurfer wrote: post #8

It makes me wonder if we'll see an explosion of purpose trained LLMs because we hit diminishing returns on invest with pre training or if it takes a couple of months to fold these advantages back into the frontier models. Given the size of frontier models I would assume that they can incorporate many specializations and the most lasting thing here is the training environment. But there is probably already some tradeo…

> if we'll see an explosion of purpose trained LLMs...

Domain specific models have been on the roadmap for most companies for years now for both competitive (why give up your moat to OpenAI or Anthropic) and financial (why finance OpenAI's margins) perspective.

Re: Tongyi DeepResearch – open-source 30B MoE Model that rivals OpenAI DeepResearch

#23

Post by btbuildem »

aliljet wrote: post #5

Sunday morning, and I find myself wondering how the engineering tinkerer is supposed to best self-host these models? I'd love to load this up on the old 2080ti with 128gb of vram and play, even slowly. I'm curious what the current recommendation on that path looks like. Constraints are the fun part here. I know this isn't the 8x Blackwell Lamborghini, that's the point. :)

I've recently put together a setup that seemed reasonable for my limited budget. Mind you, most of the components were second-hand, open box deals, or deep discount of the moment.

This comfortably fits FP8 quantized 30B models that seem to be "top of the line for hobbyists" grade across the board.

- Ryzen 9 9950X

- MSI MPG X670E Carbon

- 96GB RAM

- 2x RTX 3090 (24GB VRAM each)

- 1600W PSU

Re: Tongyi DeepResearch – open-source 30B MoE Model that rivals OpenAI DeepResearch

#24

Post by rahimnathwani »

numpad0 wrote: post #13
theflyestpilot wrote: post #10

I hope the translation for this is actually "Agree" Deep research. Just a dig at "You are absolutely right!" sycophancy.

TIL the "full" name of Alibaba Qwen is 通義千問(romanized as "Tongyi Qianwen", something along "knows all thousand questions"), of which the first half without the Chinese accent flags is romanized identically to "同意", meaning "same intents" or "agreed". The Chinese version of the link says "通义 DeepResearch" in the title, so doesn't look like the "agree" to be the case. Completely agreed that it would be hilarious. 1: ht…

For people who don't read Chinese: the two 'yi' characters numpad0 mentioned (义 and 義) are the same, but written in different variants of Chinese script (Simplified/Traditional).

Re: Tongyi DeepResearch – open-source 30B MoE Model that rivals OpenAI DeepResearch

#25

Post by rokob »

Traubenfuchs wrote: post #19

It still feels to me like OpenAI has zero moat. There are like 5 paid competitors + open source models. I switch between gemini and ChatGpt whenever I feel one fails to fully grasp what I want, I do coding in claude. How are they supposed to become the 1 trillion dollar company they want to be, with strong competition and open source disruptions every few months?

I don’t know if they can pull it off but a lot of companies are built on strong enterprise sales being able to sell free stuff with a bow on it to someone who doesn’t know better or doesn’t care.

Re: Tongyi DeepResearch – open-source 30B MoE Model that rivals OpenAI DeepResearch

#26

Post by isoprophlex »

Traubenfuchs wrote: post #19

It still feels to me like OpenAI has zero moat. There are like 5 paid competitors + open source models. I switch between gemini and ChatGpt whenever I feel one fails to fully grasp what I want, I do coding in claude. How are they supposed to become the 1 trillion dollar company they want to be, with strong competition and open source disruptions every few months?

Premium grade deals with Oracle. They will bullshit their way into government and enterprise environments where all the key decision makers are clueless and/or easily manipulated.

Re: Tongyi DeepResearch – open-source 30B MoE Model that rivals OpenAI DeepResearch

#27

Post by miki123211 »

deepanwadhwa wrote: post #18
zurfer wrote: post #8

It makes me wonder if we'll see an explosion of purpose trained LLMs because we hit diminishing returns on invest with pre training or if it takes a couple of months to fold these advantages back into the frontier models. Given the size of frontier models I would assume that they can incorporate many specializations and the most lasting thing here is the training environment. But there is probably already some tradeo…

-> GPT 3.5 was awesome at chess I don't agree with this. I did try to play chess with GPT3.5 and it was horrible. Full of hallucinations.

It was GPT-3 I think.

As far as I remember, it's post-training that kills chess ability for some reason (GPT-3 wasn't post-trained).

Re: Tongyi DeepResearch – open-source 30B MoE Model that rivals OpenAI DeepResearch

#28

Post by jlokier »

aliljet wrote: post #5

Sunday morning, and I find myself wondering how the engineering tinkerer is supposed to best self-host these models? I'd love to load this up on the old 2080ti with 128gb of vram and play, even slowly. I'm curious what the current recommendation on that path looks like. Constraints are the fun part here. I know this isn't the 8x Blackwell Lamborghini, that's the point. :)

I use a Macbook Pro with 128GB RAM "unified memory" that's available to both CPU and GPU.

It's slower than a rented Nvidia GPU, but usable for all the models I've tried (even gpt-oss-120b), and works well in a coffee shop on battery and with no internet connection.

I use Ollama to run the models, so can't run the latest until they are ported to the Ollama library. But I don't have much time for tinkering anyway, so I don't mind the publishing delay.

Re: Tongyi DeepResearch – open-source 30B MoE Model that rivals OpenAI DeepResearch

#29

Post by embedding-shape »

simonw wrote: post #17
embedding-shape wrote: post #3

Isn't OpenIA "Deep research" (not "DeepResearch") a methodology/tooling thing, and you'll get different responses depending on what specific model you use with it? As far as the UI allows you to, you could use Deep research with GPT-5, GPT-4o, o3 and so on, and that'll have an impact on the responses. Skimming the paper and searching for some simple terms makes it seem like they never expand on what exact models they…

At this point "deep research" is more of a pattern - OpenAI and Perplexity and Google Gemini all offer products with that name which work essentially the same way, and Anthropic and Grok have similar products with a slightly different name attached. The pattern is effectively long-running research tasks that drive a search tool. You give them a prompt, they churn away for 5-10 minutes running searches and they output…

Yes, but I think my previous point still matter, namely what exact model is being used greatly affects the results.

So without specifying which model is being used, it's really hard to know what is better than something else, because we don't understand what the underlying model is, and if it's better because of the model itself, or the tooling, which feels like an important distinction.

Re: Tongyi DeepResearch – open-source 30B MoE Model that rivals OpenAI DeepResearch

#30

Post by nickpinkston »

Traubenfuchs wrote: post #19

It still feels to me like OpenAI has zero moat. There are like 5 paid competitors + open source models. I switch between gemini and ChatGpt whenever I feel one fails to fully grasp what I want, I do coding in claude. How are they supposed to become the 1 trillion dollar company they want to be, with strong competition and open source disruptions every few months?

Yea, I agree.

Arguably LLMs are both (1) far easier to switch between models than it is today to switch from AWS / GCP / Azure systems, and (2) will be rapidly decreasing switching costs for your legacy systems to port to new ones - ie Oracle's, etc. whole business model.

Meanwhile, the whole world is building more chip fabs, data centers, AI software/hardware architectures, etc.

Feels more like we're headed to commodification of the compute layer more than a few giant AI monopolies.

And if true, that's actually even more exciting for our industry and "letting 100 flowers bloom".

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