[HN Gopher] Tongyi DeepResearch - open-source 30B MoE Model that...
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Tongyi DeepResearch - open-source 30B MoE Model that rivals OpenAI
DeepResearch
Author : meander_water
Score : 221 points
Date : 2025-11-02 11:43 UTC (11 hours ago)
(HTM) web link (tongyi-agent.github.io)
(TXT) w3m dump (tongyi-agent.github.io)
| jychang wrote:
| This is over a month old, they released the weights a long time
| ago.
| earthnail wrote:
| And for those not so tightly in the loop: how does it compare?
| jwr wrote:
| That's OK -- not all of us follow all the progress on a daily
| basis, and a model that is a month old doesn't become useless
| just by being a month old!
| embedding-shape wrote:
| 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've used,
| just that they've used a specific feature from ChatGPT?
| simonw wrote:
| 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 a report
| (with "citations") at the end.
|
| This Tongyi model has been fine-tuned to be really good at
| using its search tool in a loop to produce a report.
| embedding-shape wrote:
| 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.
| aliljet wrote:
| 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. :)
| homarp wrote:
| llama.cpp gives you the most control to tune it for your
| machine.
| giobox wrote:
| If you just want to get something running locally as fast as
| possible to play with (the 2080ti typically had 11gb of VRAM
| which will be one of the main limiting factors), the ollama app
| will run most of these models locally with minimum user effort:
|
| https://ollama.com/
|
| If you really do have a 2080ti with 128gb of VRAM, we'd love to
| hear more about how you did it!
| CuriousSkeptic wrote:
| Im sure this guy has some helpful hints on that:
| https://youtube.com/@azisk
| exe34 wrote:
| llama.cpp + quantized:
| https://huggingface.co/bartowski/Alibaba-NLP_Tongyi-DeepRese...
|
| get the biggest one that will fit in your vram.
| davidsainez wrote:
| This is the way. I managed to run (super) tiny models on CPU
| only with this approach.
| trebligdivad wrote:
| How do people deal with all the different quantisations?
| Generally if I see an Unsloth I'm happy to try it locally;
| random other peoples...how do I know what I'm getting?
|
| (If nothing else Tongyi are currently winning AI with cutest
| logo)
| exe34 wrote:
| personally I've only used them for toying around - but in
| all cases you have to test them for your use case anyway.
| btbuildem wrote:
| 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
| pstuart wrote:
| That's basically what I imagined would be my rig if I were to
| pull the trigger. Do you have an NVLink adapter as well?
| btbuildem wrote:
| No NVLink; it took me a long time to compose the exact
| hardware specs, because I wanted to optimize performance.
| Both cards are on x8 PCIe direct CPU channels, close to
| their max throughput anyway. It runs hot with the CPU
| engaged, but it runs fast.
| nine_k wrote:
| Does it offer more performance than a Macbook Pro that could
| be had for a comparable sum? Your build can be had for under
| $3k; a used MBP M3 with 64 GB RAM can be had for
| approximately $3.5k.
| btbuildem wrote:
| I'm not sure, I did not run any benchmarks. As a ballpark
| figure -- with both cards throttled down to 250W, running a
| Qwen-30B FP8 model (variant depending on task), I get
| upwards of 60 tok/sec. It feels on par with the premium
| models, tbh.
|
| Of course this is in a single-user environment, with vLLM
| keeping the model warm.
| PeterStuer wrote:
| Unfortunately the RTX 3090 has no native FP8 support.
| jlokier wrote:
| 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.
| MaxMatti wrote:
| How's the battery holding up during vibe coding sessions or
| occasional LLM usage? I've been thinking about getting a
| MacBook or a laptop with a similar Ryzen chip specifically
| for that reason.
| anon373839 wrote:
| I'd strongly advise ditching Ollama for LM Studio, and using
| MLX versions of the models. They run quite a bit faster on
| Apple Silicon. Also, LM Studio is much more polished and
| feature rich than Ollama.
| terhechte wrote:
| Fully agree to this. LM Studio is much nicer to use and
| with MLX faster on Apple Silicon
| jwr wrote:
| I just use my laptop. A modern MacBook Pro will run ~30B models
| very well. I normally stick to "Max" CPUs (initially for more
| performance cores, recently also for the GPU power) with 64GB
| of RAM. My next update will probably be to 128GB of RAM,
| because 64GB doesn't quite cut it if you want to run large
| Docker containers _and_ LLMs.
| sumo43 wrote:
| Try running this using their harness
| https://huggingface.co/flashresearch/FlashResearch-4B-Thinki...
| 3abiton wrote:
| As many pointed out, Macs are decent enough to run them (with
| maxxed rams). You also have more alternative, like DGX Sparks
| (if you appreciate the ease of cuda, albeit a tad bit slower
| token generation performance), or the Strix Halo (good luck
| with ROCm though, AMD still peddling hype). There is no
| straitghtforwars "cheap" answer. You either go big (gpu
| server), or compromise. Either way use either vllm or sglang,
| or llama.cpp. ollama is just inferior in every way to
| llama.cpp.
| mehdibl wrote:
| It's a Qwen 3 MoE fine tune...
| zurfer wrote:
| 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 tradeoff, as GPT 3.5 was
| awesome at chess and current models don't seem trained
| extensively on chess anymore.
| deepanwadhwa wrote:
| -> 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.
| miki123211 wrote:
| 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).
| alephnerd wrote:
| > 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.
| onlyrealcuzzo wrote:
| Isn't the whole point of the MOE architecture exactly this?
|
| That you can individually train and improve smaller segments as
| necessary
| idiotsecant wrote:
| I think it's the exact opposite - you don't specifically
| train each 'expert' to be a SME at something. Each of the
| experts is a generalist but becomes better at portions of
| tasks in a distributed way. There is no 'best baker', but
| things evolve toward 'best applier of flour', 'best kneader',
| etc. I think explicitly domain-trained experts are pretty
| uncommon in modern schemes.
| viraptor wrote:
| That's not entirely correct. Most of moe right now are
| fully balanced, but there is an idea of a domain expert moe
| where the training benefits fewer switches.
| https://arxiv.org/abs/2410.07490
| ainch wrote:
| Generally you train each expert simultaneously. The benefit
| of MoEs is that you get cheap inference because you only use
| the active expert parameters, which constitute a small
| fraction of the total parameter count. For example Deepseek
| R1 (which is especially sparse) only uses 1/18th of the total
| parameters per-query.
| criemen wrote:
| > or if it takes a couple of months to fold these advantages
| back into the frontier models.
|
| Right now, I believe we're seeing that the big general-purpose
| models outperform approximately everything else. Special-
| purpose models (essentially: fine tunes) of smaller models make
| sense when you want to solve a specific task at lower
| cost/lower latency, and you transfer some/most of the abilities
| in that domain from a bigger model to a smaller one. Usually,
| people don't do that, because it's a quite costly process, and
| the frontier models develop so rapidly, that you're perpetually
| behind them (so in fact, you're not providing the best possible
| abilities).
|
| If/when frontier model development speed slows down, training
| smaller models will make more sense.
| rokob wrote:
| This whole series of work is quite cool. The use of `word-break:
| break-word;` makes this really hard to read though.
| soared wrote:
| I actually can't read it for some reason? My brain just can't
| connect the words
| don-bright wrote:
| so it appears the entire text has been Translated with non-
| breaking space unicode x00a0 instead of normal spaces x0020,
| so the web layout is considering all paragraph text as a
| super-long single word ('the\00a0quick\00a0\brown\00a0fox'
| instead of 'the quick brown fox') - the non-breaking space
| character appears identically to breaking-space when rendered
| but underlying coding breaks the concept of "break at end of
| word" because there is no end as 00a0 literally means "non-
| breaking"). per Copilot spending a half hour explaining this
| to me, apparently this can be fixed by opening web browser
| developer view, and copy/pasting this code into the console.
|
| function replaceInTextNodes(node) { if (node.nodeType ===
| Node.TEXT_NODE) { node.nodeValue = node.nodeValue
| .replace(/\u00A0/g, ' '); } else {
| node.childNodes.forEach(replaceInTextNodes); } }
|
| replaceInTextNodes(document.body);
| dlisboa wrote:
| That's why typography matters. You can't read it because a
| very basic convention has been broken here and that throws
| everything off.
| theflyestpilot wrote:
| I hope the translation for this is actually "Agree" Deep
| research. Just a dig at "You are absolutely right!" sycophancy.
| numpad0 wrote:
| TIL the "full" name of Alibaba Qwen is Tong Yi Qian Wen
| (romanized as "Tongyi Qianwen", something along "knows all
| thousand questions"), of which the first half without the
| Chinese accent flags is romanized identically to "Tong Yi ",
| meaning "same intents" or "agreed".
|
| The Chinese version of the link says "Tong Yi DeepResearch" in
| the title, so doesn't look like the "agree" to be the case.
| Completely agreed that it would be hilarious.
|
| 1: https://www.alibabacloud.com/en/solutions/generative-
| ai/qwen...
| rahimnathwani wrote:
| For people who don't read Chinese: the two 'yi' characters
| numpad0 mentioned (Yi and Yi ) are the same, but written in
| different variants of Chinese script
| (Simplified/Traditional).
| Traubenfuchs wrote:
| 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?
| rokob wrote:
| 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.
| isoprophlex wrote:
| 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.
| nickpinkston wrote:
| 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".
| whiplash451 wrote:
| Isn't the moat in the product/UI/UX? I use Claude daily and
| love the "scratch notebook" feel of it. The barebone model does
| not get you any of this.
| hamandcheese wrote:
| I agree that the scaffolding around the model contributes
| greatly to the experience. But it doesn't take billions of
| dollars in GPUs to do that part.
| Grimblewald wrote:
| Of course they dont, the only advantage it ever had was the
| willingness to destroy trust on the internet by scraping
| everything from everyone rules and expectations be dammed.
|
| The underlying architecture isnt special, the underlying skills
| and tools aren't special.
|
| There is nothing openAI brings to the table other than a
| willingness to lie, cheat, and steal. That only gives you an
| edge for so long.
| red2awn wrote:
| The moat of OpenAI is 1. internal knowledge they've built over
| the last few years building front tier models 2. their talent
| 3. the ChatGPT brand (go ask a random person on the street,
| they know ChatGPT but not Claude or Gemini)
| steveny3456 wrote:
| Juju
| krystofee wrote:
| Isnt it huge deal, that this 30B model can compare and surpass
| huge closed models?
| tbruckner wrote:
| Has anyone found these deep research tools useful? In my
| experience, they generate really bland reports don't go much
| further than summarization of what a search engine would return.
| ainch wrote:
| The reports are definitely bland, but I find them very helpful
| for discovering sources. For example, if I'm trying to ask an
| academic question like "has X been done before," sending
| something to scour the internet and find me examples to dig
| into is really helpful - especially since LLMs have some base
| knowledge which can help with finding the right search terms.
| It's not doing all the thinking, but those kind of broad
| overviews are quite helpful, especially since they can just run
| in the background.
| kmarc wrote:
| I caught myself that most of my LLM usage is like this:
|
| ask a loaded, "filter question" I more or less know the
| answer for, and mostly skip the prose and get to the links to
| its sources.
| andy99 wrote:
| My experience is the same as yours. It feels to me (similar to
| most LLM writing) like they write for someone who's not going
| to read it or use it but is going to glance at it and judge the
| quality that way and assume it's good.
|
| Not to different from a lot of consulting reports, in fact, and
| pretty much of no value if if you're actually trying to learn
| something.
|
| Edit to add: even the name "deep research" to me feels like
| something defined to appeal to people who have never actually
| done or consumed research, sort of like the whole "phd level"
| thing.
| blaesus wrote:
| "Summarization of what a search engine would return" is good
| enough for many of my purposes though. Good for breaking into
| new grounds, finding unknown unknowns, brainstorming etc.
| criemen wrote:
| I tend to use them when I'm looking to buy something of
| category X, and want to get a market overview. I can then still
| dig in and decide whether I consider the sources used
| trustworthy or not, and before committing money, I'll read some
| reviews myself, too. Still, it's a speedup for me.
| DataDaemon wrote:
| Unfortunately soon China will take lead in AI.
| aeve890 wrote:
| Unfortunately? May I ask why? What country would you like to be
| the lead in AI?
| ninetyninenine wrote:
| The USA of course. Isn't it obvious? What other country is
| more Free and great? None. Why does this even need to be
| asked?
|
| China is full of people who want communism to dominate the
| world with totalitarian control so no one wants China to
| dominate anything at all because they are bad...
| Krasnol wrote:
| The USA is being led by a criminal pedo atm. There is
| military in the streets and SA-like, masked thugs are
| kidnapping people. Billionaires sit behind the wheels to
| profit from all those developments. Many of them are
| somehow related to AI. You can image what that will be/is
| used (see Palantir).
|
| The whole country is going down the drain right now. There
| is nothing about it, sane people outside the Republican
| bubble would consider "freedom".
| victorbjorklund wrote:
| USA is threatening to invade Europe so not sure it can be
| considered great.
| davidsainez wrote:
| I have been very impressed with the Qwen3 series. I'm still
| evaluating them, and I generally take LLM benchmarks with a
| huge grain of salt, but their MoE models in particular seem to
| offer a lot of bang for the compute. But what makes you so sure
| they will take the lead?
| ninetyninenine wrote:
| Isn't this an indication they are already in the lead? They
| currently have the best model that beats everyone on all
| quantitative metrics? Are you implying that the US has a better
| model somewhere?
| sumo43 wrote:
| I made a 4B Qwen3 distill of this model (and a synthetic dataset
| created with it) a while back. Both can be found here:
| https://huggingface.co/flashresearch
| Nymbo wrote:
| Just tried this out with my web search mcp, extremely impressed
| with it. Never seen deep research this good from a model so
| small.
| brutus1213 wrote:
| I recently got a 5090 with 64 GB of RAM (intel cpu). Was just
| looking for a strong model I can host locally. If I had
| performance of GPT4-o, I'd be content. Are there any suggestions
| or cases where people got disappointed?
| p1esk wrote:
| 5090 has 32GB of RAM. Not sure if that's enough to fit this
| model.
| svnt wrote:
| It should fit enough of the layers to make it reasonably
| performant.
| IceWreck wrote:
| LlamaCPP supports offloading some experts in a MoE model to
| CPU. The results are very good and even weaker GPUs can run
| larger models at reasonable speeds.
|
| n-cpu-moe in https://github.com/ggml-
| org/llama.cpp/blob/master/tools/serv...
| bogtog wrote:
| GPT-OSS-20B at 4- or 8-bits is probably your best bet?
| Qwen3-30b-a3b probably the next best option. Maybe there exists
| some 1.7 or 2 bit version of GPT-OSS-120B
| yalogin wrote:
| In my experience using these supposed expert models, they are all
| more or less the same given they all are trained on the same
| internet data. The differentiation and value is in the context
| window management and how relevant info from your session is
| pulled in. So it's the interface to the model that makes all the
| difference. Even there the differences are quite minimal. That is
| because all these companies want to toe the line between
| providing functionality to keep the users engaged and pushing
| them to sign up for the subscription.
|
| All this to ask the question, if I host these open source models
| locally, how is the user interface layer that remembers and picks
| the right data from my previous session and the agentic
| automation and others implemented? Do I have to do it myself or
| are the free options for that?
| viksit wrote:
| this is a great question. what are the main use cases that you
| have for this? i've been working on a library for something
| similar and exposing it via an mcp interface. would love to
| pick your brain on this (@viksit on twitter)
| ninetyninenine wrote:
| Is China dominating the US in terms of AI? Given that they
| currently have a model that beats the best models at all formal
| quantitative benchmarks?
|
| What is the state of AI in China? My personal feeling is that it
| doesn't dominate the zeitgeist in China as it does in the US and
| despite this because of the massive amount of intellectual
| capital they have just a small portion of their software
| engineering talent working on this is enough to go head to head
| with us even though it only takes a fraction of their attention.
| idiotsecant wrote:
| I think the lesson of the Chinese catchup in AI is that there
| is a massive disadvantage in being first, in this domain. You
| can do all the hard work and your competitors can distill that
| work out of your model for pennies on the dollar. Why should
| anyone want to do the work?
| MaxPock wrote:
| This sounds like copium . If it was just about
| distillation,we'd be seeing many awesome models from Europe
| ,Japan and even India.
| whiplash451 wrote:
| Has anyone tried running this on a 5090 or 6000 pro? What
| throughput do you see?
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