Introduction
In an astonishing shift within the artificial intelligence landscape, a new AI startup funded by a Chinese hedge fund has unveiled its groundbreaking open weights model known as Deep Seek R1. This intriguing development raises compelling questions about the future of AI technology, especially in relation to energy consumption and resource allocation.
Unlike OpenAI, which has heavily relied on vast data centers and substantial energy resources, Deep Seek claims to have achieved superior performance metrics with significantly less investment—just $6 million for development.
The Rise of Deep Seek R1
What is Deep Seek R1?
Deep Seek R1 is an open weights model that functions efficiently on limited GPU memory and lower bandwidths compared to OpenAI's offerings.
- Key Features:
- Achieves performance that reportedly exceeds OpenAI’s best models.
- Operates effectively on budget GPUs, enabling local deployment.
- Capable of distilling other models for improved performance on slower hardware.
Objectives of Deep Seek
The creation of Deep Seek R1 challenges norms about AI resource consumption:
- Reducing dependency on high-energy infrastructure.
- Making advanced AI accessible to users with average computing setups (e.g., Raspberry Pi).
Implications for OpenAI
How Deep Seek Disrupts the Market
The emergence of Deep Seek R1 serves as a potential threat to OpenAI's model:
- OpenAI’s success has paralleled the exclusivity of its technology, largely depending on people without the capacity to access energy-intensive resources.
- The breakthrough by Deep Seek suggests that models can be run effectively and economically, potentially democratizing AI access.
The Model Run Challenge
While Deep Seek R1 can technically run on low-powered devices like a Raspberry Pi, the practical performance of its larger model (671b) still requires significant GPU resources:
- Performance Metrics:
- Running Deep Seek R1 on a server with 192 cores yields about 4 tokens per second, which indicates the model’s high computational demands.
- This highlights a dual reality where availability is broadened, yet the best performance home setups still need capable GPUs.
Enhancing Performance with GPUs
The Role of External Graphics Cards
To optimize the performance of AI models like Deep Seek R1:
- Integrating an External GPU:
- An AMD W 7700 graphics card enhances processing speed significantly, resulting in performance jumps between 20 to 50 tokens per second.
- The advantages of using GPU over CPU are clear, showcasing that well-optimized models can deliver high speeds even on less conventional systems.
Making AI Accessible
- Potential Users:
- Hobbyists: Light chatbots for personal projects.
- Developers: Rapid testing of AI models without costly investments.
- With widespread compatibility for various hardware setups, Deep Seek R1 aims to provide an entry point for many eager users.
The AI Market Bubble
Industry Impacts
The launch of Deep Seek has already begun to ripple through the AI industry:
- NVIDIA's Market Response:
- Following the launch, NVIDIA experienced a loss of over half a trillion dollars in market value.
- Nonetheless, their stock remains significantly higher than it was in early 2023, illustrating that despite setbacks, AI is still a lucrative industry.
Reassessing Resource Allocation
- The narrative surrounding AI technology is shifting to address over-consumption of energy and computing power:
- This reevaluation could catalyze new developments aiming at sustainability in AI.
AI technology can be powerful without consuming the bulk of the world’s resources. If Deep Seek R1 succeeds in changing the landscape, it could awaken a more responsible approach to AI development.
Conclusion
The introduction of Deep Seek R1 signifies an important turning point in AI technology, directly challenging established norms held by giants like OpenAI. With its capability to function effectively on less sophisticated hardware and at a fraction of the cost, Deep Seek not only embodies competitive spirit but also highlights the need for more responsible use of resources in AI.
As we navigate this technological shift, it becomes imperative for consumers and developers alike to reconsider the future of computing and AI, aiming not only for innovation but also for sustainability and accessibility. The excitement surrounding this development prompts many to look ahead at the fruits of collaboration and creativity in the emerging AI landscape, always tethered by the essential question—How much of our world's energy do we need to consume to achieve meaningful progress?
open AI which is only really open about consuming all the world's energy just got rattled to its core deep seek a new
AI startup run by a Chinese hedge fund created a new open weights model called R1 that allegedly beats open ai's best
models in most metrics and they did it for $6 million with gpus that run at half the memory bandwidth of open AI
Pony Stark was able to build this in a cave with a bunch of scraps besides the embarrassment of a Chinese start up
beating open AI using 1% of the resources their model can distill other models to make them run better on slower
Hardware meaning this Raspberry Pi can run one of the best local quen AI models even better now open ai's entire Moe is
predicated on people not having access to the insane energy and GPU resources to train and run massive AI models but
that moat disappears if anyone can buy a GPU and run a model that's good enough for free anytime they want but
sensationalist headlines aren't telling you the full story this Raspberry Pi can technically Run Deep seek R1 but it's
not the same thing as deep seek R1 671b which is a 400 GB model that model the one that actually beats chat GPT
still requires a massive amount of GPU compute but the big difference is assuming you have a few 309s you could
run it at home you don't have to pay open AI for the privilege of running one of their fancy models you can just
install a llama download deep seek and play with it to your heart's content and even if you don't have a bunch of gpus
you could technically still run deep seek on any computer with enough RAM like here it's running on my 192 core
ere 1 server it's running deep seek 671b at about 4 tokens per second which isn't crazy fast but this server won't set you
back like a 100,000 bucks either even though it's only using a00 Watts which is honestly pretty amazing a noisy
server like this isn't going to be in everyone's living a Raspberry Pi could be though so let's look at how the
smaller 14b model runs on it it's definitely not going to win any speed records testing a few different prompts
I got about 1.2 tokens per second I mean it runs but if you want a chatbot for like rubber duck debugging or to give
you a few ideas for your next YouTube title this isn't fun but we can speed things up a lot all we need is an
external graphics card because gpus and the vram on them are way faster than CPUs and system m
I have this setup I've been testing with an AMD W 7700 Graphics card it has 16 gigs of Speedy vram and as long as it
can fit the whole AI model in that it should be way faster than any CPU and it is like 10 times faster I can get
between 20 to 50 tokens per second depending on the type of work I'm doing here's the raw output from an
interactive session and if I look at NV toop I can see all this processing is being done on the GPU and if I run llama
bench it's reporting 24 to 54 tokens per second and this GPU isn't even targeted at llms you can go a lot faster if
you're interested in running gpus on Raspberry Pi or maybe even other arm boards well you're in for a treat this
year not only do we have AMD gpus working great the new Intel open source drivers are also working somewhat and
Nvidia might be in the cards too on top of that I have an Orion 06 a cm5 ITX board and even a high five premier p550
all of which have full full size by6 PCI slots so even if the year of the Linux desktop will never come at least we'll
get custom arm and risk 5 PCS AI is still in a massive bubble Nvidia just lost more than half a trillion dollars
in value in one day after deep seek was launched but their stock price is still eight times higher today than it was in
2023 and it's not like anyone's hyping up AI any less now the one good takeaway I think is people might realize we don't
need to devote more than half the world's energy resources or set up a Dyson Sphere around the Sun just to help
computers solve trillions of multiplication problems to spit out another thousand mediocre web apps the
other takeaway is that there's new confusion in AI models over who precisely is Winnie the Pooh until next
Heads up!
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