Stories with Tag Affordable AI

• GPT 4.5 level for 1% of the price

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  Posted: 2025-03-16 10:23:46

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  Baidu claims their new Ernie 3.5 Titan model achieves performance comparable to GPT-4 at significantly lower cost. This enhanced model boasts improvements in training efficiency and inference speed, alongside upgrades to its comprehension, generation, and reasoning abilities. These advancements allow for more efficient and cost-effective deployment for various applications.

  The Twitter post from Baidu, titled "GPT 4.5 level for 1% of the price," announces a significant development in the field of large language models (LLMs). Baidu asserts that their newly developed artificial intelligence model, ERNIE 3.5 Titan, has achieved performance comparable to the highly advanced GPT 4.5, while simultaneously boasting a dramatically reduced cost of operation. This cost reduction, quantified as a staggering 99% decrease compared to GPT 4.5, represents a potential paradigm shift in the accessibility and affordability of cutting-edge AI technology. Baidu posits that this breakthrough will democratize access to powerful language models, opening up a plethora of opportunities for businesses and researchers who were previously priced out of utilizing such advanced capabilities. The implication is that ERNIE 3.5 Titan offers substantially similar performance to OpenAI's GPT 4.5 at a fraction of the financial investment, potentially disrupting the current landscape of LLM deployment and research. This announcement highlights Baidu's commitment to advancing the field of AI and making sophisticated language models more readily available to a wider audience.

  • GPT-4.5
  • Large Language Model
  • LLM
  • AI
  • artificial intelligence
  • Pricing
  • Cost Reduction
  • Affordable AI
  • Baidu

  Summary of Comments ( 152 )
  https://news.ycombinator.com/item?id=43377962

  Verbose tl;dr

  HN users discuss the claim of GPT 4.5 level performance at significantly reduced cost. Some express skepticism, citing potential differences in context windows, training data quality, and reasoning abilities not reflected in simple benchmarks. Others point out the rapid pace of open-source development, suggesting similar capabilities might become even cheaper soon. Several commenters eagerly anticipate trying the new model, while others raise concerns about the lack of transparency regarding training data and potential biases. The feasibility of running such a model locally also generates discussion, with some highlighting hardware requirements as a potential barrier. There's a general feeling of cautious optimism, tempered by a desire for more concrete evidence of the claimed performance.

  The Hacker News post titled "GPT 4.5 level for 1% of the price" links to a 2012 tweet from Baidu announcing their Deep Neural Network processing speech with dramatically improved accuracy. The discussion in the comments focuses on the cyclical nature of hype around AI and the difficulty of predicting long-term progress.

  Several commenters express skepticism about comparing a 2012 advancement in speech recognition to the capabilities of large language models like GPT-4.5. They point out that these are distinct areas of AI research and that directly comparing them based on cost is misleading.

  One commenter highlights the frequent pattern of inflated expectations followed by disillusionment in AI, referencing Gartner's hype cycle. They suggest that while impressive at the time, the 2012 Baidu announcement represents a specific incremental step rather than a fundamental breakthrough comparable to more recent advancements in LLMs.

  Another commenter recalls the atmosphere of excitement around deep learning in the early 2010s, contrasting it with the then-dominant approaches to speech recognition. They suggest that the tweet, viewed in its historical context, captures a moment of genuine progress, even if the long-term implications were difficult to foresee.

  A few comments delve into the specifics of Baidu's work at the time, discussing the use of deep neural networks for acoustic modeling in speech recognition. They acknowledge the significance of this approach, which paved the way for subsequent advancements in the field.

  Overall, the comments reflect a cautious perspective on comparing advancements across different AI subfields and different time periods. While acknowledging the historical significance of Baidu's 2012 achievement in speech recognition, they emphasize the distinct nature of current large language model advancements and caution against drawing simplistic cost comparisons. The discussion highlights the cyclical nature of AI hype and the challenges in predicting long-term technological progress.

• Train Your Own O1 Preview Model Within $450

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  Posted: 2025-02-21 08:42:38

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  This post details how to train a large language model (LLM) comparable to OpenAI's GPT-3 175B parameter model, nicknamed "O1," for under $450. Leveraging SkyPilot, a framework for simplified and cost-effective distributed computing, the process utilizes spot instances across multiple cloud providers to minimize expenses. The guide outlines the steps to prepare the training data, set up the distributed training environment using SkyPilot's managed spot feature, and efficiently train the model with optimized configurations. The resulting model, trained on the Pile dataset, achieves impressive performance at a fraction of the cost typically associated with such large-scale training. The post aims to democratize access to large language model training, enabling researchers and developers with limited resources to experiment and innovate in the field.

  This blog post, titled "Train Your Own O1 Preview Model Within $450," details a cost-effective method for training a large language model (LLM) comparable in performance to Google's Gemini 1.0 "preview" model, specifically on tasks related to mathematical reasoning and code generation. The authors, affiliated with UC Berkeley's Sky Computing Lab, leverage a combination of innovative techniques and readily available cloud resources to achieve this remarkable feat.

  Their methodology centers around fine-tuning a pre-trained LLaMA-2 70B parameter model using a meticulously curated dataset designed to enhance its capabilities in the aforementioned domains. This dataset comprises a diverse mix of high-quality data sources, including GSM8K (for mathematical problem-solving), MATH (another dataset focusing on mathematical reasoning), and HumanEval (for code generation and evaluation). The authors emphasize the importance of data quality and diversity in achieving optimal results, highlighting their careful selection process.

  The training process itself is optimized for both performance and cost-efficiency. They utilize SkyPilot, a framework developed by the same research group, to manage the distributed training across multiple cloud instances. SkyPilot automates and optimizes various aspects of the training pipeline, such as resource allocation, task scheduling, and fault tolerance. This automation simplifies the complex process of distributed training and significantly reduces the engineering overhead required. Furthermore, SkyPilot's cost-aware scheduling capabilities exploit spot instances and other cost-saving measures offered by cloud providers, contributing significantly to the overall affordability of the training process.

  The authors meticulously document their experimental setup, including the specific hardware configuration, training hyperparameters, and evaluation metrics employed. They present compelling empirical results demonstrating the performance of their fine-tuned model, showcasing its competitive performance against the Gemini 1.0 preview model on benchmark datasets. They also provide a detailed breakdown of the training costs, emphasizing the accessibility of this approach for researchers and developers with limited resources. The blog post concludes by highlighting the potential implications of their work and encouraging further exploration in the domain of cost-effective LLM training. The authors suggest their methods could democratize access to powerful LLMs, enabling broader participation and innovation in the field of artificial intelligence. They also offer access to their code and data through provided GitHub links, facilitating reproducibility and further research building upon their work.

  • Large Language Models
  • LLMs
  • O1 Preview Model
  • Model Training
  • Affordable AI
  • Cloud Computing
  • Sky Computing
  • Distributed Computing
  • Cost Optimization
  • AI Infrastructure
  • machine learning
  • deep learning
  • AI Training Costs
  • Budget AI

  Summary of Comments ( 52 )
  https://news.ycombinator.com/item?id=43125430

  Verbose tl;dr

  HN users generally express excitement about the accessibility and cost-effectiveness of training large language models offered by SkyPilot. Several commenters highlight the potential democratizing effect this has on AI research and development, allowing smaller teams and individuals to experiment with LLMs. Some discuss the implications for cloud computing costs, comparing SkyPilot favorably to other cloud providers. A few raise questions about the reproducibility of the claimed results and the long-term viability of relying on spot instances. Others delve into technical details, like the choice of hardware and the use of pre-trained models as starting points. Overall, the sentiment is positive, with many seeing SkyPilot as a valuable tool for the AI community.

  The Hacker News post titled "Train Your Own O1 Preview Model Within $450" generated a moderate amount of discussion, with a focus on the cost and accessibility of training large language models (LLMs). Several commenters expressed skepticism about the claimed $450 figure, pointing out that it likely doesn't include crucial costs like data acquisition and ongoing maintenance/inference. There was a general sentiment that while the decreasing cost of training is exciting, it's still not truly within reach of hobbyists or small-scale researchers.

  One commenter argued that the true cost is significantly higher when factoring in data preparation, experimentation, and the expertise required to manage the process. They highlighted the hidden costs associated with trial and error, especially when dealing with complex models. Another user concurred, emphasizing that the compute cost is only a fraction of the total expenditure, with engineering time representing a significant portion.

  The conversation also touched on the challenges of evaluating these models. One commenter questioned the efficacy of using standard benchmarks, suggesting they may not adequately capture the nuances and real-world performance of LLMs. Another pointed out the inherent difficulty in comparing different models trained on varying datasets, making a true apples-to-apples comparison challenging.

  Some commenters discussed the implications of this increased accessibility. One user raised concerns about potential misuse, specifically the possibility of generating harmful or misleading content. Others expressed excitement about the potential for smaller companies and research groups to experiment with and contribute to the field of LLMs.

  A few users also discussed technical aspects, like the choice of hardware and the specific optimization techniques used in the Sky project. One commenter questioned the use of A100 GPUs, suggesting that newer, more cost-effective options might be available.

  Overall, the comments reflect a cautious optimism about the progress being made in democratizing access to LLM training. While acknowledging the decreasing cost, the discussion highlights the remaining challenges, including hidden costs, evaluation complexities, and potential ethical concerns. The commenters generally agreed that while the $450 figure might be technically achievable for the specific scenario outlined, it doesn't represent the full picture for most individuals or small teams looking to train their own LLMs.