From: redpointai
AI model development and deployment face various hurdles, particularly in efficiency, cost, and architectural innovation. Mistral, a leading open-source LLM developer, is at the forefront of addressing these challenges [00:04:05].
Efficiency and Performance Gaps
Mistral aims to close the gap in performance and usability between open-source and closed-source AI offerings [00:04:05]. While active work is underway to reduce the performance gap [00:04:07], a usability gap also exists, with closed-source offerings often having better software surroundings and functioning APIs [00:04:10]. Mistral is actively working to close this usability gap [00:04:21].
Future of LLMs and Technical Hurdles
The development of large language models (LLMs) still presents several future areas of development:
- Efficiency Frontier There remains an “efficiency Frontier” to be pushed [00:10:25].
- Model Controllability The question of making models controllable has not been fully solved [00:10:44].
- Architectural Improvements Current architectures, such as plain Transformers, spend the same amount of compute on every token, suggesting room for more efficient designs [00:11:09].
- Deployment and Latency Challenges include deploying models on smaller devices and improving latency to make models “think faster,” which would open up new application areas [00:11:27].
Compute and Resource Constraints
The economics and resource costs of AI model scaling present significant challenges for companies, especially startups:
- GPU Availability While large entities like Meta possess vast GPU resources (e.g., 600,000 GPUs) [00:11:51], Mistral, with 1.5k H100s [00:12:22], focuses on efficiency and a high concentration of GPUs per person to foster creative training methods [00:12:08].
- High Costs Acquiring substantial GPU clusters, such as 350,000 H100s, is prohibitively expensive for a startup [00:12:40].
- Unit Economics Ensuring that the dollar spent on compute and training accrues to more than a dollar in revenue is crucial for a viable business model [00:13:02].
- Staying Relevant The primary challenge for model providers is to secure enough compute to remain competitive and relevant [00:13:26].
- Saturation and Data Limits There are unknowns regarding when model performance saturates and how to prevent saturation if data resources become limited [00:13:45].
- Hardware Advancements While new chips like Nvidia’s GB200 offer improvements in dollar per flops (e.g., 30% improvement), they are expensive and primarily geared towards the training side [00:16:37].
Architectural Inertia
The widespread adoption and co-adaptation of systems to Transformers create a challenge for adopting new architectures:
- Co-adaptation Transformers have been dominant for seven years, leading to co-adaptation in training methods, optimization algorithms, debugging processes, and even hardware [00:14:42].
- High Bar for New Architectures The “ladder climbing” achieved with Transformers has set a very high bar, making non-incremental architectural changes very challenging [00:15:07].
- Incremental Improvements While radical new architectures face high hurdles, improvements in areas like sparse attention for memory efficiency are possible [00:15:37].
Data Strategies for Enterprises
Enterprises face challenges in effectively using their vast data resources for fine-tuning models:
- Retrieval Augmentation First For companies with a ton of data, the initial approach should be retrieval augmentation generation (RAG) and empowering assistants with tools and data access, rather than immediate fine-tuning [00:29:08].
- Demonstration Data Fine-tuning is most effective with “demonstration data,” which involves traces of user interactions to enable the model to imitate behavior [00:29:30].
- New Data Acquisition Many enterprises lack this specific type of demonstration data, requiring them to acquire a “brand new kind of data” [00:29:54]. This creates a more even playing field for companies to start acquiring it, but necessitates a rethinking of their data strategy [00:29:58].