FOD#34: It's All About Inference

Happy festive week to everyone enjoying a few days off.

We thought this week would be calm, but thought-provoking articles keep coming, contemplating bigger topics that will influence our AI conversations in 2024.

Our minds today are on Inference, thanks to a few interesting newsletters and papers published last week:

Inference as the Competitive Arena (SemiAnalysis's View):

• SemiAnalysis suggests that as the pre-training of models like GPT-3.5 becomes commoditized, the real competitive battleground shifts to inference - the ability to efficiently and effectively apply these models in practical settings. The focus is on how companies can differentiate themselves not just through the models they offer, but through their ability to deliver these models more efficiently and effectively to end-users. This includes considerations of cost, speed, unique distribution channels, and the capacity to fine-tune models to specific needs.

Inference and Market Dynamics (The Exponential View's Response):

• The Exponential View acknowledges the plummeting costs of inference and the resulting market dynamics. It aligns with the notion that inference efficiency is crucial but adds that quality maintenance, despite reduced costs, is vital. The article discusses how the race to lower inference costs is driving market expansion and necessitates strategic shifts focusing on technological advancements and differentiation. It implies that the companies that can master the art of efficient, high-quality inference will lead the future market.

Inference Efficiency on Limited Resources (Apple’s Paper: LLM in a flash: Efficient Large Language Model Inference with Limited Memory):

• The paper by Apple’s researchers directly addresses one of the most significant technical challenges in the inference battle: running large, sophisticated models within the memory and computational constraints of typical devices. By proposing methods to efficiently run LLMs that exceed available DRAM capacity and optimize data transfer from flash memory, this research is a game-changer. It allows for more efficient use of resources, which is crucial for inference services, especially when serving models at scale and in diverse environments.

Inference in Architectural Innovation (Nathan Lambert's Deep Dive):

• Nathan Lambert's article adds a new dimension to the discourse on LLM inference. It discusses state-space models and non-attention architectures, such as Mamba and StripedHyena, challenging the dominance of attention-based mechanisms in language modeling. This innovation aligns with the themes highlighted by SemiAnalysis and The Exponential View about the evolving landscape of AI, focusing on inference efficiency and market dynamics. Lambert's insights suggest a potential shift in LLM architectures, emphasizing the need for continuous innovation in model design to improve long-context performance and computational efficiency, thereby redefining efficient and effective LLM inference."

These perspectives collectively demonstrate that the emphasis has shifted from creating powerful models to effectively deploying them in practical scenarios. Application layers and real-world impact are taking center stage! Efficiency, speed, and cost-effectiveness are no longer secondary considerations but have become paramount.

• For AI companies and tech developers, this means prioritizing strategies that enhance inference performance, including hardware optimizations, innovative memory management techniques, and cost-effective distribution methods.

• For end-users and businesses, it translates into more accessible, faster, and more reliable AI services that can drive better decision-making, automation, and user experiences.

• For the broader tech landscape, it signifies a shift towards more sustainable and scalable AI, where advancements aren't just about what AI can do in theory but what it can practically deliver in everyday applications.

If your kids are looking over your shoulder, asking ‘What is Inference?!’, you can show them that:

And for you, here is an LLM Inference part from Andrej Karpathy:

Twitter Library

Research papers, categorized for your convenience

Long-Awaited Gemini

• Gemini: A Family of Highly Capable Multimodal Models

  • Gemini, developed by Google, is a family of multimodal models capable of processing and understanding text, images, audio, and video. It includes three variants: Ultra, Pro, and Nano, each designed for specific applications →read the paper

• A Challenger to GPT-4V? Early Explorations of Gemini in Visual Expertise

  • This paper evaluates Gemini, Google's new Multi-modal Large Language Model (MLLM), in visual understanding across domains, comparing it to GPT-4V and highlighting challenges and potential →read the paper

Multimodal Models and AI Capabilities

• Rich Human Feedback for Text-to-Image Generation

  • This paper proposes improving text-to-image models by integrating detailed human feedback, enhancing image generation by identifying flaws and guiding model fine-tuning →read the paper

• Generative Multimodal Models are In-Context Learners

  • Description: This paper introduces Emu2, a generative multimodal model with 37 billion parameters, capable of handling diverse vision-language tasks →read the paper

Large Language Models (LLMs) and their Enhancements

• Retrieval-Augmented Generation (RAG) for LLMs: A Survey

  • The paper asserts that RAG effectively combines the parameterized knowledge of LLMs with non-parameterized external knowledge, making it a critical method for implementing large language models →read the paper

• Silkie: Preference Distillation For Large Visual Language Models

  • This preprint explores preference distillation in large vision-language models, introducing a Vision-Language Feedback (VLFeedback) dataset and demonstrating significant improvements in perception and cognition tasks →read the paper

• Mini-GPTs: Efficient LLMs through Contextual Pruning

  • This paper introduces Mini-GPTs, smaller yet efficient versions of LLMs developed through contextual pruning, offering promise for domain-specific applications →read the paper

• PowerInfer: Fast LLM Serving with a Consumer-grade GPU

  • "PowerInfer" introduces an efficient LLM inference engine for personal computers with consumer-grade GPUs, advancing efficient LLM serving on consumer-grade hardware →read the paper

Category: Datasets and Benchmarking

• M3DBench: Let’s Instruct Large Models with Multi-modal 3D Prompts

  • This paper introduces M3DBench, a dataset facilitating multi-modal 3D instruction-following for machine learning models, enhancing 3D understanding and reasoning capabilities →read the paper

• Catwalk: A Unified Language Model Evaluation Framework for Many Datasets

  • "Catwalk" provides a unified interface for evaluating numerous NLP datasets and models, simplifying large-scale experiments →read the paper

AI Applications and Practical Solutions

• AppAgent: Multimodal Agents as Smartphone Users

  • This paper introduces AppAgent, a multimodal agent framework designed to operate smartphone apps by mimicking human interactions, demonstrating versatility in handling various functions and interfaces →read the paper

• Cached Transformers: Improving Transformers with Differentiable Memory Cache

  • The paper introduces Cached Transformer, a new Transformer model incorporating Gated Recurrent Cached (GRC) attention, enhancing performance in various tasks →read the paper

Innovative Approaches and Model Insights

• Zero-Shot Metric Depth with a Field-of-View Conditioned Diffusion Model

  • The paper introduces a diffusion model for zero-shot metric depth estimation in monocular images, addressing challenges in joint indoor-outdoor scene modeling and depth-scale ambiguity →read the paper

• Time is Encoded in the Weights of Fine-tuned Language Models

  • This study introduces "time vectors," a method to adapt language models (LMs) to new time periods, effectively customizing LMs to new time periods →read the paper

• Adversarial Attacks on GPT-4 via Simple Random Search

  • This paper explores an adversarial attack on OpenAI's GPT-4 model using simple random search, revealing potential vulnerabilities in large language models →read the paper

Thank you for reading, please feel free to share with your friends and colleagues. In the next couple of weeks, we are announcing our referral program 🤍

Another week with fascinating innovations! We call this overview “Froth on the Daydream" - or simply, FOD. It’s a reference to the surrealistic and experimental novel by Boris Vian – after all, AI is experimental and feels quite surrealistic, and a lot of writing on this topic is just a froth on the daydream.