SemiAnalysis’ Post

There's a lot of nontechnical discussion about Generative AI scaling - let's dig into something a bit more solid:

OpenAI o1's New Paradigm: Test-Time Compute Explained What is Test-Time Compute (TTC)? Test-Time Compute refers to the practice of utilizing additional computational resources during the inference phase of machine learning models. Unlike traditional approaches that rely solely on a pre-trained model's fixed architecture and parameters, TTC allows for dynamic adjustments and computations to refine predictions. Key Principles of TTC Dynamic Resource Allocation: Instead of a one-size-fits-all approach, TTC enables models to allocate more computational power to complex or uncertain inputs. This can involve running multiple forward passes or using more sophisticated algorithms to explore the solution space. Iterative Refinement: TTC often employs iterative methods where the model can improve its predictions over several steps. This is similar to how humans might reconsider their answers based on feedback or further thought. Exploration vs. Exploitation: TTC balances the exploration of various potential solutions with the exploitation of known good solutions, allowing for a more robust decision-making process during inference. Methodologies in TTC Several techniques are commonly used in TTC to enhance performance: 1. Best-of-N Sampling In this method, the model generates multiple predictions for a given input and selects the best one based on a predefined criterion (e.g., highest confidence score). This approach helps in capturing diverse outputs and choosing the most reliable one. 2. Beam Search Beam search is a heuristic search algorithm that explores multiple possible sequences of outputs simultaneously. By keeping track of the top N sequences at each step, it ensures that only the most promising paths are pursued, thus improving overall output quality. 3. Monte Carlo Tree Search (MCTS) MCTS is particularly useful in decision-making tasks where outcomes are uncertain. It builds a search tree based on random sampling of possible actions and outcomes, allowing the model to evaluate potential future states before making a decision. 4. Adaptive Distribution Updates In scenarios where models can learn from their mistakes during inference (online learning), adaptive distribution updates allow the model to adjust its output distribution based on feedback received from previous predictions. https://lnkd.in/dKzfduCF

#VectorDB are getting a lot of attention with all the interest in #GenAI, Large Language Models (#LLM) and Retrieval Augmented Generation (#RAG). Karthikeyan Nagalingam and Rodrigo Nascimento have published a “Reference Architecture – #VectorDatabase solution with NetApp” which serves as a comprehensive guide for customers who are currently using or planning to use vector databases, detailing the best practices on platforms such as #ONTAP, #StorageGRID, AWS #FSxN, and protecting it using #SnapCenter. https://lnkd.in/eBHKtbr5

Use Google Managed Prometheus and Triton Inference Server on GKE to Simplify #LLM observability and monitoring. Large Language Models (LLMs) are revolutionizing various industries, from chatbots to content creation. However, their deployment and maintenance demand careful observability information including monitoring metrics and tracing. You need insights into critical metrics such as CPU/GPU usage, latency, throughput, error rates, and resource utilization to ensure optimal performance and catch potential issues early. Traditional monitoring setups can be complex, especially in a Kubernetes environment. Popular tools like self managed local Prometheus offer powerful metrics collection but add operational overhead and lack of persistence as well as centralized views. On the other hand, inference servers like Huggingface TGI are essential to offer entry level model serving but may miss same level of performance metrics comparing to native solutions from Nvidia and Triton Servers. https://lnkd.in/eRYSYmJX

Say goodbye to memory limitations in vector indexing! 📢 KDB.AI introduces qHNSW, a revolutionary vector index that combines the power of HNSW with massive on-disk storage. Here's what makes qHNSW a game-changer: 👉🏾 Massive Scalability: Create as many indexes as your disk space allows! 👉🏾 Reduced Memory Footprint: Save precious memory for other tasks. 👉🏾 Cost-Effective: Utilize less expensive disk storage. 👉🏾 Fast Search Speeds: Optimized memory mapping keeps searches speedy. Perfect for: ✅ Large-scale applications. ✅ Memory-constrained environments. ✅ Cost-conscious projects with high-performance needs. Break free from memory limitations and scale your vector indexing to new heights! #vectorindexing #qHNSW #KDB #AI #machinelearning #scalability #performance #efficiency

The most impactful benefit of vector quantization is increased scalability and cost savings through reduced computing resources and efficient processing of vectors. And when combined with Search Nodes—MongoDB’s dedicated infrastructure for independent scalability through workload isolation and memory-optimized infrastructure for semantic search and generative AI workloads— vector quantization can further reduce costs and improve performance, even at the highest volume and scale to unlock more use cases.

Happy to share that I have successfully completed the Elastic Observability Engineer (On-Demand) course! 🚀 #Elastic #ObservabilityEngineer #ProfessionalDevelopment #SkillDevelopment #DXCTechnology

Let me try to explain a key development in the journey of GenAI through 2 simple words -  Seq and Vec A direct result of Warren Weaver’s Memorandum of Translation that we spoke about in my previous post, seq2vec family of models perfectly apply the encoder-decoder architecture that Transformers so elegantly adapted. Enhancements also use the Attention mechanism designed to capture “context”. Let me explain - Seq = sequence Vec = vectors Let’s say - Seq is a sequence - lets say of words, i.e., a sentence.  Vec is a mathematical representation of an image or video, i.e., a collection of pixels Seq to vec? Take a sequence (a phrase or a sentence) and convert it to a video or image. SeqVec tools you know: Dall-E, Midjourney, Runway, Sora Vec to seq? Take a vec (image or video) and conduct a search (Google Image Search). It can also “listen to” and summarize the image or video. VecSeq tools you know: Gemini, AWS Nova, 4o/o1 all have these capabilities now Seq to seq? What do you think? Tell me in the comments below?

Powerful DBRX by Databricks is a new state-of-the-art open LLM and beats other open and closed models on various fronts. Available in Ollama, works silicon MLX, but the 4-bit quantization still requires over 70Gb of VRAM. https://buff.ly/3xgmiXK #databricks #LLMs

Civo meets Llama 3.2: How to deploy AI Models on GPU clusters? Setting up a GPU-enabled cluster to run LLMs can be complex and time-consuming, especially for those who require seamless integration, data security, and regulatory compliance. To address this challenge, we've created a step-by-step guide to deploying a Kubernetes GPU cluster on Civo using the Civo LLM Boilerplate.  Read the tutorial https://lnkd.in/ewmKPydD