Retrieval-augmented generation (RAG) services are rapidly gaining adoption in enterprise settings as they combine information retrieval systems (e.g., databases) with large language models (LLMs) to enhance response generation and reduce hallucinations. By augmenting an LLM’s fixed pre-trained knowledge with real-time information retrieval, RAG enables models to effectively extend their context to large knowledge bases by selectively retrieving only the most relevant information. As a result, RAG provides the effect of dynamic updates to the LLM’s knowledge without requiring expensive and time-consuming retraining. While some deployments keep the entire database in memory, RAG services are increasingly shifting toward persistent storage to accommodate ever-growing knowledge bases, enhance utility, and improve cost-efficiency. However, this transition fundamentally reshapes the system’s performance profile: empirical analysis reveals that the Search & Retrieval phase emerges as the dominant contributor to end-to-end latency. This phase typically involves (1) running a smaller language model to generate query embeddings, (2) executing similarity and relevance checks over varying data structures, and (3) performing frequent, long-latency accesses to persistent storage. To address this triad of challenges, we propose a metamorphic in-storage accelerator architecture that provides the necessary programmability to support diverse RAG algorithms, dynamic data structures, and varying computational patterns. The architecture also supports in-storage execution of smaller language models for query embedding generation while final LLM generation is executed on DGX A100 systems. Experimental results show up to 4.3x and 1.5x improvement in end-to-end throughput compared to conventional retrieval pipelines using Xeon CPUs with NVMe storage and A100 GPUs with DRAM, respectively.

While model serving has unlocked unprecedented capabilities, the high cost of serving large-scale models continues to be a significant barrier to widespread accessibility and rapid innovation. Compiler optimizations have long driven substantial performance improvements, but existing compilers struggle with neural workloads due to the exponentially large and highly interdependent space of possible transformations. Although existing stochastic search techniques can be effective, they are often sample-inefficient and fail to leverage the structural context underlying compilation decisions. We set out to investigate the research question of whether reasoning with large language models (LLMs), without any retraining, can leverage the context-aware decision space of compiler optimization to significantly improve sample efficiency. To that end, we introduce a novel compilation framework (dubbed Reasoning Compiler) that formulates optimization as a sequential, context-aware decision process, guided by a large language model and structured Monte Carlo tree search (MCTS). The LLM acts as a proposal mechanism, suggesting hardware-aware transformations that reflect the current program state and accumulated performance feedback. Monte Carlo tree search (MCTS) incorporates the LLM-generated proposals to balance exploration and exploitation, facilitating structured, context-sensitive traversal of the expansive compiler optimization space. By achieving substantial speedups with markedly fewer samples than leading neural compilers, our approach demonstrates the potential of LLM-guided reasoning to transform the landscape of compiler optimization.