Search Results: LLMInference

Transformer-based LLMs consistently become the performance choke point in production systems, with latency often exceeding user expectations by 10x. This deep dive reveals how the architecture's design for parallel training clashes with the sequential demands of token generation, creating fundamental bottlenecks rooted in memory access patterns rather than raw compute power. Understanding these constraints is essential for effective optimization.

NVIDIA's newly open-sourced Dynamo framework rethinks large language model serving through disaggregated GPU processing, intelligent KV cache routing, and elastic resource management. This deep dive examines how its Rust-core architecture tackles the fundamental bottlenecks of LLM inference while slashing operational costs for reasoning models.

New research reveals speculative decoding's true potential for accelerating LLM inference hinges on a critical factor: domain-specific draft models. While theoretical gains promise 3x speedups, real-world implementations fall short without custom training – a crucial insight for latency-sensitive AI applications.