Speculative Decoding
Speculative Decoding is an inference optimization technique that pairs a large target model with a lightweight draft mechanism that quickly proposes several next tokens. The target model verifies thos
Sep 9, 20265 min read
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Cache Eviction
The key-value cache is an inference optimization technique that eliminates redundant recomputation of past token representations during autoregressive generation. The KV cache memory footprint scales
Aug 6, 20269 min read6Model Quantization
Quantization is a model compression technique that reduces numerical precision of weights and activations from floating-point to lower-bit representations, decreasing model size and computational cost
Jul 31, 20265 min read1Neural Architecture Search
Automates the process of determining optimal model configurations by systematically exploring large spaces of possible architecture to identify those that best balance accuracy, computational cost, me
Jul 30, 20263 min readStructured approximations
Approximation-based compression techniques restructure model representation to reduce complexity while maintaining expressive power, complementing the pruning and distillation methods discussed earlie
Jul 28, 20265 min readKnowledge distillation
Knowledge distillation involves using a large teacher model to train a smaller student model. The student model not only learns from the correct labels but also from the teacher's output distribution.
Jul 27, 20263 min read1Model Pruning
Structured model optimization Structured model optimization works in two key ways: Eliminating parameter redundancy. Structuring computations for efficient hardware execution through techniques like
Jul 25, 20263 min read5Model Optimization
Model optimization is the systematic transformation of ML models to maximize computational efficiency while preserving task performance, enabling deployment across divers hardware constraints. In most
Jul 23, 20263 min read17