Mixture-of-Experts Routing for Efficient Large Language Models
How mixture-of-experts routing activates only a subset of parameters per token, reducing inference compute while preserving model capacity in large language models.
Artificial Intelligence
Cutting-edge architectures, LLMs, fine-tuning, and agentic workflows.
KV Cache Reuse and Prefix Caching in vLLM for Multi-Tenant LLM Serving
Learn how vLLM prefix caching reuses KV cache blocks across shared prompts to cut prefill latency, GPU memory waste, and cost in multi-tenant LLM serving.
Speculative Decoding in vLLM: Slashing TTFT and Inter-Token Latency in LLM Serving
Explore speculative decoding in vLLM to reduce inter-token latency and TTFT in high-concurrency LLM serving environments with zero accuracy loss.
