Speculative Sampling

A breakthrough benchmark shared on Reddit's LocalLLaMA community reveals that Gemma 4 26B (AWQ 4-bit) has reached a blistering 600 tokens/second on a single RTX 5090 (32GB VRAM), leveraging DFlash speculative sampling within vLLM (0.19.2rc1).▶ Speculative Sampling has evolved into the definitive performance multiplier for single-GPU setups. By utilizing a DFlash draft model, the benchmark achieved massive throughput gains in a 256-input/1024-output workload.▶ RTX 5090 Hardware Synergy: The 32GB VRAM and massive memory bandwidth allow 26B-class models to run at speeds previously reserved for much smaller architectures, effectively bridging the gap between local setups and enterprise-grade inference clusters.Bagua InsightHitting 600 tok/s is a watershed moment for the local LLM ecosystem. It signifies the end of the "latency bottleneck" for real-time AI interaction. While traditional autoregressive decoding is bound by memory bandwidth, the "predict-then-verify" paradigm of DFlash, powered by the RTX 5090’s raw compute, pushes inference efficiency toward its physical limit. The synergy between Gemma 4’s architecture and vLLM’s scheduling proves that the 20B-30B parameter range is the new "sweet spot" for edge AI Agents. This level of performance enables complex, multi-step Agentic workflows to execute in seconds, ensuring a seamless user experience that rival cloud-based APIs.Actionable AdviceDevelopers should immediately prioritize the integration of DFlash and similar speculative sampling techniques within vLLM to achieve low-latency local RAG or Agentic deployments. For enterprises looking to deploy high-performance LLMs at the edge, the combination of a 26B-scale model and speculative sampling offers a superior performance-to-cost ratio compared to deploying larger, slower models on more expensive hardware.