Speculators v0.9.0 Adds Faster Training and FP8 Backend
Faster training and halved storage lower the cost of building custom speculative decoding models, which speed up inference without changing model weights. Teams running vLLM can now train draft models on their own workloads and deploy them in the same stack. The FP8 backend matters most for those serving large models on memory-constrained GPUs.
