Observed facts: AstraBlog’s article describes an 8B bf16 model on an H100, estimating that reading 16 GB of weights at 3.35 TB/s takes about 4.8 ms per pass. It explains that a target model can score a sequence of drafted tokens in one pass, accepting the longest agreeing prefix in greedy decoding. For sampled decoding, the article gives an acceptance rule and residual distribution intended to reproduce the target distribution exactly. It also surveys attached drafters such as EAGLE-3, multi-token prediction, DFlash, and DSpark.
Mechanism: A drafter proposes candidates that the target scores in a single pass; verification accepts agreeing tokens and applies a correction rule for sampling so the output follows the target distribution. The target model’s weights are already being read for the verification pass; scoring several candidate positions can therefore use that data movement more efficiently than generating each token in a separate pass. A drafter supplies candidate tokens, and the target rejects mismatches or keeps agreements. A weak drafter cannot alter the target distribution under the described correction rule; it can only reduce the amount of useful work accepted.
Why now: The article, posted October 11, 2026, consolidates first-principles reasoning with a range of newer drafter designs, making a complicated inference technique easier for builders to compare and reason about.
Implications: Builders should evaluate drafter quality and verification cost together, not draft length alone. Builders evaluating latency should consider drafter quality and verification cost together, rather than treating draft length alone as the optimization target. They should also distinguish a correctness-preserving method from evidence of a particular production speedup.
Evidence boundary: The supplied source is a technical explainer, not an independent deployment study or a new comparative benchmark. Its H100 calculation is an illustrative estimate, and the article does not establish that every model, batch size, serving engine, or workload will benefit equally.
Watch: Look for workload-specific measurements that report end-to-end latency, accepted draft length, and overhead across drafter architectures and serving configurations.