Another dimension of scaling has dropped: - scaling the speculator to accelerate inference (and thus RL)
GLM 5.2 DSpark update! The full Speculators training run is well underway and we have the epoch-1 checkpoint ready for your GPUs using vLLM nightly: https://huggingface.co/RedHatAI/GLM-5.2-speculator.dspark This improves upon the speedup from the preview checkpoint by another 1.5-2x. Stay tuned for more!
Users are excited about RedHatAI's GLM-5.2 DSpark Speculator Checkpoint because training dedicated speculators overcomes long-standing autoregressive inference bottlenecks to deliver much faster performance.
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DSpark probably has too little capacity and easily saturates in terms of avg acceptance length. Bigger/more intensive speculators kind of defeat the purpose. But… there must be a way… it's a new axis of sparsity too. Sparse speculator? Decoder^2? Sharing from kv-shared layers?
Another dimension of scaling has dropped: - scaling the speculator to accelerate inference (and thus RL)
@teortaxesTex Making faster speculators at the same accept length too
Historically, autoregressive bottlenecks have capped inference speed since the transformer times.
Training dedicated speculators flips that, parallel verification on guessed tokens,
now tuned end-to-end for your exact workload.
For agentic loops and RL?
This is rocket fuel: tighter feedback cycles, longer horizons, cheaper reasoning runs.
They added another lever to the datascape.
Early adopters on vLLM nightly are already compounding.