[Research] 2,788 tokens/s on a single RTX 5090 with a diffusion LLM — Introducing ORL (fully open source)
We're excited to share ORL (On-Policy Residual Learning), our new research on making diffusion language models (dLLMs) significantly faster without sacrificing generation quality.
Proof2 community threads
Key points
- With ORL and our custom inference engine dlmrt, we've achieved:
- 7,708 tokens/s on 8× B200 — DiffusionGemma 26B MoE + ORL, NVFP4
- Training on stale, offline residuals creates a mismatch with inference.
- We introduce Adjacent Entropy Contraction, which encourages each denoising step to reduce uncertainty relative to the previous step.
Sources (2)
- [1][Research] 2,788 tokens/s on a single RTX 5090 with a diffusion LLM — Introducing ORL (fully open source)r/LocalLLaMA (top, daily) · Oct 16, 07:33 PM
We're excited to share ORL (On-Policy Residual Learning), our new research on making diffusion language models (dLLMs) significantly faster without sacrificing generation quality.
With ORL and our custom inference engine dlmrt, we've achieved:
- [2][Research] 2,788 tokens/s on a single RTX 5090 with a diffusion LLM — Introducing ORL (fully open source) [R]r/MachineLearning (top, daily) · Oct 16, 08:03 PM · same content
Extractive summary: sentences quoted from the sources.
Before this
- Oct 16, 2026Welcome Beam: a 501B open model you can own, customize, and scale
- Oct 15, 2026Granite Switch 4.1: Chat, Decisions, and Speech from One Endpoint
- Oct 15, 2026unslothai/unsloth v0.1.908-beta: NVIDIA Nemotron 3.5 + Meta Muse Spark Open
- Oct 15, 2026NVIDIA Nemotron 3.5 Super VL Powers Specialized Agents with Multimodal Reasoning
- Oct 15, 2026StepFun Releases Step 5 Preview Open Weights: 600B MoE, 27B Active, 1M Context
- Oct 15, 2026Local and Distributed Large LLM Inference with MLX and RDMA on Mac Studio
