BlockServe: Block-Grained Continuous Batching for High-Throughput Diffusion LLM Serving
BlockServe introduces block-grained continuous batching to address convergence heterogeneity in diffusion Large Language Models (dLLMs), allowing immediate eviction of completed requests at block boundaries. The framework employs mixed-state execution with gather-scatter indexing to extend dual cache and parallel decoding to heterogeneous batches, reducing compute bubbles and tail latency. A compute-aware admission controller utilizes token-budgeted refill to expand effective batch capacity with
Analysis
TL;DR
- BlockServe introduces block-grained continuous batching to address convergence heterogeneity in diffusion Large Language Models (dLLMs), allowing immediate eviction of completed requests at block boundaries.
- The framework employs mixed-state execution with gather-scatter indexing to extend dual cache and parallel decoding to heterogeneous batches, reducing compute bubbles and tail latency.
- A compute-aware admission controller utilizes token-budgeted refill to expand effective batch capacity without compromising resource efficiency.
- Empirical results on Dream and LLaDA models show a 1.9–10.6x throughput improvement over Fast-dLLM across five benchmarks while maintaining comparable generation quality.
Why It Matters
This research addresses a critical bottleneck in deploying diffusion-based generative models, which suffer from significant latency variations compared to autoregressive models. By enabling efficient, high-throughput serving of dLLMs, BlockServe facilitates the practical adoption of these models in production environments where low tail latency and high concurrency are essential.
Technical Details
- Block-Grained Scheduling: Unlike traditional continuous batching that waits for sequence completion, BlockServe evicts finished requests immediately at block boundaries, preventing fast sequences from being stalled by slow stragglers.
- Mixed-State Execution: The system supports heterogeneous batches by using gather-scatter indexing to manage dual caches and parallel decoding states simultaneously, ensuring efficient memory access and computation across varying convergence rates.
- Compute-Aware Admission Control: An admission controller regulates incoming requests based on token budgets, dynamically adjusting batch size to maximize throughput while preventing resource exhaustion.
- Performance Benchmarks: Evaluated on Dream and LLaDA architectures, demonstrating significant throughput gains (up to 10.6x) over existing baselines like Fast-dLLM.
Industry Insight
- Adoption of Diffusion LLMs: As diffusion models gain traction for text generation, infrastructure must evolve beyond autoregressive-serving paradigms; BlockServe provides a blueprint for next-generation serving engines.
- Optimization Focus: Practitioners should prioritize handling convergence heterogeneity and state management in batched inference to unlock the full potential of non-autoregressive generative models.
- Scalability Implications: The token-budgeted refill strategy offers a scalable approach to managing variable-length generation tasks, potentially reducing hardware costs for high-concurrency deployments.
Disclaimer: The above content is generated by AI and is for reference only.