Parallel Decoding for LLM Inference

• Parallel decoding is a technique that restructures LLM inference to execute multiple stages concurrently, significantly reducing latency.
• It employs methods like pipeline and tensor parallelism along with dynamic bucketing and priority-aware scheduling to optimize hardware utilization.
• Implementations demonstrate throughput gains up to 3.6× and improved GPU usage by mitigating idle periods and communication bottlenecks.

Parallel decoding refers to a comprehensive set of hardware, system, and algorithmic techniques aiming to maximize throughput and minimize latency in LLM serving environments by exploiting concurrency during the autoregressive decoding phase. In contrast to classical sequential decoding, which generates tokens one at a time, parallel decoding architectures leverage pipeline parallelism, batch-level concurrency, and disaggregation of the inference phases. The field incorporates advanced scheduling, memory management, and communication strategies to address the stringent performance demands and hardware constraints characteristic of modern generative model inference.

1. Architectural Foundations of Parallel Decoding

Parallel decoding fundamentally restructures the LLM inference pipeline to decouple and concurrently execute distinct stages of generation: prompt prefill, incremental decoding, and in multi-tenant or distributed systems, inter-block or inter-request coordination. The main elements are:

• Pipeline Parallelism: The transformer is partitioned into $L$ consecutive stages distributed across $P$ GPUs. Micro-batches traverse stages 1 to $P$ in a round-robin manner. The system achieves a speedup of $1.8$--$2.5\times$ in end-to-end throughput versus a sequential single-device baseline. However, naive pipeline scheduling suffers from fill/drain "bubbles"—inefficient GPU idle periods especially during the transition between prompt and token phases (Zhen et al., 28 Apr 2025).
• Tensor Parallelism: Each transformer layer's computation is split across GPUs by partitioning the matrix operations along the feature or head dimensions. This enables near-linear scaling in matrix throughput, while introducing additional intra-layer communication costs, approximately $2(P-1)/P$ of tensor size per layer (Miao et al., 2023).
• Disaggregated Inference: Recent systems physically separate prefill and decode stages onto distinct resources. Prefill replicas perform the dense matrix-multiplication intensive context encoding, while decoding replicas focus on auto-regressive token generation, each using bespoke batch sizes and parallelism suited to their computational profiles. This disaggregation eliminates mutual interference between prompt and decode workloads, improves resource utilization, and facilitates independent scaling and optimization (Jiang et al., 11 Feb 2025, Strati et al., 4 Mar 2024).
• Hardware-Aware Partitioning: Practical implementations (e.g., MoLink, HexGen-2) support heterogeneous GPU environments by applying graph partitioning and max-flow solutions to assign stages, batch sizes, and transfer patterns based on device compute, memory, and interconnect bandwidth (Jin et al., 7 Jul 2025, Jiang et al., 11 Feb 2025).

2. Scheduling Strategies and Request Coordination

Parallel decoding systems employ sophisticated scheduling algorithms to maximize hardware utilization and meet service-level objectives (SLOs) in the presence of bursty, heterogeneous workloads:

• Dynamic Bucketing: Systems such as BucketServe group requests with similar sequence lengths into buckets, minimizing inter-request padding waste and enabling effective batch-level parallelism within physical memory bounds. Bounds are chosen to minimize expected padding overhead under the observed request distribution, constantly adapting via interval bisection (Zheng et al., 23 Jul 2025).
• Continuous and Microbatch Scheduling: Platforms like DéjàVu and FastDecode employ "microbatch swapping"—ensuring that the number of concurrent in-flight microbatches matches the pipeline's depth and communication-limited concurrency. This approach maintains near-constant GPU memory usage (usually $1$ or $2$ batches per stage), while overlapping compute and KV-cache data movement to maximize throughput (Strati et al., 4 Mar 2024, He et al., 18 Mar 2024).
• Priority-Aware Dispatching: To handle mixtures of latency-sensitive (online) and throughput-oriented (offline) workloads, hybrid schedulers (HyGen, BROS) apply two-phased or greedy heuristics. They admit urgent, short jobs based on predicted SLO slack while opportunistically filling capacity with longer tasks. These frameworks implement bidirectional cache sharing or preemption to prevent head-of-line blocking and enforce strict SLOs for real-time requests while minimizing throughput sacrifice for best-effort jobs (Borui et al., 13 Apr 2025, Sun et al., 15 Jan 2025).
• Length-Predictive Scheduling: Systems such as SSJF use lightweight proxy models to predict the output sequence length per request and prioritize short jobs, yielding significant reductions in average and tail latency (30–40%), and up to 3.6× higher throughput compared to FCFS under realistic arrival and burstiness conditions (Qiu et al., 12 Apr 2024).

3. Memory and Communication Optimization

Parallel decoding is constrained by the need to manage the expanding per-request key-value (KV) cache, which dominates GPU memory and limits batch concurrency:

• Streaming and Layerwise Paging: DéjàVu introduces a streaming library (DéjàVuLib) facilitating contiguous layer-by-layer KV-cache streaming, overlapping cache writes/flushes during prompt processing, and batching small updates for efficient PCIe/NVLink transfers during decoding. Practical overhead remains negligible ($<2\%$), even in distributed, off-device streaming (Strati et al., 4 Mar 2024).
• Swappable Cache Layouts: Microbatch swapping policies limit in-GPU residency to one or two microbatches per stage, trading off between memory usage and data movement overhead. Persistent storage or offloading strategies (e.g., hierarchical paging in FastDecode and Token-level LayerKV) complement these approaches, enabling almost linear reductions in per-GPU memory footprint (He et al., 18 Mar 2024).
• Communication-Aware Partitioning: In distributed deployments, the allocation of KV-cache transfer flows is governed by max-flow formulations over the GPU-interconnect graph. Schedulers actively match batch sizes, phase assignments, and link usage to prevent sudden bandwidth bottlenecks, ensuring smooth hand-off of prefill outputs to decoding stages and maximizing token/sec throughput under heterogeneous node and network constraints (Jiang et al., 11 Feb 2025, Jin et al., 7 Jul 2025).

4. Fault Tolerance and System Robustness

Parallel decoding systems designed for production deployments must address fault tolerance and system recovery:

• State Replication: DéjàVu implements per-stage KV-cache replication to neighboring nodes in a logical ring, enabling rapid detection (via heartbeat) and localized recovery (via replay and cache restoration) from node failures. In empirical tests, end-to-end latency increases are sharply curtailed (1.24× vs 1.91×) compared to non-fault-tolerant baselines (Strati et al., 4 Mar 2024).
• Asynchronous Rollback: Upon detection of a stage failure, consistent recovery points are established from replicas, and the pipeline resumes with a minimal replay of lost work, bounded by the latest safe step and small computation overlaps (Strati et al., 4 Mar 2024).

5. Performance Impact and Empirical Benchmarks

Parallel decoding architectures consistently yield significant performance gains across a variety of benchmarks and hardware settings:

System	Throughput Gain	Latency Reduction	Memory Reduction	GPU Utilization
DéjàVu	up to 2× vs. FT	Prompt/Decode bubble removal	1.8× via swap	>90% (vs. 60–70%)
BucketServe	3.58× vs. UELLM, 1.31× DistServe	SLO-tail isolation	<1% runtime overhead	81.7% avg (vs. 50–60%)
SSJF	2.2–3.6× vs. FCFS	30–40% JCT, p95	N/A	Improved batch usage
FastDecode	1.88–5.04× vs. vLLM	Batching unlocked by offload	up to 5× batch	85–95%
HexGen-2	up to 2.0× vs. DistServe	1.5× P99 improvement	Cost: 30% lower (equal perf)	Hetero GPU-aware

[See (Zheng et al., 23 Jul 2025, Strati et al., 4 Mar 2024, Jiang et al., 11 Feb 2025, He et al., 18 Mar 2024, Sun et al., 15 Jan 2025, Qiu et al., 12 Apr 2024) for system-specific details]

6. Limitations and Future Directions

Several challenges and directions for further development have emerged:

• Scalability and Heterogeneity: Extending queueing, dynamic bucketing, and resource-partitioning techniques to multi-node clusters with varying GPU memory, computation, and network bandwidth remains open. HexGen-2 and MoLink exemplify emerging solutions, but full elasticity and multi-level scheduling are under exploration (Jiang et al., 11 Feb 2025, Jin et al., 7 Jul 2025).
• Workload Characterization: Real-world traces are highly heterogeneous, with independent diurnal shifts in input/output length distributions and strong client-level locality. Optimal scheduling and planning increasingly depend on realistic workload models, per-client burstiness profiling, and adaptive autoscaling (Xiang et al., 15 May 2025).
• Adaptive and Energy-Aware Scheduling: Systems such as FREESH integrate per-request scheduling and dynamic device-level DVFS/partitioning to minimize carbon and energy subject to SLOs. Global control loops operating at multiple time scales—across geo-distributed clusters—are becoming essential in production (He et al., 2 Nov 2025, Stojkovic et al., 29 Mar 2024).
• Algorithmic Advances: Integration of speculative decoding, early exit, and more sophisticated proxy-based job-size predictors promises further improvements in both head/tail latency and hardware cost (Qiu et al., 12 Apr 2024, Miao et al., 2023).
• Privacy and Security: Cross-node KV-cache transfers, especially in decentralized or edge scenarios, introduce new privacy and correctness challenges. Emerging protocols for secure cache movement and auditability are critical directions (Wu et al., 4 Jan 2025, Zhen et al., 28 Apr 2025).

Parallel decoding thus subsumes an interlocking set of systems and algorithms that collectively achieve multi-fold improvements in throughput, latency, and cost-efficiency, with increasing adaptability to heterogeneous, distributed, and bursty workloads characteristic of modern LLM deployments.