SLOs-Serve vs vLLM Feature Comparison | Generated by AI
Comparison: SLOs-Serve vs. vLLM
SLOs-Serve extends vLLM by integrating it as the core batching engine while adding layers for multi-SLO (Service Level Objective) guarantees, making it more suitable for production environments with diverse, latency-sensitive workloads. vLLM focuses primarily on high-throughput inference for single-SLO or throughput-maximizing scenarios, using techniques like PagedAttention for memory efficiency. Below is a structured comparison based on key aspects from the SLOs-Serve paper and vLLM’s design.
| Aspect | SLOs-Serve | vLLM |
|---|---|---|
| Primary Focus | Multi-SLO serving for multi-stage LLM apps (e.g., tight TTFT for prefill in reasoning, tight TPOT for decode in coding). Handles bursty, mixed workloads with stage-specific guarantees. | High-throughput batching for continuous decoding, optimized for memory-bound workloads via PagedAttention. Assumes uniform SLOs or prioritizes aggregate throughput. |
| SLO Handling | Explicit multi-SLO support: Per-stage (prefill/decode) and per-app SLOs (e.g., 50ms TPOT for coding vs. 100ms for chat). Uses soft admission control to reject/defer violating requests. | No native multi-SLO; relies on static configs (e.g., max batch size). SLO violations common under contention (e.g., >2x latency spikes in bursts). |
| Scheduler | Dynamic programming (DP)-based: Optimizes prefill budgets, batch sizes, and speculation lengths per SLO tier. Predicts execution time with Roofline model (R² > 0.8 accuracy). | Continuous batching scheduler: Greedily packs requests into dynamic batches, focusing on decode-heavy workloads. No SLO-aware planning. |
| Prefill Optimization | Chunked prefill with adaptive speculation (1-10 tokens per SLO). Allocates “prefill budget” to balance with decode. | Single-shot prefill per request; supports chunked but without SLO adaptation. Prone to head-of-line blocking in mixed loads. |
| Decode Optimization | SLO-adaptive batch sizing (up to 512+ tokens) and speculative decoding tailored to TPOT targets. | Efficient continuous decoding with look-ahead batching; high throughput (e.g., 10-20x over Hugging Face) but ignores per-request deadlines. |
| Resource Management | Multi-replica routing via Ray; burst resilience with best-effort queues and preemption. Handles disaggregated setups. | Single-node or basic distributed (via Ray integration); no proactive routing or SLO-prioritized allocation. |
| Throughput & Capacity | 2.2× average capacity gain over vLLM (geo-mean across 6 scenarios: chatbot, coding, etc.). E.g., 2.4× in reasoning bursts. Super-linear scaling with replicas. | Baseline for throughput: Up to 24x faster than alternatives in decode-heavy traces, but degrades under SLO constraints (e.g., 50% capacity loss in mixed workloads). |
| Overhead | <10ms per schedule; minimal due to DP efficiency (O(n) states). | Low (<1ms batching); but lacks SLO logic, so no added overhead. |
| Use Cases | Production apps with strict SLAs: Tool-calling agents, reasoning chains, bursty traffic (e.g., Azure traces). | Research/dev throughput: Simple serving, long-context generation, uniform loads. |
| Limitations | Assumes known SLOs/deadlines; future extensions needed for unknown lengths. Built on vLLM, so inherits its decode strengths. | Struggles with multi-stage/variable SLOs; high tail latencies in shared GPU setups (p99 >2x target in evaluations). |
| Implementation | Open-source prototype on vLLM + Ray; supports OPT/ToolLlama models on A100/H100 GPUs. | Mature open-source (GitHub); widely adopted, supports 100+ models, easy deployment. |
In summary, vLLM excels in raw efficiency for throughput-oriented serving but falls short on SLO compliance in complex, real-world scenarios—leading to the 2x+ gains in SLOs-Serve via targeted optimizations. If your workload is purely throughput-maximizing without latency guarantees, vLLM remains simpler and faster to deploy.
SLOs-Serve: Optimized Serving of Multi-SLO LLMs
vLLM: Easy, Fast, and Cheap LLM Serving with PagedAttention