SGLang: Optimized LLM Execution & Caching

• SGLang is a co-designed programming language and runtime system that optimizes LLM serving, orchestration, and structured output with integrated prompt engineering and hierarchical caching.
• It features a Python-embedded DSL supporting agentic flows, parallel generation, and structured control, enabling efficient multi-turn state and tool invocation in LLM pipelines.
• Innovations such as radix-tree KV-cache reuse and dynamic batch scheduling yield significant gains in latency, throughput, and cost efficiency for both on-device and server deployments.

SGLang is a co-designed programming language and optimized runtime system for efficient serving, orchestration, and structured output of large and small LLMs, focusing on advanced prompt engineering, cache persistence, and real-time performance in agentic and structured-program workloads. Developed to address the needs of complex LLM pipelines—including control flow, tool invocation, multi-turn state, and hierarchical caching—SGLang unifies a Python-embedded frontend, an execution runtime with advanced KV-cache reuse via radix-trees, and modular backends, supporting both on-device and server deployments. Its system-level innovations have catalyzed new paradigms in LLM accelerator, agent stack, and cache management research (Zheng et al., 2023, Pan et al., 27 Jun 2025, Xie et al., 26 Aug 2025, Sharma et al., 4 Oct 2025, Yu et al., 20 Nov 2025).

1. Evolution and Motivations

Contemporary LLM applications require the composition of multiple prompt calls, structured output, dynamic dispatch to external tools, parallel execution, and persistent context across sessions. Legacy LLM inference frameworks—such as LangChain, LMQL, or ordinary REST endpoints—fail to optimize for multi-call batch efficiency, cache sharing, latency minimization, or agentic correctness. SGLang was developed to close this gap by:

• Embedding prompt-composable primitives (e.g., gen(), select(), fork(), control flow) within Python for maximal programmability (Zheng et al., 2023).
• Co-optimizing the serving stack for both throughput and latency by batch scheduling, prefix-sharing heuristics, and persistent cache management.
• Providing native support for schema-constrained decoding, agent orchestration, and robust metric reporting in production (Sharma et al., 4 Oct 2025).

This integrated design shifts inference systems from stateless sampling to a model for programmatic agent flows and structured interleaved operations.

2. Frontend Language and Agent Programming Model

SGLang's frontend is an embedded Python DSL that exposes primitives allowing arbitrary interleaving of structured prompt construction, parallel generation calls (fork/join), and external function invocation (Zheng et al., 2023). Major primitives include:

• +=: Appends prompt fragments to the current context.
• gen(): Executes a model generation call at a program point.
• select(choices=...): Forces selection among explicit response options.
• fork(n), join(): Spawns and merges parallel streams for explorative search.
• run(fn, args...): Executes subprograms as nested SGLang streams.

By allowing direct Python control flow, users embed loops, branching, conditionals, and type-safe calls to external libraries, promoting seamless integration of logic, tool use, and multi-modal pre/post-processing.

The frontend is optimized for agentic flows such as ReAct, multi-turn chat, self-consistency sampling, structured extraction, and retrieval-augmented generation—especially in tightly coupled SLM-dominant agent stacks (Zheng et al., 2023, Sharma et al., 4 Oct 2025).

3. Execution Runtime, Caching, and Scheduling

Execution Architecture

SGLang's runtime is engineered for single-replica execution and exposes the following core modules (Zheng et al., 2023, Pan et al., 27 Jun 2025):

• Stream Executor: Each prompt stream executes asynchronously, enabling interleaved computation and efficient lockstep progress across program subgraphs.
• Batch Engine: Applies continuous dynamic batching with a global token budget $B_{\max}$ at each round. Prefill and decode are unified into the same batch scheduling loop, enabling low latency.
• Cache System: Employs a radix-tree mapping token-prefixes to KV-cache pages (RadixAttention), with LRU eviction of infrequently used pages. On every new prompt, the longest matching prefix in cache is reused—minimizing redundant transformer computation.
• Kernel Optimizations: Utilizes fused blockwise attention and FFN kernels (FlashAttention-style), CUDA graphs for kernel launch minimization, and a non-contiguous KV-cache layout for efficient page management (Zheng et al., 2023, Pan et al., 27 Jun 2025).

Scheduling and Sharing

Requests are queued using FCFS discipline, but batch formation uses a secondary prefix-sharing heuristic: among eligible requests, those with the longest shared prefix with the persisted global cache are prioritized, maximizing immediate reuse and hitting cache (Pan et al., 27 Jun 2025).

Memory Management

Cache pages are managed in paged GPU memory, with pure LRU eviction. Upon memory overflow, the lowest-priority request's pages are purged; upon preemption, SGLang discards the request's KV cache, and recomputation occurs upon resumption. SGLang's base version does not offload to CPU or disk, but hierarchical extensions (see Strata) add multi-tier cache management (Xie et al., 26 Aug 2025).

4. Innovations in Long-Context and Hierarchical Caching

As context lengths and cache footprint grow, SGLang is extended by systems such as Strata and SGLang-LSM for multi-tier, scalable KV cache management.

Strata (Xie et al., 26 Aug 2025)

Strata augments SGLang with:

• HiRadixTree: Tracks KV-cache pages and their location (GPU, CPU, disk), exposing delay-hit detection and async metadata marking.
• Strata CacheController: Manages GPU/CPU/disk tiers and orchestrates transfers using GPU-assisted I/O kernels for high-bandwidth, low-interference data movement.
• Scheduler: Implements balanced batch formation and delay-hit deferral, maintaining load/compute ratio below threshold τ for efficient batch assembly and resource utilization.

Strata can yield up to 5× lower TTFT on long-context workloads compared to vLLM+LMCache and 3.75× the throughput of TensorRT-LLM, with negligible regression on short-context tasks.

SGLang-LSM (Yu et al., 20 Nov 2025)

SGLang-LSM introduces:

• An LSM-tree–backed prefix-preserving storage engine, mapping token sequences to ordered keys, storing only small metadata in the LSM index and appending bulk KV tensors to sequential log files.
• Dynamic control over size-ratio $T$ and runs parameter $K$, guided by live workload mix, to adapt between tiering (write-heavy) and leveling (read-heavy) regimes to optimize I/O amplification and latency.
• Batch operations for get/put and resource controls (budgeting, throttling, consolidation).
• Resulting cache hit rate improvements up to 143% and TTFT reductions up to 24% over file-per-token baselines.

These extensions enable scaling cache to hundreds of millions of tokens across commodity GPUs and SSDs without the directory blow-up or random I/O bottlenecks endemic to naive file-per-token approaches.

5. Agentic System Features, Structured Decoding, and Production Metrics

SGLang is leveraged in agent systems where the goal is schema-valid, API-constrained outputs and reliable tool execution at high throughput and low cost (Sharma et al., 4 Oct 2025). Key features include:

• Schema-First Prompting: Prompts embed JSON Schema definitions. Guided decoding constrains generation to schema-valid completions, verified at each step by incremental streaming validators. Early abort is applied to malformed partial output.
• Guided Decoding/Function Calling: Constrained beam, on-the-fly grammar restriction, and post-generation validation ensure strict adherence to registered tool signatures and schema formats.
• Uncertainty-Aware Routing/Verification: SGLang systems route by cost/latency/confidence proxies, invoke verifier adapters for near-misses, and escalate to LLM fallback only on persistent failures or high-uncertainty events.

Adopted engineering metrics are:

Metric	Definition
Cost per Successful Task (CPS)	$$\frac{\sum_{x\in B}\mathrm{cost}(x)}{|\{x\in B : \mathrm{valid}(x)\land \mathrm{exec}(x)\}|}$$
Schema Validity Rate	$$\frac{|\{x\in B : \mathrm{validate}(y_x, S)\}|}{|B|}$$
Executable-Call Rate (ExecRate)	$$\frac{|\{x\in B : \mathrm{validate}(y_x, S)\land \mathrm{argsExact}(y_x)\}|}{\#\mathrm{calls}}$$
p50/p95 Latency	Median and 95th-percentile end-to-end latency over $B$
Energy per Request	$e_i \cdot T_{\mathrm{out}}$ (joules per token times output tokens)

Empirical results show SGLang with INT4–INT8–quantized SLMs achieves >98% schema-valid @1, >97% executable-call rates, and 9–10× cost reduction (CPS = 0.11×) versus FP16 LLMs. SGLang-enforced cascades exhibit superior latency and energy characteristics at output invocation scale (Sharma et al., 4 Oct 2025).

6. System Comparison, Performance, and Limitations

SGLang achieves up to 4–6× higher throughput than baseline vLLM and LMQL on end-to-end agentic, reasoning, and long-context tasks (Zheng et al., 2023), with critical gains stemming from:

• RadixAttention/KV-cache reuse—turning off collapses throughput (−70–90%).
• Batch scheduling by prefix sharing—random/FCFS scheduling lowers throughput by 20–40% on shared-context workloads.
• Interpreter parallelism and kernel fusion—eliminating either reduces performance 30–50% or more in realistic pipelines.

Head-to-head, SGLang (especially with Strata) delivers up to 5× lower TTFT and 2–3.75× throughput on long-context tasks compared to vLLM+LMCache and TensorRT-LLM (Xie et al., 26 Aug 2025). For production agent systems, it achieves typical p50 latencies <400 ms for schema-constrained hops and 10–30× lower energy per request (via quantization and cache minimization) (Sharma et al., 4 Oct 2025).

Principal limitations include:

• Single-replica architecture (in base) with no built-in load balancing or distributed coordination; addressing multi-node deployments is an open direction.
• Pure LRU or lowest-priority eviction in memory management, with expensive recomputation upon resumption (improved by Strata/SGLang-LSM).
• No out-of-the-box grammar-constrained decoding (work in progress); integration into structured-output stacks is ongoing.
• Limited quantization/mixed-precision in earliest versions (now supported in production agentic stacks).

7. Production Deployment and Practical Guidance

SGLang provides modular YAML-based configuration, allowing batch sizing, constraint selection, quantization scheme, validator insertion, and fallback policies. Production deployment is structured via shadow, blue/green, and rollback patterns based on real-time SLA metrics. CI-driven schema fuzzing, dynamic adapter refresh, and programmable interface hooks enable closed-loop damage recovery and safe evolutionary upgrades (Sharma et al., 4 Oct 2025).

Hierarchical cache warmup, LSM-tree window adjustment, and I/O bandwidth capping are critical for scaling SGLang-LSM in SSD-backed infrastructures (Yu et al., 20 Nov 2025).

SGLang thus constitutes a foundational technology for performant, scalable, and programmable LLM program execution, powering both current agent stacks and the next generation of context- and latency-aware inference platforms.