# SGLang 0.4 made scheduling overlap, cache locality and constrained output distinct performance claims SGLang released v0.4 on December 4, 2024 with a zero-overhead batch scheduler, cache-aware load balancing, DeepSeek attention work and xgrammar structured output. Canonical: https://rangoon.ai/insights/archive-sglang-04-december-2024/ Author: Rangoon Editorial (https://rangoon.ai/insights/editorial/) Published: 2026-10-23 Updated: 2026-10-23 Event date: 2024-12-04 Tags: [SGLang](https://rangoon.ai/insights/tags/sglang/), [LLM inference](https://rangoon.ai/insights/tags/llm-inference/), [structured output](https://rangoon.ai/insights/tags/structured-output/), [KV cache](https://rangoon.ai/insights/tags/kv-cache/) Source note: Historical reporting: the SGLang team published the v0.4 release post on December 4, 2024. Reported speedups are source-specific benchmark claims. Image: Inference bottleneck map. Original Rangoon explanatory diagram: concepts for editorial review, not measured results, an official legal determination, vendor interface or event photograph. ## Key takeaways - v0.4 separated CPU scheduling, cache routing and grammar-constrained decoding work. - Reported speedups depend on model, hardware and benchmark inputs. - Structured-output performance needs both validity and latency measurement. ## The December release named several different serving optimizations [source 1](https://www.lmsys.org/blog/2024-12-04-sglang-v0-4/) [source 2](https://blog.mlc.ai/2024/11/22/achieving-efficient-flexible-portable-structured-generation-with-xgrammar) The SGLang team published its v0.4 release on December 4, 2024. The post named a zero-overhead batch scheduler, cache-aware load balancer, data-parallel attention for DeepSeek models and xgrammar for faster structured outputs. It reported measured improvements including scheduler, cache and structured-generation figures under its selected setups. MLC Community’s November XGrammar document supplies a substantive account of the integrated grammar technology. These are valuable engineering details, but they are not portable promises about another model, accelerator, traffic shape or grammar. The source’s four feature areas make v0.4 a useful case study in decomposed inference work. Scheduler overlap, prefix locality, model-specific attention parallelism and grammar masking are not interchangeable improvements. Historical reporting should preserve their separate conditions instead of turning a release post into one undifferentiated throughput claim. ## Each optimization acts on a different bottleneck [source 1](https://www.lmsys.org/blog/2024-12-04-sglang-v0-4/) [source 2](https://blog.mlc.ai/2024/11/22/achieving-efficient-flexible-portable-structured-generation-with-xgrammar) Overlapping CPU scheduling with GPU computation targets scheduling overhead; cache-aware balancing targets reuse of previously computed prefix state; data-parallel attention targets a model-architecture-specific memory and communication issue; grammar backends target constrained decoding. Combining their reported gains as one speed number loses the diagnosis. A serving team should first measure whether it is CPU-bound, memory-bound, queue-bound, bandwidth-bound or constrained-decoding-bound before choosing an intervention. The XGrammar paper supplies a second substantive lens for the constrained-output feature: JSON Schema and context-free grammar constraints differ in expressiveness and masking cost. When a product needs nested or recursive output, grammar choice can become a throughput and correctness issue. The SGLang release’s xgrammar integration should therefore be tested against the actual structure, not plain text generation. ## Benchmarks must preserve request and grammar shape [source 1](https://www.lmsys.org/blog/2024-12-04-sglang-v0-4/) [source 2](https://blog.mlc.ai/2024/11/22/achieving-efficient-flexible-portable-structured-generation-with-xgrammar) Record model revision, quantization, GPU count, tensor or data parallelism, prompt lengths, output lengths, concurrency, cache state, grammar complexity and warmup policy. Verify structured output against the expected schema as well as measuring token rate; a fast invalid JSON response is not an improvement. Test cache eviction and an uneven request distribution, since a cache-aware policy can work well for repeated prefixes while offering little benefit for unique requests. Add a grammar-rejection fixture with deliberately invalid model tendencies, such as an extra field, a malformed enum or an unclosed nested structure. Measure both whether the decoder prevents the output and how much latency it adds. This reveals the real contract: valid output under a known constraint, rather than a generic claim that structured generation is faster. ## Analysis: publish a bottleneck-to-control map with results [source 1](https://www.lmsys.org/blog/2024-12-04-sglang-v0-4/) [source 2](https://blog.mlc.ai/2024/11/22/achieving-efficient-flexible-portable-structured-generation-with-xgrammar) Attach every benchmark result to the bottleneck it intends to address and the fallback if assumptions fail. For example, a grammar path needs a plain-generation fallback and a validation error route; a cache policy needs a cold-start capacity plan. This helps an operator reason about performance without repeating supplier-style headline numbers. The original release establishes the historical technical claims; the mapping is editorial systems analysis. Do not combine a cache-hit improvement from repeated prompts with a grammar throughput result from a different model into one business estimate. The relevant denominator is a production request mix with its own structure and reuse rate. Separate plots for cold versus warm requests and constrained versus unconstrained decoding produce a clearer decision record. ## Sources 1. [LMSYS: SGLang v0.4 release](https://www.lmsys.org/blog/2024-12-04-sglang-v0-4/) 2. [MLC Community: XGrammar structured generation](https://blog.mlc.ai/2024/11/22/achieving-efficient-flexible-portable-structured-generation-with-xgrammar) ## Related reading - [NVIDIA’s memory codesign post highlights a practical boundary between capacity and usable agent throughput](https://rangoon.ai/insights/nvidia-memory-full-stack-codesign-2026/) - [SGLang 0.5.1 added cache and scheduling work to its serving release](https://rangoon.ai/insights/archive-sglang-0-5-1-hicache-august-2025/) - [NVIDIA Dynamo replaced the unsupported Docker Sandboxes prompt with a verifiable reasoning-models inference event](https://rangoon.ai/insights/archive-nvidia-dynamo-inference-march-2025/) ## More information - [Documentation index](https://rangoon.ai/llms.txt) - [AI access and policies](https://rangoon.ai/ai/) - [Source repository](https://github.com/hypler-dev/rangoon)