SGLang v0.4 & RadixAttention 2.0: Multi-Node Speculative Decoding, Structured JSON & FP8 Kernels
In late October 2026, the high-throughput LLM runtime landscape took a major leap forward with the release of SGLang v0.4, introducing distributed KV cache sharing and sub-millisecond constrained decoding.
Executive Architecture Summary: SGLang v0.4
As foundation models scale in operational deployment, the primary bottlenecks in production inference have shifted from raw compute matrix multiplications to Memory-Bound Key-Value (KV) cache orchestration and inter-token scheduling latency. The release of SGLang v0.4 directly addresses this inflection point, overhauling its core runtime with RadixAttention 2.0, multi-node disaggregated prefill-decode clustering, and native FP8 GEMM kernels powered by FlashInfer.
Benchmark evaluations conducted on NVIDIA H100 and B200 clusters indicate a 3.4x improvement in throughput for complex multi-turn conversational agents and structured JSON extraction workloads compared to legacy v0.3 releases, establishing SGLang as a formidable rival to vLLM v1 in enterprise microservices.
1. RadixAttention 2.0: Distributed Prefix Trees & Cross-Node KV Eviction
RadixAttention has historically excelled at retaining prefix trees across conversational sessions. In version 0.4, RadixAttention evolves into a distributed architecture capable of operating across disaggregated memory pools via Remote Direct Memory Access (RDMA). Rather than discarding evicted KV caches to host DRAM when GPU high-bandwidth memory (HBM) saturates, RadixAttention 2.0 streams compressed cache pages across 800Gbps InfiniBand fabrics to remote dedicated storage nodes.
When an agent initiates an extended retrieval query sharing identical system prompts, tool schemas, or few-shot demonstration tokens, the engine reconstitutes the KV states via zero-copy GPU kernel pointers in under 45 microseconds. This eliminates redundant prefill compute, reducing Time to First Token (TTFT) by up to 88% on prompts exceeding 32,000 tokens.
2. Multi-Candidate Speculative Decoding with EAGLE-2 Kernels
Speculative decoding has frequently suffered from draft verification bottlenecks on smaller batch sizes. SGLang v0.4 incorporates native tree-structured draft verification utilizing EAGLE-2 regression heads. By predicting feature vectors rather than discrete tokens at the draft phase, the system generates multi-token candidate branches that are verified in a single forward pass through the target model.
On Llama 3.3 70B running across 4x H100 SXM5 nodes, SGLang v0.4 achieves an average acceptance rate of 3.8 tokens per step, propelling single-stream generation speeds to 164 tokens per second while maintaining mathematical parity with autoregressive outputs.
3. Native Structured JSON Regex Compilation via Outlines Engine
Enterprise voice bots and autonomous workflows rely heavily on constrained decoding to guarantee valid JSON responses. Traditional post-processing parsers fail under non-deterministic outputs, while early logit-masking implementations introduced massive CPU latency penalties. SGLang v0.4 embeds a compiled C++ finite-state machine (FSM) directly into the FlashInfer attention loop.
By compiling Pydantic schemas into bitmasked lookup tables ahead of execution, token validity masks are evaluated concurrently with attention projection kernels, adding less than 1.8 microseconds of latency per decoding step regardless of schema complexity.
Production Benchmark Metrics: SGLang v0.4 vs. Competitors
| Inference Engine | Prefill Latency (32K Tokens) | Decoding Throughput (Tokens/s) | Memory Footprint (KV Cache) | Constrained JSON Overhead |
|---|---|---|---|---|
| SGLang v0.4 | 142 ms | 4,820 tok/s | Optimized (Radix 2.0 FP8) | +1.8 μs/token |
| vLLM v1 (C++ Core) | 158 ms | 4,650 tok/s | Optimized (PagedAttn v3) | +4.2 μs/token |
| TensorRT-LLM 0.14 | 136 ms | 4,910 tok/s | Static Allocation | +12.6 μs/token |
| HuggingFace TGI | 410 ms | 2,150 tok/s | Standard Paged Cache | +28.0 μs/token |
FinOps & Infrastructure Sizing Recommendations
For organizations deploying production API endpoints, adopting SGLang v0.4 unlocks substantial operational expenditure reductions. By deploying FP8 weight quantization paired with RadixAttention 2.0 disaggregation, infrastructure teams can host dual 70B parameter models on a single 8x H100 node that previously supported only one, effectively cutting server rental costs by 50% while scaling peak concurrent request capacity by 240%.