The SGLang team has publicly validated its engine against DeepSeek’s proprietary inference framework, DeepInfer, by running DeepSeek’s own benchmark workloads on identical hardware. The results show SGLang achieving up to 1.04x prefill throughput on 4x GB300 and 4x B300 nodes, while delivering decode steps 1.1% to 7.4% shorter on a 64x GB300 cluster. This parity is significant because DeepInfer is the engine DeepSeek runs behind its own service, having recently open-sourced it alongside its specialized GPU kernels.

Kernel Integration and Architectural Parity

SGLang now executes the full suite of DeepSeek’s optimized kernels, including FlashMLA, DeepGEMM, DeepEP, DeepSelect, and the TileLang kernels. The integration extends to the Mega series—MegaAttention, MegaMoE, MegaGate, and Mega-mHC—which fuse token dispatch, expert GEMMs, and combine operations into single kernels. On B300 and GB300 hardware, MegaAttention serves as the default path for both prefill and decode, while DSpark speculative decoding and SWA bounded replay mechanisms operate above these kernels to reduce target-model forward passes and manage sliding-window KV caches efficiently.

Methodological Differences in Measurement

While the hardware was consistent, the measurement methodologies diverged in ways that affect direct comparison. DeepInfer timed approximately nine prefill batches on GPU 0, repeating the same prompt per GPU, whereas SGLang averaged every complete batch across all GPUs between 20% and 90% request completion, using different new tokens per request. Configuration differences also played a role; SGLang kept Engram tables on device and used a draft model with no redundant experts, while DeepInfer ran tables in host memory and included 64 redundant experts in its draft model. These choices impact memory pressure and verification costs, meaning the charts reflect distinct operational priorities rather than pure engine superiority.

The Capacity Question Beyond Throughput

DeepInfer’s README explicitly lists KV cache systems, prefill/decode disaggregation, and load balancing across data-parallel replicas as requirements for a complete inference service. SGLang ships all three, utilizing a unified radix cache and Linker to connect L1 GPU caches to shared L3 storage like 3FS or Mooncake. For serving teams, the real decision lies in capacity planning: where the KV cache tier lives, whether the draft model carries redundant experts, and how the gateway handles replicas with different cached prefixes. The benchmark results confirm SGLang is a viable alternative, but the operational burden of managing disaggregated infrastructure remains the critical factor for production deployment.

Key Takeaways

  • SGLang matches DeepInfer’s prefill throughput (up to 1.04x) and outperforms it in decode steps (1.1%-7.4% shorter) on GB300/B300 clusters.
  • The comparison highlights methodological differences, with SGLang using device-resident Engram tables and no redundant experts in its draft model versus DeepInfer’s host-memory tables and 64 redundant experts.
  • SGLang has integrated DeepSeek’s full kernel stack, including FlashMLA, DeepGEMM, and the Mega series, enabling direct performance parity.
  • Production viability depends on implementing the three complete-service requirements: KV cache systems, prefill/decode disaggregation, and load balancing across replicas.

The Bottom Line

SGLang has successfully neutralized DeepInfer’s performance advantage by integrating its full kernel stack, but the real competitive edge now lies in operational simplicity. While raw throughput is comparable, the choice between engines should hinge on which team can more efficiently manage the complex capacity decisions around KV cache tiers and disaggregated infrastructure.