DeepInfer vs SGLang: V4.1-Flash Serving Compared

DeepInfer and SGLang both target DeepSeek-V4.1-Flash, but they are at different layers. DeepInfer is DeepSeek's Apache-2.0 inference engine for one model, with an offline-only release. SGLang is a general high-performance serving framework with a preview V4.1-Flash recipe, an OpenAI-compatible API, PD disaggregation, HiCache, and a router. There is no public head-to-head; this page compares scope and fit.

The short answer

If you want the most complete V4.1-Flash serving path today, start with SGLang — but read the preview caveat carefully. Its V4.1 support uses preview Docker images and has not shipped in a stock SGLang release as of 2026-10-20. If you want DeepSeek's own engine, have SM100 or Ascend 950 hardware, and are prepared to build the service layer, evaluate DeepInfer.

This is not a “DeepInfer is faster” or “SGLang is faster” page. There is no public same-model, same-hardware, same-workload benchmark. SGLang's headline number and DeepInfer's charts are different instruments.

What each one is

DeepInfer

DeepInfer is DeepSeek's open-source inference framework for serving DeepSeek-V4.1-Flash at scale. It is Apache-2.0, supports NVIDIA SM100 and Huawei Ascend 950, and ships an offline engine with optimized kernels, Wide-EP/EPLB, DSpark, SWA bounded replay, asynchronous scheduling, deterministic modes, and structured outputs.

Its release omits the KV cache system, prefill/decode disaggregation, and data-parallel load balancing. It supports only one model. The value is the DeepSeek-specific tuning and the visibility into a production inference stack — not a turnkey API.

SGLang

SGLang is a general high-performance serving framework. Its V4.1-Flash cookbook documents a preview image, DSpark speculative decoding, PD disaggregation, HiCache (hierarchical KV caching), SWA bounded replay, Engram, an OpenAI-compatible API, and a cache-aware router. The day-0 post lists NVIDIA Blackwell/Hopper and AMD MI350X among the validated hardware paths.

The important caveat: SGLang says V4.1-Flash support “has not shipped in an SGLang release yet,” so a stock pip install sglang cannot serve it. The cookbook uses preview Docker images. That is a real operational risk, not a footnote.

Head-to-head: scope, not speed

Sources: DeepInfer README/Q&A; SGLang V4.1 cookbook and day-0 post. Accessed 2026-10-20.

Where DeepInfer wins

• DeepSeek's own tuning. The engine is built around V4.1's CED architecture, DSpark, SWA bounded replay, and Wide-EP decode. It is the closest thing to DeepSeek's production compute path that is public.
• Ascend 950 in the official release. SGLang has AMD and NVIDIA paths; DeepInfer lists Huawei Ascend 950 alongside SM100. If Ascend is your platform, that matters.
• Deterministic modes. Batch-invariant inference and deterministic sampling (NVIDIA only) are available. SGLang's V4.1 cookbook says its backend currently refuses deterministic inference and is not bitwise stable across batch composition.
• Simplicity of scope. One model, one engine, no general-framework compatibility layer. For a research deployment, that can be easier to reason about.

Where SGLang wins

• A complete serving layer. OpenAI-compatible API, PD disaggregation, HiCache, prefix caching, and a router are documented parts of the V4.1 recipe. DeepInfer leaves those as integration work.
• More hardware options. The V4.1 cookbook covers NVIDIA B200/B300/GB300/H200 and AMD MI350X; DeepInfer's official list is SM100 and Ascend 950.
• Operational tooling. The cookbook gives complete launch commands for low-latency and high-throughput recipes, plus agentic long-context configurations. That is a faster path from zero to a running service.
• Mature general framework. SGLang has a broader community, more integrations, and a longer production history than a launch-day engine.

The benchmark trap

SGLang's day-0 post reports 873.63 tokens/s at batch size 1 on 4× GB300 with DSpark and a simulated acceptance length of 5.5. That number is a single-stream decode result under a specific configuration. It is not a serving throughput number, and it is not a DeepInfer comparison.

DeepInfer's decode chart uses 64 SM100 GPUs, a 131,072-token context, nextn=3, and a 75% draft-acceptance assumption. Its axes are output throughput vs per-user speed. SGLang's number and DeepInfer's chart cannot be placed on the same leaderboard without normalizing model, hardware, context, acceptance rate, and concurrency.

The useful comparison is a controlled one: same checkpoint, same GPU count, same input/output distribution, same cache hit rate, same SLO. Report TTFT and TPOT at p50/p95/p99, throughput under a latency constraint, and cost per million delivered output tokens. Neither vendor has published that for the other engine. We have not run either engine on identical hardware; this is a source-based comparison.

The preview caveat

“SGLang supports V4.1-Flash” is true, but incomplete. The cookbook says support has not shipped in an SGLang release; it uses preview Docker images. That means:

• You are tracking a moving image, not a pinned stable release.
• Flags and backend behavior can change between preview builds.
• You should pin the image digest and test upgrades like a production dependency.

DeepInfer has the opposite caveat: it is a stable code drop but a very early project, with three public commits at launch and no release tag. Both are early in different ways. Do not mistake “preview” for “unusable,” or “official release” for “mature.”

Decision guide

FAQ

Sources

• DeepInfer README — scope, hardware, missing components, charts.
• DeepInfer Q&A — production architecture and cache design.
• SGLang V4.1-Flash cookbook — preview image, DSpark, PD, HiCache, router, hardware.
• SGLang day-0 post — feature and hardware coverage.
• SGLang kernel optimization post — 873.63 tokens/s claim and configuration.
• DeepInfer Benchmarks — how to read the official charts.

Next: DeepInfer vs vLLM, DeepInfer vs TensorRT-LLM, or the comparison hub.