Sglang
SGLang is the open-source serving engine for LLMs, multimodal and diffusion models, tuned for high throughput on NVIDIA, AMD, TPU, NPU and CPU hardware.
If you already pay for GPUs and want more tokens per second out of them, SGLang belongs in a head-to-head benchmark against vLLM on your own traffic. The schedulers and kernel work are the draw: disaggregated prefill/decode, speculative decoding, the zero-overhead scheduler, and per-release wins like a 1.93x faster prefill from breakable CUDA graphs or DeepSeek-V4.1 Flash going from 35 to 873 tokens/s. The breadth is real too — NVIDIA, AMD, TPU, Ascend NPU and XPU from one engine. The catch is unchanged: everything above the kernel is your problem. Choose it for control and throughput, and pick a managed endpoint like Baseten or Anyscale when you would rather not run the cluster.
Verified 6d ago · liveness 65/100 · cite: rightaichoice.com/tools/sglang
- Platform and ML engineers serving open-weight models on their own GPU clusters
- Teams optimizing tokens-per-second and time-to-first-token on mixed prompt workloads
- Deployments spanning mixed or non-NVIDIA hardware: AMD, TPU, Ascend NPU, XPU, CPU
- Serving very large or long-context models such as Kimi K3
- Teams without infrastructure engineers to run, tune and maintain a self-hosted server
- Buyers who need a managed endpoint with an uptime SLA and a vendor support contract
- Anyone expecting built-in fine-tuning, training or data labeling in the same tool
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Skip SGLang if you want a managed endpoint with an uptime SLA and a support contract, or if nobody on your team wants to own GPU capacity planning, parallelism tuning and upgrades.
You pay the accelerator bill, not a licence fee — idle GPUs cost the same as busy ones, so throughput per dollar is the number that matters.
SGLang itself is open-source software, so the cost comparison is your hardware and engineering time versus a managed inference endpoint. Teams with existing GPU capacity come out far ahead on tokens per dollar; teams without GPUs should price Baseten, Anyscale or another managed server against the fully loaded cost of buying and staffing the cluster.
In short
Sglang — SGLang is the open-source serving engine for LLMs, multimodal and diffusion models, tuned for high throughput on NVIDIA, AMD, TPU, NPU and CPU hardware. Best for Platform and ML engineers serving open-weight models on their own GPU clusters, Teams optimizing tokens-per-second and time-to-first-token on mixed prompt workloads, Deployments spanning mixed or non-NVIDIA hardware: AMD, TPU, Ascend NPU, XPU, CPU. Free to use.
What's new in Sglang
Checked 6 days agoAcross the latest 5 updates: 4 feature updates and 1 launch.
Unified Radix Cache in SGLang: one tree for hybrid model prefix caching
SGLang detailed a unified radix cache that consolidates prefix caching for hybrid models into a single tree. On DeepSeek-V4-Flash with a shared system prompt, token hit rate rose from 43.8%.
RLinf x SGLang: Integrating Cosmos3 from Fine-Tuning to Efficient Parallel Evaluation
RLinf and SGLang integrated Cosmos3, covering the path from fine-tuning through efficient parallel evaluation.
DeepSeek-V4.1 Flash on SGLang: from 35 to 873 tokens/s
SGLang reported DeepSeek-V4.1 Flash throughput rising from 35 to 873 tokens/s on the project's serving stack.
SGLang and Miles Add Day-0 Support for DeepSeek-V4.1
SGLang and Miles shipped day-0 support for DeepSeek-V4.1, making it servable on release day.
Breakable CUDA Graph in SGLang: 5x faster graph builds, 1.93x faster prefill
Breakable CUDA Graph in SGLang cuts graph build time 5x and speeds prefill 1.93x on the tested configuration.
What people actually say about Sglang — is it worth it?
We ran a structured research pass across product reviews, community discussions, and post-purchase forum threads to surface the patterns vendors won't publish themselves. Below: the recurring strengths, the hidden costs people mention most, and the cohort that consistently regrets adopting this tool.
35 mentions across 2 sources (Hacker News, Lemmy) · researched Jul 3, 2026.
Average across the 2 sources that answered — each source counts once, not each post.
- +Top-tier inference engine alongside vLLM and llama.cpp.
- +Broad hardware support: NVIDIA, AMD, CPU, TPU, Ascend.
- +Advanced optimizations like disaggregated prefill/decode and speculative decoding.
- +OpenAI-compatible API makes integration straightforward.
- +Active development with patches for new models like IndexCache.
- −Steeper learning curve than Ollama for beginners.
- −Smaller community than vLLM, fewer tutorials and plugins.
- −Documentation can be sparse for advanced features or edge-cases.
- −Occasional instability with very new or proprietary models.
- −OpenAI API compatibility is not always byte-identical.
- • GPU/TPU compute costs
- • Engineering time for tuning
Viability Score
How well maintained and how widely used is Sglang? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: October 2026
How we score →Key Features
- Open-source inference serving for LLMs, multimodal and diffusion models
- Day-0 support for DeepSeek-V4.1 and Kimi K3 (2.8T parameters, 1M context)
- Disaggregated prefill/decode serving pipeline
- Speculative decoding to cut generation latency
- Zero-overhead scheduler for reduced host-side overhead
- Optimized GPU kernels including FlashInfer MoE and MLA backends
- Runs on NVIDIA GPUs, AMD GPUs, CPU servers, TPU, Ascend NPUs, XPU
- Supports DeepSeek, Qwen, GPT-OSS, Llama, Mistral, GLM models
- Diffusion model serving: FLUX 3, Qwen-Image 2.1, Ming-Image 0.1
- OpenAI-compatible API endpoints for drop-in client compatibility
- Beam search returning the n best sequences per request
- Unified radix tree prefix caching for hybrid models
- Multi-node and multi-GPU distributed inference
- Distributed chunked prefill (DCP) for long-context workloads
- Chunked pipeline parallelism and tensor/expert/context parallelism
About Sglang
SGLang is an open-source serving framework for large language and multimodal models, aimed at teams that run their own accelerators and measure success in tokens per second per dollar. It scales from a single GPU to multi-node clusters, and the project lists NVIDIA GPUs, AMD GPUs, CPU servers, TPU, Ascend NPUs and XPU as supported hardware — one engine across silicon you already own. The model roster is broad and open-weight first: DeepSeek, Qwen, GPT-OSS, Llama, Mistral and GLM are named on the site, plus diffusion models such as FLUX 3, Qwen-Image 2.1 and LongCat-Image-Edit. Release v0.5.21 (Oct 2, 2026) added DeepSeek-V4.1 Flash, GigaChat 3.5, MiMo-V2.6 and Ling-3.0-flash-VL, following day-0 support for DeepSeek-V4.1 and for Kimi K3, a hybrid model at 2.8T parameters with a 1M context length. The engineering is where the speed comes from: disaggregated prefill/decode puts the two phases on separate resources, speculative decoding shortens generation, a zero-overhead scheduler keeps host work off the critical path, and kernel work plus configurable parallelism (tensor, pipeline, data, expert and context parallel) does the rest. Recent releases added a unified radix tree for hybrid-model prefix caching, a CPU-only simulator that predicts time-to-first-token within roughly 6% on most traces, DSpark draft-KV transfer under disaggregated context-parallel serving, and a breakable CUDA graph that cut graph build time 5x and prefill 1.93x. Install is a uv/pip or Docker step, then one command launches the server; you query standard OpenAI-compatible endpoints, so most client code carries over. The trade is explicit: you own capacity planning, upgrades and the pager. Managed endpoints such as Baseten or Anyscale take that off your plate; vLLM is the nearest open-source rival, and which one wins shifts per release and per workload.
Behind the Verdict
SGLang's pitch is narrow and honest: open weights, your hardware, no managed layer in between. What you get for that is a serving engine that keeps landing concrete performance work rather than roadmap slides. Disaggregated prefill/decode separates the compute-bound prefill from the memory-bound decode so each can scale on its own terms. Speculative decoding, chunked pipeline parallelism and distributed chunked prefill handle the awkward cases — very large models and long context. The scheduler is designed so host overhead stays off the critical path, which matters most when a workload is latency-sensitive at high concurrency. The release notes are the clearest evidence of where effort goes. v0.5.21 (Oct 2, 2026) shipped 779 PRs from 227 contributors and added DeepSeek-V4.1 Flash, GigaChat 3.5, MiMo-V2.6, Ling-3.0-flash-VL and several diffusion models. v0.5.20 added a CPU-only simulator that runs the real scheduler, radix cache and hierarchical cache with a latency predictor, predicting TTFT within roughly 6% on most measured traces. Unified radix tree caching lifted token hit rate from 43.8% on DeepSeek-V4-Flash with a shared system prompt. W4A8 MoE quantization on Hopper gained about 12% output throughput on DeepSeek-V4-Flash with no GSM8K change. DSpark draft KV can now transfer from a context-parallel prefill to a parallel decode, verified on 8x B300 over NIXL and Mooncake up to 256K input. The honest weaknesses are structural, not defects. This is infrastructure software: you provision GPUs, tune parallelism, and absorb upgrades yourself — no uptime SLA, no vendor support contract, no fine-tuning or data labeling in the box. Compatibility caveats exist and shift between releases: beam search does not yet mix with speculative decoding, disaggregation or data parallelism; the responses API only stores results when the server is started with an explicit flag, and PD deployments cannot enable it; prefill context parallelism v1 was removed and HIP, NPU and MUSA prefill CP is rejected until ported. Very large or long-context models generally demand multi-node setups and careful tuning. None of that is a reason to avoid it — it is the price of the control and throughput you came for.
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Real-world workflow fit
Concrete scenarios for the personas Sglang actually fits — and what changes day-one when you adopt it.
Install SGLang from the uv/pip index or the lmsysorg/sglang Docker image for CUDA 12.9, launch one server against a Qwen or DeepSeek checkpoint, and point existing OpenAI-compatible client code at it.
Outcome: A working endpoint on hardware you already own, with no change to application code and no managed vendor in the request path.
Benchmark speculative decoding and disaggregated prefill/decode against your real prompt mix, then use the CPU-only simulator to predict TTFT across configurations without burning GPU hours.
Outcome: A configuration chosen on measured tokens-per-second and time-to-first-token instead of vendor benchmarks, with predicted TTFT within roughly 6% on most traces.
Deploy a hybrid model such as Kimi K3 (2.8T parameters, 1M context) with distributed chunked prefill and chunked pipeline parallelism across multiple nodes.
Outcome: Long-context requests served from open weights on your own cluster rather than through a managed endpoint.
Use Cases
- Serve open-weight LLMs for real-time chat at low latency on your own GPUs
- Run high-throughput batch text generation across a multi-GPU or multi-node cluster
- Serve multimodal models that handle both text and images at scale
- Deploy very large long-context hybrid models such as Kimi K3 (2.8T params, 1M context)
- Benchmark TTFT and tokens-per-second for candidate open models behind one interface
- Drop SGLang behind existing apps via OpenAI-compatible endpoints
- Plan cluster capacity with the CPU-only simulator before committing hardware
- Serve diffusion image models alongside LLMs from a single engine
Models Under the Hood
as of 2026-09-23
Limitations
- SGLang is self-hosted open-source serving software, so you provision and manage the accelerators it runs on.
- Getting to first request means installing via uv/pip or Docker and launching a server pointed at a model — expect real technical work.
- Very large or long-context models generally need multi-node or multi-GPU distributed setups and careful parallelism tuning.
- Support runs through community channels: GitHub, Slack, Discord and Discussions rather than a contracted SLA.
- Feature combinations have caveats that move between releases: beam search does not yet mix with speculative decoding, disaggregation or data parallelism; the responses API retains results only when the server starts with --enable-response-store, and PD deployments cannot enable it; prefill context parallelism v1 was removed, and HIP, NPU and MUSA prefill CP is rejected until those platforms are ported.
- There is no fine-tuning, training or data-labeling layer in the box.
as of 2026-10-02
Verification history
We have re-verified Sglang 7 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-checked, vendor evidence unchanged
- — re-checked, vendor evidence unchanged
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
Showing the 6 most recent of 7 verification passes.
Free to cite with attribution — this page re-verifies continuously.
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Where the pricing makes sense
The company stage and team size where Sglang's pricing actually pencils out — and where peers do it cheaper.
SGLang itself is open-source software, so the cost comparison is your hardware and engineering time versus a managed inference endpoint. Teams with existing GPU capacity come out far ahead on tokens per dollar; teams without GPUs should price Baseten, Anyscale or another managed server against the fully loaded cost of buying and staffing the cluster.
Setup time & first value
How long it actually takes to get something useful out of Sglang — broken out by persona, not the marketing-page minute.
One engineer with GPU access: install via uv/pip or Docker and launch a single-GPU server in under an hour. A tuned multi-GPU production deployment on your own traffic is a multi-day benchmark-and-tune exercise. Multi-node or very large model deployments add cluster and networking work measured in weeks.
Switching to or from Sglang
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From vLLM: reuse your OpenAI-compatible client code and rerun your benchmark suite against SGLang to compare tokens-per-second on the same prompts.
- →From a managed inference endpoint (Baseten, Anyscale, Together): point client code at a self-hosted OpenAI-compatible base URL once the server is running.
- →From a hand-rolled Hugging Face Transformers server: replace the serving loop with SGLang and keep the model weights and tokenizer.
- →From TensorRT-LLM: rebuild your deployment around SGLang's parallelism and scheduler options, verifying kernel backends on your target GPU.
- ↗To vLLM: swap the serving engine behind the same OpenAI-compatible endpoints and re-benchmark on your traffic.
- ↗To a managed endpoint (Baseten, Anyscale): move weights behind a hosted API when you no longer want to run the cluster.
- ↗To a proprietary API (OpenAI, Anthropic, Google): only when open weights stop fitting your latency, quality or compliance needs.
- ↗To another open server (LMDeploy, TGI, TensorRT-LLM): keep the API surface and change the engine under it.
Resources & Guides
Tutorials & Learning
YouTube returned 6 videos for “Sglang”, and we withheld 6: 6 could not be judged, because “Sglang” is a single word that other videos use for other things. We are showing none, because we could not prove any of them are about Sglang.
Official links
Tools that pair well with Sglang
Common stack mates teams adopt alongside Sglang, with the specific reason each pairing earns its keep.
Wafer Pass
Wafer Pass sells flat-rate inference on open models — GLM, Qwen, DeepSeek, and Kimi — on a serving stack the company keeps retuning for your traffic.
Mistral
Mistral sells sovereign AI: frontier open-weight models you can own, self-host, or run on EU inference.
Lemonade
A private AI assistant that runs on your own computer to search, analyze, and draft from your files.
Featured Head-to-Head Comparisons
Sglang vs Spider Cloud
Do not compare them as alternatives; they solve fundamentally different problems. Choose Spider Cloud if you need to collect web data for AI agents or RAG pipelines. Choose SGLang if you need to serve LLMs efficiently on your own hardware. If both are needed, use Spider Cloud to feed data into models served by SGLang.
Sglang vs Voyage Ai
For teams building enterprise RAG pipelines with domain-specific embedding needs (finance, legal), Voyage AI offers specialized models and long-context support, but requires a sales conversation. SGLang is the clear choice for developers needing high-throughput, self-hosted LLM inference on diverse hardware—it's free, open-source, and excels at serving open models. Choose based on whether your bottleneck is embedding accuracy or inference performance.
Sglang vs Temporal Ai
For teams building reliable AI agents that need crash-proof execution and human-in-the-loop, Temporal AI is the clear choice. For developers deploying LLMs with maximum throughput and supporting many hardware backends, SGLang is unmatched. These tools are complementary: SGLang serves the model, Temporal orchestrates the workflow around it.
Alternatives to Sglang
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Wafer Pass
Wafer Pass sells flat-rate inference on open models — GLM, Qwen, DeepSeek, and Kimi — on a serving stack the company keeps retuning for your traffic.
Frequently Asked Questions
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