Comparison of SGLang and vLLM Inference Frameworks

Source:https://chatgpt.com/share/67c04e2b-ced8-800c-ac26-0c6b89aa697c

I'll compare sglang and vllm in terms of performance (speed, memory efficiency, throughput) and features (model support, batching, fine-tuning, etc.). I'll gather insights on benchmarks, optimizations, and unique functionalities of both frameworks. I'll update you once I have the findings.

Overview

SGLang and vLLM are both high-performance inference frameworks for large language models (LLMs). They focus on maximizing throughput and efficiency when serving models like Llama, Mistral, and others. Below, we compare their performance characteristics and feature sets, highlighting practical use cases, advantages, and trade-offs.

Performance

  • Speed (Latency & Response Time): Both frameworks aim for low latency, but SGLang often achieves faster time-to-first-token in single-request scenarios. In benchmarks, SGLang delivered lower latency (faster first token) than alternatives – about 22% faster TTFT than the slowest competitor in one testwww.clarifai.com. This is partly due to an “overhead-free” CPU scheduler in SGLang that minimizes per-request processing delaysgithub.com. vLLM also reduces latency via continuous batching, which merges incoming requests so the GPU is never idlewww.hyperstack.cloud. In fact, vLLM’s batching strategy can reduce p50 latency even as throughput scaleswww.anyscale.comwww.hyperstack.cloud. Both can serve interactive applications (chatbots, etc.) with prompt responses, but SGLang’s design favors minimal scheduling overhead, giving it an edge in immediate response time under light loadswww.clarifai.com. vLLM’s latency shines when many concurrent requests are batched, avoiding the delays of traditional batchingwww.hyperstack.cloud.

  • Memory Efficiency: vLLM was designed with memory optimization as a core feature. Its PagedAttention algorithm dynamically allocates GPU memory for the KV cache, drastically reducing wasteblog.runpod.io. Traditional systems might waste 60–80% of KV cache memory, whereas vLLM keeps waste under 4%blog.runpod.io. This near-optimal memory use means vLLM can support longer context windows and larger models on the same hardware, and serve more requests per GPU. SGLang adopts similar techniques – it implements “token attention (paged attention)” for efficient cache usagegithub.comand RadixAttention for automatic KV cache reuselmsys.orggithub.com. In practice, both frameworks avoid memory bloats; SGLang even supports running models in lower precision (FP8) to cut memory and improve speedlmsys.org. vLLM’s memory management is battle-tested (used by many companies to cut GPU count for serving)blog.runpod.io, whereas SGLang’s approach is comparably efficient but newer. For most deployments, memory footprint will be lower with either SGLang or vLLM compared to naive HuggingFace pipelines, thanks to these optimizations.

  • Throughput: High token generation throughput is a primary goal of both frameworks. SGLang often touts higher overall throughput in tokens/sec, especially for large models or heavy loads. In LMSYS tests, SGLang outperformed vLLM with up to 3.1× higher throughput on a Llama-70B modellmsys.org. Another report found SGLang had strong single-stream generation speed, producing results faster than other frameworks (e.g. ~8% more tokens/sec than vLLM in one benchmark)www.clarifai.com. vLLM, however, is no slouch – it dramatically improves throughput over vanilla implementations (e.g. 3.5× faster than Hugging Face’s TGI server in one case)blog.runpod.io. vLLM’s continuous batching keeps the GPU busy, reaching near maximal throughput especially under multi-request workloadswww.hyperstack.cloud. In fact, one study showed up to 23× throughput improvement in real-world settings by using vLLM’s batching vs. no batchingwww.anyscale.com. Recent benchmarks by the vLLM team (v0.6.0) claim that vLLM achieved the highest throughput among open-source engines on certain datasets, even edging out SGLang and others for complex or long-generation querieswww.hyperstack.cloud. The takeaway is that SGLang currently tends to lead in raw throughput for many scenarioslmsys.org, but vLLM’s latest optimizations have narrowed the gap, with each outperforming the other in specific caseswww.hyperstack.cloud. Throughput needs for most applications (e.g. serving many chatbot sessions in parallel) can be met by either, but SGLang may push slightly higher token/sec on the newest GPUs, whereas vLLM is extremely robust and scales well as load increases.

  • Scalability: Both frameworks scale to larger models and multi-GPU deployments, but their approaches differ. vLLM supports distributed inference, allowing deployment across multiple GPUs or nodes for serving bigger models or higher trafficwww.clarifai.com. It can leverage model parallelism or even work with accelerators like TPU and AWS Inferentia (via AWS Neuron), indicating broad scalability on various hardwarewww.clarifai.com. SGLang supports multi-GPU tensor parallelism to serve very large models (e.g. it can split a 405B-parameter model across 8 GPUs)lmsys.orggithub.com. In single-node scenarios, SGLang’s Python-based scheduler is highly efficient at batching many concurrent requests, and it continues to scale throughput as batch size growslmsys.org. Under extremely heavy loads (e.g. 100+ concurrent streams), SGLang maintains good latency for most models, though one report noted it struggled with a specific model (Mistral) at high concurrency, suggesting a need for further tuning for that architecturewww.clarifai.com. vLLM’s design has been proven in multi-tenant, high-concurrency environments – its continuous batching and async scheduling ensure the GPU remains utilized even as requests scale up, and recent versions introduced asynchronous CPU/GPU coordination to reduce bottlenecksblog.vllm.aiblog.vllm.ai. Both frameworks can be deployed on cloud clusters or on-prem rigs with multiple GPUs. In summary, vLLM offers strong multi-node and multi-GPU scaling out-of-the-box (with documented use in large clusters), while SGLang focuses on scaling up on a node with efficient parallelism (and is actively adding features for distributed setups). Each can serve increasing workloads, but vLLM’s broader hardware support (NVIDIA, AMD, Intel, etc.)www.clarifai.comgives it an edge in flexibility when scaling across different environments.

Features

  • Model Support: vLLM and SGLang both support a wide range of transformer-based LLMs. vLLM integrates with Hugging Face Transformers, so it can load popular architectures like Llama 2/3, Mistral, Falcon, GPT-NeoX, etc., as long as they are decoder-only (or even encoder-decoder to some extent)www.hyperstack.cloud. The vLLM docs specifically mention compatibility with models such as Llama 3.1, Mistral, Qwen-2 and other GPT-type modelswww.hyperstack.cloud. It also supports specialized model types: Mixture-of-Experts models, multi-modal vision-language models, and even embedding or reward models (meaning you could serve embedding generators or value models with it)www.clarifai.com. SGLang likewise is designed to be model-agnostic and has extensive model support for generative LMs: its documentation lists Llama (all sizes including Llama 3), Mistral, Qwen, OpenAI’s GPT models (if weights available), and moregithub.com. SGLang has also been used with vision-language models (e.g. it powered the LLaVA v1.6 visual chat demo) and supports multi-modal inputs nativelygithub.com. Both frameworks can load models in Hugging Face format (SGLang can directly download .safetensors from HF Hub given a model pathdev.to). In practice, if a model works with Hugging Face’s standard AutoModelForCausalLM, it will likely work with vLLM or SGLang. One difference: SGLang often provides day-one support for new research models (as noted by their quick integration of Meta’s latest Llama and DeepSpeed’s new model releases)github.com, which is beneficial if you want to deploy cutting-edge models quickly. vLLM’s broad hardware compatibility also means you could even run smaller models on CPU or different accelerators if neededwww.clarifai.com, whereas SGLang has mainly focused on GPU deployment (Linux OS) for nowdev.to.

  • Batching Capabilities: Both SGLang and vLLM excel at dynamic batching (continuous batching) to maximize throughput. vLLM pioneered “continuous batching,” which dynamically merges incoming requests at each generation stepwww.hyperstack.cloud. This means users don’t have to manually batch requests; vLLM’s scheduler will combine tokens from different requests on the fly, keeping the GPU pipeline full. The result is higher throughput and better GPU utilization even with many small or varied-length requestswww.hyperstack.cloud. This approach also avoids long wait times – instead of waiting to form a large batch, vLLM immediately schedules new token generation with whatever requests have arrived, improving latency for each requestwww.hyperstack.cloud. SGLang implements a similar concept via its efficient Python-based batch schedulerwww.clarifai.com. It continuously batches and re-batches requests each step, and the team claims it can match or outperform C++ batching implementations in efficiencywww.clarifai.com. SGLang uses RadixAttention and other techniques to allow reuse of computation among batched requests (e.g. if some share prefixes) and to organize token generation in a way that keeps GPUs busy. In essence, both frameworks relieve the user from manual batching – you can stream a single request or 100 requests, and the system will automatically utilize available capacity. This is ideal for deployment scenarios with unpredictable traffic. One point to note is that vLLM’s earlier versions had some CPU overhead in scheduling (due to a lot of Python logic per iteration)blog.vllm.aiblog.vllm.ai, but the latest release has optimized this, reducing scheduling overhead significantly. SGLang from the start emphasized an “overhead-free” scheduler in Python that minimizes such costsgithub.com. Both now achieve very low overhead per token iteration, which is crucial for smooth scaling. Bottom line: Both SGLang and vLLM handle batch inference extremely well. vLLM’s continuous batching has been proven to drastically improve real-world throughput (especially when many users send requests concurrently)www.anyscale.com, and SGLang’s continuous batch scheduler similarly allows it to maintain high token/sec even as request volume growslmsys.org. This makes either framework well-suited for services like AI chat APIs, where requests arrive asynchronously and need to be served together efficiently.

  • Fine-Tuning Support: These frameworks are primarily built for inference serving, not training, so fine-tuning is not their main function. Neither vLLM nor SGLang includes an out-of-the-box fine-tuning pipeline (you would fine-tune models using other tools, then load the resulting model into these engines for serving). That said, both can accommodate fine-tuned models and certain adapter techniques. vLLM, for example, supports loading LoRA (Low-Rank Adaptation) checkpoints dynamically into a base model at runtimegithub.com. This means if you have a base model and a LoRA fine-tuned weights file, vLLM’s server can apply the LoRA on the fly without requiring a separate merged model file (useful for quickly swapping in fine-tuned variants). SGLang does not explicitly advertise dynamic loading of adapters, but since it builds on PyTorch and Hugging Face model interfaces, you can likely load a fine-tuned model (or merged LoRA) by pointing SGLang to those weights. In practice, users treat these frameworks as serving backends: you fine-tune your LLM using frameworks like PyTorch/Accelerate or DeepSpeed, then use SGLang or vLLM to serve the optimized model. Neither framework performs further training while serving (aside from perhaps minor caching or prefix-tuning mechanisms). One advantage on SGLang’s side is that its frontend language could allow prompt-programming style adaptation – e.g. chaining a series of prompts or few-shot examples to steer a model for a task (not a model weight change, but an application-level fine-tuning). Meanwhile, vLLM’s strength is efficiency – serving a fine-tuned model with long context or many users very cost-effectively. Trade-off: If your workflow involves frequently updating model weights or on-the-fly fine-tuning in production, neither is designed for that (you’d swap out the model in the server when ready). But vLLM’s ability to hot-swap LoRA adapters may offer a bit more flexibility for rapid iteration in development. Overall, both frameworks focus on serving pre-trained or fine-tuned models, ensuring that any model (original or fine-tuned) runs as fast as possible rather than providing tools to do the fine-tuning itself.

  • API Design and Ease of Use: Both SGLang and vLLM can operate as a standalone server with an OpenAI-compatible REST API, making them easy to integrate into applications. vLLM provides a command-line tool (vllm serve) that launches a server implementing the OpenAI Completion/Chat API protocoldocs.vllm.ai. This means you can use OpenAI’s client libraries or any HTTP calls to interact with your local vLLM server as if it were OpenAI’s endpointdocs.vllm.ai. This design is very convenient for deploying a drop-in replacement – existing apps that use OpenAI’s API can be pointed to your vLLM server with minimal changes. SGLang similarly offers an OpenAI-compatible API server. After installing SGLang, you run python -m sglang.launch_server --model-path &lt;<model> to host a model, and SGLang exposes endpoints (/v1/completions, etc.) for completionslmsys.org. Users have reported that SGLang’s OpenAI-mimic endpoint works well for integration behind proxies or with existing toolswww.reddit.com. In terms of ease of use, vLLM is praised for being simple to set up — it’s a Python package that can load models from the Hugging Face Hub and requires no deep expertise to get runningwww.hyperstack.cloudwww.hyperstack.cloud. SGLang is also straightforward for developers but is a bit more low-level out of the box: it doesn’t come with a web UI or model downloader UI; you interact with it via API or Python. A casual user might need to use command-line and config files (as one blogger noted, SGLang “was not created for enthusiasts to run on home rigs” but rather for custom integration, lacking a one-click GUI)dev.to. However, SGLang’s frontend programming model is a unique feature: it provides a Structured Generation Language that lets developers script complex prompting logic, chain multiple generation calls, implement conditional flows, and even handle multi-modal I/O in a concise waygithub.com. This is essentially a domain-specific language (embedded in Python) for orchestrating the model – for example, you could prompt the model, then post-process or adjust and prompt again in one “program” rather than writing a lot of external code. vLLM does not have an equivalent built-in DSL; you’d handle such logic in your own application code or via frameworks like LangChain on top of the API. Depending on needs, SGLang’s approach can be more flexible for custom generation workflows (like tool use, constrained decoding, etc.), while vLLM’s design is minimal and focused on just serving model in/out. Both frameworks are extensible (open-source and written largely in Python), so advanced users can customize scheduling, add new model architectures, or integrate them into larger systems. To summarize, API and usability are strong for both: vLLM is a plug-and-play server with broad adoption (easy for standard use cases), and SGLang is almost as easy to deploy while also offering a powerful programming interface for those who need more control over prompting and output generation.

  • Deployment Options: Both SGLang and vLLM are self-hosted solutions that can run on local machines or cloud instances with the appropriate hardware. There is no vendor lock-in – you can run them on-premise, on AWS/GCP/Azure GPU VMs, or even (for smaller models) on a CPU machine. SGLang is distributed via pip and Docker, so you can install it in a Python environment or pull a Docker image for conveniencelmsys.org. It currently supports Linux environments (for example, users on Windows have run it via WSL)dev.to. SGLang has been used in production settings like LMSYS’s Chatbot Arena and by startups, typically deployed on GPU serverslmsys.orglmsys.org. vLLM is likewise installable via pip and has Docker containers; it can be integrated with serving platforms (e.g., there are guides to deploy vLLM on Ray Serve and Beam Cloud)www.anyscale.comdocs.beam.cloud. vLLM’s broad hardware support makes it flexible in deployment: it works with NVIDIA GPUs (with CUDA), AMD GPUs (with ROCm), as well as CPU-only modes for x86/ARM and even specialized acceleratorswww.clarifai.com. In practice, if you have an NVIDIA GPU box, both frameworks will utilize it fully (including multi-GPU support). If you have non-NVIDIA hardware, vLLM may be more likely to run out-of-the-box (for instance, vLLM can use AMD’s HIP runtime or even run on Intel GPUs). For distributed deployment, vLLM can be scaled out using cluster frameworks (the AnyScale example shows using it in a distributed Ray Serve cluster to handle more load)www.anyscale.comwww.anyscale.com. SGLang’s documentation currently focuses on single-node deployment, but it can serve very large models on multiple GPUs in one machine (and presumably could be orchestrated on multiple nodes with some work). In terms of cloud vs local: both can be used locally for development (e.g., testing an LLM on your workstation with a GPU) and then containerized and deployed to a cloud for production. There aren’t cloud SaaS offerings specifically for SGLang or vLLM (they are open-source projects, not cloud services), so you manage the infrastructure. One trade-off is that SGLang’s cutting-edge performance features (like FP8 precision) might require newer GPUs (H100/A100 with support for those instructions)lmsys.org, whereas vLLM will work on older GPUs too but without those specific speedups. Both frameworks have active communities and are regularly updated, which helps with deployment support and troubleshooting.

  • Integration with Other Tools: Both frameworks integrate well with the Hugging Face ecosystem for model weights and tokenizers. You can load models from the Hub and use the corresponding tokenizer in both SGLang and vLLM (they handle all that internally via Transformers library). If you have pipelines built on Hugging Face’s transformers or accelerate, switching to vLLM or SGLang might require replacing the generation call with an API call or their library call, but the rest (model config, tokenizer) remains standard. For MLOps and serving platforms, vLLM has examples working with Ray (for scaling inference)www.anyscale.comand Kubernetes (there are community Helm charts and docker-compose examples for vLLM). SGLang, being newer, is quickly being adopted into similar workflows; for instance, it’s used with FastChat for multi-model servinglmsys.org, and the Beam Cloud docs show how to deploy an OpenAI-compatible SGLang server on their platformdocs.beam.cloud. In terms of compatibility with optimized runtimes: SGLang’s backend has integrated many low-level optimizations (FlashAttention/FlashInfer kernels, etc.)github.comso that you don’t necessarily need external tools like TensorRT. It is not directly a TensorRT or Triton server backend (instead, it’s an alternative to those). vLLM similarly uses optimized CUDA kernels (including FlashAttention) under the hoodwww.hyperstack.cloud, instead of relying on Nvidia’s TensorRT-LLM. If one wanted to use NVIDIA Triton Inference Server, that’s a different stack (Triton with FasterTransformer or TensorRT-LLM); by contrast, vLLM and SGLang come with their own serving runtime. Thus, you typically wouldn’t combine vLLM/SGLang with Triton — you’d choose one approach or the other. Both frameworks support quantization techniques like GPTQ and AWQ for int4/int8 models to speed up and lighten modelswww.clarifai.comgithub.com, integrating those libraries so you can serve quantized models easily. In summary, SGLang and vLLM are meant to be relatively self-contained solutions: they integrate the model, scheduling, and optimized kernels such that you plug them into your application (via API or Python) rather than chaining them with other inference servers. This makes them quite straightforward to integrate at the application level (especially with the OpenAI API compatibility). For developers already using LangChain or other LLM orchestration libraries, both vLLM and SGLang can act as the backend LLM service (LangChain can call the OpenAI API, which in this case is backed by these local servers). SGLang’s additional frontend DSL could also integrate with external tools – for example, its external interactions feature can call out to other APIs or tools during generationgithub.com, which is powerful for building agent-like applications. Meanwhile, vLLM’s focus on standards and simplicity means it will play nicely with most existing ML Ops pipelines and cloud deployments.

Practical Use Cases and Trade-offs

Both SGLang and vLLM are geared towards serving large language models efficiently, but they have distinct strengths that make each one preferable for certain scenarios:

  • Real-time Chatbots and High-Concurrency Services: If you are building a production chatbot service (with many users or multi-turn conversations), both frameworks shine by handling concurrent queries with low latency. vLLM has a strong track record here – its continuous batching and memory optimizations were proven in real services to significantly cut costs and latencyblog.runpod.io. If your use case demands stable, predictable performance under heavy load and you want a solution that has been widely adopted (tested by “thousands of companies”)blog.runpod.io, vLLM is a safe choice. On the other hand, SGLang has shown excellent performance in chatbot arenas and benchmarks, often delivering more tokens per second for a given modellmsys.org. SGLang would be advantageous if absolute throughput is critical – e.g. maximizing utilization of a high-end GPU to serve the most users or the longest responses possible. It has been used to serve models generating trillions of tokens in aggregatelmsys.org. Trade-off: vLLM might be slightly easier to deploy in a distributed manner for scaling out, and it’s very robust, whereas SGLang might require a bit more tuning when scaling to unusual models or scenarios (as seen with the Mistral concurrency casewww.clarifai.com). However, SGLang’s superior single-request speed means in a low-concurrency setting (e.g. an interactive session or a few parallel sessions), it can provide snappier responses.

  • Resource Efficiency and Cost-Sensitive Deployment: If your goal is to maximize model performance per dollar (e.g. reduce the number of GPUs needed for a given throughput), both frameworks help, but they do so in slightly different ways. vLLM is extremely memory-efficient – for instance, LMSYS (the team behind SGLang/Vicuna) reported they halved their GPU count using vLLM while serving 2–3× more requests by eliminating memory wasteblog.runpod.io. This indicates that if GPU memory is the limiting factor (such as serving long contexts or many simultaneous sessions), vLLM will make the most of it, possibly allowing you to serve larger contexts or more users on one GPU than SGLang might (though SGLang also tries to reuse memory, vLLM’s paging is very mature). SGLang achieves efficiency by leveraging lower precision and optimized compute – e.g. FP8 quantization can dramatically speed up inference on supported hardwarelmsys.org, and its scheduler avoids CPU slowdownslmsys.org. In practical terms, for shorter prompts and shorter outputs but very high QPS, SGLang’s lean scheduling can squeeze out extra throughput (thus serving more requests per second on the same hardware)lmsys.org. For longer prompts/contexts, vLLM’s memory management may prevent slowdowns or out-of-memory issues as it dynamically allocates just what is neededblog.runpod.io. Cost trade-off: If you have expensive GPUs and want to use every bit of them, SGLang might deliver slightly higher raw throughput (so you get more value per GPU-hour)lmsys.org. If your models have huge context windows or you want to host multiple models on one GPU, vLLM’s near-zero memory waste lets you do that more safelyblog.runpod.io(less risk of needing extra GPUs for overflow). Many users might choose vLLM when running several different model instances on the same server for this reason.

  • Application Complexity and Custom Generation Flows: For use cases that go beyond simple single-turn completion – for example, an application that needs to call the LLM multiple times with conditional logic, or integrate image inputs with text generation (vision+language), SGLang’s design offers an advantage. Its structured frontend language lets you script these complex interactions in a unified waygithub.com. A practical case might be implementing a chatbot that first calls the LLM to format a query, then calls an external API, then feeds the result back into the LLM – SGLang can coordinate this in one workflow. vLLM, while flexible as a backend, doesn’t natively provide that orchestration layer; you’d handle it in your application code or use a library on top. So for researchers or developers who want fine-grained control over the generation process (like enforcing certain output formats, injecting constraints, or parallelizing several generation tasks), SGLang provides tools to do so convenientlygithub.com. Meanwhile, vLLM is optimized for the core task of given a prompt, efficiently produce a completion. This simplicity is beneficial for straightforward integration – e.g., hooking vLLM up to a chat UI or an API gateway is very easy, as noted by community userswww.reddit.com. Trade-off: If your project is essentially a direct LLM service (chatbot, text completion API, etc.), vLLM’s streamlined approach is ideal. If your project treats the LLM as one component in a larger pipeline of logic (potentially involving tools, multi-turn programmatic prompting), SGLang might reduce development friction by allowing that logic to reside alongside the model calls.

  • Community and Maturity: vLLM has been around a bit longer and gained a large user base, which means more community support, examples, and tested integrations. It’s known to be stable for production use (the v0.6 release in late 2024 was a major performance update improving on any earlier hiccups). SGLang is newer (initial release mid-2024) but is backed by the reputable LMSYS team and an active open-source communitygithub.com. It’s quickly catching up in features and being adopted by early adopters. One trade-off here is that with vLLM you might find more off-the-shelf guides, forum answers, or existing Docker setups for various scenarios, whereas with SGLang you might be an early explorer in some aspects (for example, running SGLang on Windows or in certain managed platforms could require more tinkering simply because fewer people have documented it). However, SGLang’s codebase is relatively small and Pythonic (the core scheduler <4K lines of code)lmsys.orglmsys.org, which can make it easier to understand and modify if you need to. vLLM is also mainly Python but has grown to incorporate more complex features, which could be a consideration if you plan to extend it deeply. In summary, both SGLang and vLLM are excellent choices for efficient LLM inference, and in many cases they can be used interchangeably with great results. If you prioritize maximum throughput and customization and are willing to use a cutting-edge system, SGLang offers significant advantages in speed (often outperforming others in benchmarks)lmsys.organd flexibility (with its DSL and easy hackability). If you prioritize proven stability, memory optimization, and easy integration into existing workflows, vLLM is a strong candidate – it provides nearly optimal GPU memory usageblog.runpod.io, continuous batching for high throughput, and an easy API that fits into applications out of the boxwww.hyperstack.clouddocs.vllm.ai. Many teams will evaluate both for their specific use case. For example, a company running an online chat service might test throughput and latency with both frameworks using their typical workload; one might find SGLang gives better token/sec and stick with it, or find that vLLM’s throughput is sufficient and prefer its simplicity. The good news is that both frameworks are open-source and improving rapidly, so the gap between them is likely to continue narrowing as they adopt each other’s best ideas (indeed, SGLang has integrated concepts like paged attentiongithub.com, and vLLM has been reducing scheduling overhead as SGLang didblog.vllm.ai). Ultimately, the choice may come down to the specifics of your deployment environment and needs: SGLang for those who need bleeding-edge performance and advanced generation control, vLLM for those who need a battle-tested, efficient serving engine that’s plug-and-play. Both represent the state of the art in LLM serving, enabling practical use of large models in real applications with much better speed and cost-efficiency than naive approaches.

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