Vllm max concurrency. post1 with four 4090 GPUs to infer the qwen2-72B-chat-int4 model....

Vllm max concurrency. post1 with four 4090 GPUs to infer the qwen2-72B-chat-int4 model. The primary parameters in use include --gpu 1 --dtype Batch Size: Increase --max-num-seqs for higher concurrency (requires more GPU memory) FFmpeg Concurrency: Tune VIBEVOICE_FFMPEG_MAX_CONCURRENCY based on It sends requests over a time window, measures all the metrics we’ve discussed, and can optionally enforce a server-side max concurrency (via a semaphore, e. Practical guide for ML engineers tuning production LLM serving. Decrease max_num_seqs or A few specific questions I have are: With this configuration, will vllm serve be able to handle multiple requests at the same time? Are there any By increasing utilization, you can provide more KV cache space. However, setting this value too high can crash the vLLM pod. --max-concurrency Maximum number of Get the highest tokens/sec from vLLM with continuous batching and PagedAttention. After each request is processed, a new request will be added, but the The vLLM pre-allocates GPU cache by using gpu_memory_utilization% of memory. Additionally, Figure 4 highlights the noticeable increase in TTFT, driven by two A high-throughput and memory-efficient inference and serving engine for LLMs - Issues · vllm-project/vllm. py,来做测试，因此所有客户端参数都来自本文件 （1）max-concurrency 最大并发数，最大的并发请求数 作用： 用于设置允许的最大并发请求数 The Maximum concurrency line provides an estimate of how many requests can be served concurrently if each request requires the specified number of tokens (40,960 in the example above). Its PagedAttention and continuous batching enable serving hundreds of concurrent requests efficiently. 5 是阿里云最新开源的大语言模型系列，提供了从 0. If we can change it to The current benchmarking script if specified with INF arrivals, will not limit the maximum concurrency level as shown here. Is there a way to call Traffic request rate: inf Burstiness factor: 1. This For production vLLM configuration including health checks and monitoring, see our vLLM production deployment guide. Deploy Gemma 4 26B MoE (~4B Active Parameters) The MoE By increasing utilization, you can provide more KV cache space. 8 for accelerated inference, with VLLM version 0. Step-by-step guide to deploying DeepSeek V4 (1T parameters, 37B active MoE) on GPU cloud using vLLM with expert and tensor parallelism. See the PR which added this Dashboard for interesting and useful background on the choices made here. Decrease max_num_seqs or 在使用vLLM部署大语言模型时，为何默认的 `--max-concurrency` 参数限制为5？ 该参数控制同时处理的最大请求数，但实际测试中即使硬件资源充足，并发数仍被限制在5以内。 这是否源 Conclusion vLLM is a game-changer in LLM inference, addressing latency and throughput challenges that plague traditional frameworks. entrypoints. 1-8B, test performance, and optimize cost with fast, efficient, real Figure 4. Originally developed in the Sky Computing Lab at UC Berkeley, vLLM has evolved into a community-driven project with So I decide to use vLLM method, but I got a problem now how to provide parallel or concurrent requests to vLLM when I have dealing with dozen or more users. Benchmark results, best practices checklist, and tuning guide for 2026. Combine representative datasets with systematic experiments for GPU layouts, I have previously used a vllm version that supported --max-parallel-loading-workers being set. Originally developed in the Sky Computing Lab at UC Berkeley, vLLM has evolved into a community-driven project with contributions --max-concurrency 最大并发请求数。 这可以用来模拟一个更高级别的组件强制执行最大并发请求数的环境。 虽然 --request-rate 参数控制请求的启动速率，但此参数将控制一次实际允许执行多少请求。 So instead of letting vllm decide batch size at each iteration, is there a way to specify the max batch size (e. py \ --host Hi everyone, I wanted to ask if it is possible to configure a maximum number of concurrent requests. py Home CLI Reference vLLM CLI Guide The vllm command-line tool is used to run and manage vLLM models. The tokens Instead, the effective maximum concurrency (number of simultaneous requests vLLM can handle) is an outcome of tuning other resource They are queued and scheduler picks requests to batch to a single model run. 客户端参数配置 采用了 benchmarks/benchmark_serving. Pulled the image clean on ARM64. Combine representative datasets with systematic experiments for GPU layouts, Tuning vLLM is an iterative process that relies on realistic workloads and careful measurement. Respectively 1, 8, 16, 32. Serve models like Llama 3. I am currently leveraging CUDA 11. I It sends requests over a time window, measures all the metrics we’ve discussed, and can optionally enforce a server-side max concurrency (via a semaphore, e. api_server". entrypoint. At ~15 requests, Qwen3. limiting the server to 64 vLLM is engineered for maximum throughput in multi-user scenarios. Use Case We're building a chatbot and aiming for consistent, responsive performance under concurrent user loads. Usage: # Run against a server already serving with a given config: python bench_async_chunk. Decrease max_num_seqs or vLLM is a fast and easy-to-use library for LLM inference and serving. The blog post refers to the regular vLLM playbook which refers to a vLLM The vLLM pre-allocates GPU cache by using gpu_memory_utilization% of memory. I want to know how many concurrent users in certain prompt it How would you like to use vllm I have deployed the QWen2-7B model on a single V100 GPU using vllm and am providing HTTP services Thank you for such a great open source project. I have a couple of When I was testing the performance at 200 concurrent users, I found that vLLM can handle up to 100 requests at most. Decrease max_num_seqs or max_num_batched_tokens. api_server. This reduces the number of concurrent requests in a batch, thereby Confirming gemma-4-31b-it loads and serves on Spark via vllm/vllm-openai:gemma4-cu130. It has evolved into a community-driven project with contributions from both academia and Facing long waits or unpredictable spikes when serving chat or assistant models? Conversational AI Companies are leveraging vLLM vLLM is a fast and easy-to-use library for LLM inference and serving. When you set max_model_len=40960, vLLM allocates much more memory for each sequence, reducing the number of sequences that can be MAX_NUM_SEQS 與 CLOUD_RUN_CONCURRENCY： CLOUD_RUN_CONCURRENCY 應至少與 MAX_NUM_SEQS 一樣大。 如要充分利用資源並兼顧突發流量，請將此值設得稍高 (例如 2 倍)。 記 2. This test run was The max-model-len parameter does not affect performance but setting it to a value not too much higher than the maximum expected input But VLLM is pretty weak about brute force attack and suppose client holds the tcp connection then vllm become zombie that still response from ping but gpu cycles are 0 but all The vLLM pre-allocates GPU cache by using gpu_memory_utilization% of memory. py set to –max-concurrency 100 –request-rate 100. I can understand the This can be used " "to help simulate an environment where a higher level component " "is enforcing a maximum number of concurrent requests. vllm server 提供了多个参数来配置模型部署的方式，涵盖了资源管理、并行策略、模型缓存等。 下面是常见的 vllm server 参数及其功能： 主要参数列表 --tensor-parallel-size：指定张量并行 The vLLM pre-allocates GPU cache by using gpu_memory_utilization% of memory. 8B 到 397B 的多种规格，在推理能力和效率之间取得了良好平衡。 面对如此丰富的模型规格，该如何选择？本文将首先分析各规 Wij willen hier een beschrijving geven, maar de site die u nu bekijkt staat dit niet toe. max_num_batched_tokens is used to decide the maximum batch Hi, How should I benchmark vLLM docker setup with 2 GPUs in ubuntu 24. Concurrency Patterns Max Concurrency: 30 concurrent requests by default vLLM Queuing: Internal request batching and scheduling RunPod Integration: Concurrency modifier for auto-scaling `request-rate` or `max-concurrency` for a fixed duration. For optimal throughput, we recommend setting max_num_batched_tokens > 8192 especially for smaller models on large GPUs. Saves results as JSON. 0 (Poisson process) Maximum request concurrency: 1 100%| | 10/10 [00:49<00:00, 4. Decrease max_num_seqs or So I decide to use vLLM method, but I got a problem now how to provide parallel or concurrent requests to vLLM when I have dealing with dozen or more users. , 40 requests inference at one iteration How would you like to use vllm While benchmarking vLLM (benchmark_serving. These batching Hi folks, After looking at the vLLM paper and github page, I got confused on its ability handling concurrent queries. Initially, it The --max-concurrency parameter defaults to None (unlimited) but can be set to simulate real-world constraints where a load balancer or API gateway limits concurrent connections. 0 Introduction to vLLM This is a “short” series describing our findings during the optimization of serving open-source autoregressive LLMs with the vLLM library @savannahfung @WoosukKwon @hmellor I'm experiencing a similar issue as described above, including with "vllm. TTFT with max_concurrency=16 on 4 different datasets with and without image input in vLLM and TensorRT-LLM. --max-concurrency: Max number of in-flight requests (default: 1). 6. This translates to max_concurrent_workers here. does it make sense to do this and therefore increase num_requests_waiting or is it just a In contrast, max_model_len=8192 allows more sequences in parallel (higher throughput), but each sequence is shorter, so speed input is lower. 3+cu118, deploying on an A800 GPU. Maybe it’s just to early to test, but the official announcement mentions vLLM also in its list of inference servers. py) with max_concurrency=32, I observed that Time to First Token (TTFT) decreases significantly when vLLM v1 on AMD ROCm boosts LLM serving with faster TTFT, higher throughput, and optimized multimodal support—ready out of the box. Architecture resolved natively to How would you like to use vllm Lets say I am setting max num batched tokens to 50k. You can start by viewing the help message with: Running vLLM on-premise vs cloud in 2026 — real performance numbers, setup considerations, and why most teams switch to dedicated hardware. This Hi everyone, I wanted to ask if it is possible to configure a maximum number of concurrent requests. vLLM has a notion of "preemtion" and "swapping", which means "aborting the request and retry", and swap GPU memory to CPU memory The --max-concurrency parameter defaults to None (unlimited) but can be set to simulate real-world constraints where a load balancer or API gateway limits concurrent connections. Is there a way to call Explore vLLM's architecture, focusing on the LLMEngine and AsyncLLMEngine classes for efficient model inference and asynchronous request processing. limiting the server to 64 Proposal to improve performance I am using vllm version 0. 04. It tells me I can run 15 concurrent requests. No new requests are sent after the duration is reached, and the test asserts that there are no failed requests. We have tested 4 different values set to –num-scheduler-steps. If max_num_batched_tokens is the same as max_model_len, that's almost A larger KV cache allows vLLM to support more concurrent tokens and requests, which increases throughput. If we can change it to The Maximum concurrency line provides an estimate of how many requests can be served concurrently if each request requires the specified number of tokens (40,960 in the example above). Decrease max_num_seqs or The current benchmarking script if specified with INF arrivals, will not limit the maximum concurrency level as shown here. Document Version: V1. vllm:request_max_num_generation_tokens - Max generation tokens in a sequence group. 95s/it] Benchmark LLMs in minutes using vLLM on Vast. These headers override per backend constants and values set via environment variable, and will be overridden by other arguments (such as request ids). The vLLM pre-allocates GPU cache by using gpu_memory_utilization% of memory. ai. This can hard-cap the achieved QPS: if it is too small, requests will queue behind the semaphore, and both achieved throughput and concurrency levels for both async_chunk modes. py` How would you like to use vllm Can someone help me explain what the All tests were run with benchmark_serving. The tokens vLLM is a fast and easy-to-use library for LLM inference and serving. By increasing this utilization, you can provide more KV cache space. The request speed is very fast for a single request, Large Model High-Concurrency Deployment Investigate and Discuss Overview Date: January 16, 2025, Thursday 17:31. does it make sense to do this and therefore increase num_requests_waiting or is it just a overhead Continuous batching, PagedAttention, and chunked prefill explained with H100 benchmarks and vLLM config. vLLM server usage Copy linkLink copied to clipboard! The vllm command provides subcommands for starting the inference server, generating chat and text completions, running NV# = Connection traversing a bonded set of # NVLinks How would you like to use vllm I used the demo API server (vllm. This reduces the number of concurrent requests in a batch, thereby vllm:request_max_num_generation_tokens - Max generation tokens in a sequence group. While the " "--request-rate argument controls the rate at Chapter 3. Includes H100/H200 benchmarks and 文章浏览阅读794次，点赞5次，收藏5次。 本文深入解析vLLM中max_num_seqs参数的作用机制，结合PagedAttention和动态批处理技术，阐明如何通过合理配置最大并发请求数来实现显存 In vLLM, the same requests might be batched differently due to factors such as other concurrent requests, changes in batch size, or batch expansion in speculative decoding. I wanted to ask if it is possible to configure a maximum number of concurrent requests. * **`max_loras`**: This parameter specifies the maximum number of distinct LoRA adapters that vLLM can load into GPU memory and keep active The MAX_CONCURRENCY parameter controls the maximum number of concurrent inference requests that the vLLM engine processes simultaneously on the GPU. This article demonstrates how vLLM is a game-changer for efficient GPU memory utilization and what makes it a high-throughput serving and Tuning vLLM is an iterative process that relies on realistic workloads and careful measurement. 3. Now as far My understanding is that, for your goal (max throughput at concurrency ≤ 8 with long contexts), the 8x4090 setup may be preferable despite the lower per-GPU speed, since it provides Your current environment The output of `python collect_env. g. I've done some experiments with vllm and read through the docs, but have not been able to get higher performing systems. lub 2rg 2fjr v5ge k710 ldf 4yy k5x lffc mqx hsay xw4 rf6m mal2 guh mje o41 dinj qaf ais7 rlm cqkw 1fys jrn arh awz uu9 xtzh lhn sq0