--- title: Implementing vLLM in Qwen Serving | daily.dev description: A hands-on comparison of serving Qwen2-1.5B with bare transformers.generate() versus vLLM on a single T4 GPU (AWS g4dn.xlarge). Covers vLLM's three core... canonical: https://daily.dev/posts/implementing-vllm-in-qwen-serving-q8njflolx twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: Implementing vLLM in Qwen Serving | daily.dev og:description: A hands-on comparison of serving Qwen2-1.5B with bare transformers.generate() versus vLLM on a single T4 GPU (AWS g4dn.xlarge). Covers vLLM's three core... og:url: https://daily.dev/posts/implementing-vllm-in-qwen-serving-q8njflolx og:image: https://api.daily.dev/og/posts/q8NJFLoLx.png og:image:alt: Implementing vLLM in Qwen Serving og:image:width: 1200 og:image:height: 630 og:locale: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # Implementing vLLM in Qwen Serving **[Medium](https://daily.dev/sources/medium_js)** · 18 min read · 0 upvotes · 0 comments ## Summary A hands-on comparison of serving Qwen2-1.5B with bare transformers.generate() versus vLLM on a single T4 GPU (AWS g4dn.xlarge). Covers vLLM's three core mechanisms — continuous batching, paged attention, and chunked prefill — then benchmarks both engines across a 16-cell matrix (2 engines × 2 prompt lengths × 4 concurrency levels). Key findings: vLLM loses on throughput at concurrency=1 due to scheduler overhead, but wins 8-to-1 at concurrency=5 and bare server completes zero requests at concurrency=20. Also details diagnosing a CUDA OOM error using the KV cache formula, fixing max-num-seqs from 256 to 64 for the T4's 14.74 GiB VRAM, and making the bare server OpenAI-compatible for fair benchmarking with guidellm. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Questions this post answers ### Why did my vLLM deployment crash with CUDA out of memory when warming up the sampler with 256 dummy requests? The default --max-num-seqs value in vLLM is 256, and at startup vLLM pre-allocates KV cache space for that ceiling. For Qwen2-1.5B in fp16 with GQA (2 KV heads, 28 layers, head dim 128), each token costs about 28KB of KV cache, so 256 sequences at max_model_len 2048 requires roughly 15GB, which exceeds a T4's 14.74GiB capacity. Lowering --max-num-seqs to 64 reduces the reservation to about 3.7GB. _Developers debugging GPU memory errors while deploying LLM servers can find similar diagnostics and fixes on daily.dev._ ### Does vLLM always outperform a plain transformers.generate() server for LLM serving? No, at concurrency of 1 a bare transformers.generate() loop actually beats vLLM on throughput and inter-token latency because vLLM's scheduler overhead (admission control and batch management at every decode step) has no batching benefit to offset it. vLLM only wins on time-to-first-token at concurrency 1. The advantage flips sharply by concurrency 5, where bare's throughput collapses from 101.8 to 29.3 tokens/sec while vLLM climbs to 238.1, and by concurrency 20 bare completes zero requests while vLLM sustains around 765 tokens/sec. _Teams choosing an LLM serving engine can weigh these concurrency trade-offs alongside other real benchmarks on daily.dev._ ### How much does prompt length affect vLLM's throughput advantage over a naive serving loop? Long prompts (around 500 tokens) narrow vLLM's advantage only modestly thanks to chunked prefill, which slices prompt processing into chunks interleaved with decode steps. At concurrency 20, vLLM's throughput drops from 765.8 to 723.6 output tokens/sec with identical inter-token latency between short and long prompts, meaning the batching failure of a naive server dominates far more than any prefill cost difference. _Engineers tuning inference performance across prompt lengths can track findings like this on daily.dev._ ## Similar posts on daily.dev - [vLLM Reaches 25K Total TPS/GPU on Qwen3.5](https://daily.dev/posts/vllm-reaches-25k-total-tps-gpu-on-qwen3-5-hjwogrfb1) · vLLM · 1 upvotes · 0 comments - [Inside vLLM: Anatomy of a High-Throughput LLM Inference System \(2025\)](https://daily.dev/posts/inside-vllm-anatomy-of-a-high-throughput-llm-inference-system-2025--5fkcjpua7) · Hacker News · 2 upvotes · 0 comments - [Continuous Batching in LLMs](https://daily.dev/posts/continuous-batching-in-llms-yrzppfm2w) · Daily Dose of Data Science \| Avi Chawla \| Substack · 1 upvotes · 0 comments - [Inside the vLLM-Omni architecture: Serving Qwen3-Omni](https://daily.dev/posts/inside-the-vllm-omni-architecture-serving-qwen3-omni-o8bxkmvxf) · Red Hat Developer · 0 upvotes · 0 comments - [Continuous Batching Mechanics: The Scheduler Behind vLLM, TGI, and SGLang](https://daily.dev/posts/continuous-batching-mechanics-the-scheduler-behind-vllm-tgi-and-sglang-9x0j2tj3u) · DigitalOcean Community · 0 upvotes · 0 comments --- Tags: [#vllm](https://daily.dev/tags/vllm), [#qwen](https://daily.dev/tags/qwen) [View this post on daily.dev](https://daily.dev/posts/implementing-vllm-in-qwen-serving-q8njflolx) ```json {"@context":"https://schema.org","@graph":[{"@type":"Organization","@id":"https://daily.dev/#organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180},"sameAs":["https://twitter.com/dailydotdev","https://github.com/dailydotdev","https://www.linkedin.com/company/daily-dev-ltd"]},{"@type":"WebSite","@id":"https://daily.dev/#website","url":"https://daily.dev","name":"daily.dev","publisher":{"@id":"https://daily.dev/#organization"},"potentialAction":{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https://daily.dev/search?q={search_term_string}"},"query-input":"required name=search_term_string"}}]} {"@context":"https://schema.org","@type":"TechArticle","headline":"Implementing vLLM in Qwen Serving","url":"https://daily.dev/posts/implementing-vllm-in-qwen-serving-q8njflolx","mainEntityOfPage":{"@type":"WebPage","@id":"https://daily.dev/posts/implementing-vllm-in-qwen-serving-q8njflolx"},"datePublished":"2026-07-17T19:34:03.649Z","dateModified":"2026-09-13T19:03:00.565Z","description":"A hands-on comparison of serving Qwen2-1.5B with bare transformers.generate() versus vLLM on a single T4 GPU (AWS g4dn.xlarge). 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The advantage flips sharply by concurrency 5, where bare's throughput collapses from 101.8 to 29.3 tokens/sec while vLLM climbs to 238.1, and by concurrency 20 bare completes zero requests while vLLM sustains around 765 tokens/sec. Teams choosing an LLM serving engine can weigh these concurrency trade-offs alongside other real benchmarks on daily.dev."}},{"@type":"Question","name":"How much does prompt length affect vLLM's throughput advantage over a naive serving loop?","acceptedAnswer":{"@type":"Answer","text":"Long prompts (around 500 tokens) narrow vLLM's advantage only modestly thanks to chunked prefill, which slices prompt processing into chunks interleaved with decode steps. At concurrency 20, vLLM's throughput drops from 765.8 to 723.6 output tokens/sec with identical inter-token latency between short and long prompts, meaning the batching failure of a naive server dominates far more than any prefill cost difference. Engineers tuning inference performance across prompt lengths can track findings like this on daily.dev."}}]} ```