{"slug": "vllm-beats-ollama-by-20x-once-you-hit-high-concurrency", "title": "vLLM beats Ollama by 20x once you hit high concurrency", "summary": "VLLM outperforms Ollama by nearly 20x in throughput at high concurrency, according to benchmark tests running Llama 3.1 8B on an NVIDIA A100 40GB, with vLLM peaking at 793 tokens per second versus Ollama's 41 tokens per second at 256 simultaneous requests. The architectural difference lies in vLLM's PagedAttention and continuous batching, which optimize GPU utilization, while Ollama, a Go-based wrapper around llama.cpp, is designed for single-user developer experiences.", "body_md": "# vLLM beats Ollama by 20x once you hit high concurrency\n\n## The Architectural Divide\n\nvLLM is a full-blown production serving engine. It doesn't just \"run\" a model; it manages the GPU with an intensity similar to an operating system. The secret sauce is PagedAttention. In standard inference, the KV cache (which stores previous tokens) is a memory hog that creates huge fragments of wasted space. vLLM handles this by storing tokens in non-contiguous blocks, virtually eliminating memory waste.\n\nThen there is continuous batching. Most runners wait for an entire batch of requests to finish before starting the next. vLLM allows requests to enter and exit the batch in real-time. As soon as one user's response is done, another request slides into that slot. This keeps the GPU pinned at maximum utilization, which is why it's the gold standard for LLM agent deployments in the real world.\n\nOllama, conversely, is a Go-based wrapper around llama.cpp. It is designed for the \"developer at a desk\" experience. It’s brilliant for pulling a model and chatting in seconds, but it lacks the aggressive scheduling of vLLM. While you can tweak `OLLAMA_NUM_PARALLEL`\n\n, it isn't designed to saturate an A100 or H100 the way a dedicated serving stack does.\n\n## Benchmarking the Throughput Gap\n\nWhen you look at actual numbers—specifically Llama 3.1 8B running on an NVIDIA A100 40GB—the difference is staggering. At a concurrency of 1 (one person chatting), the performance is roughly the same. Ollama is snappy and efficient for a single stream.\n\nHowever, as the number of simultaneous requests climbs toward 256, the efficiency of PagedAttention and continuous batching kicks in. In verified tests, vLLM peaked at around 793 tokens per second, while Ollama struggled at roughly 41 tokens per second. We are talking about a nearly 20x difference in throughput.\n\n## Choosing Your Stack\n\nIf you are still undecided, use this logic for your deployment:\n\n**Use Ollama if:** You need a beginner-friendly setup, you're developing locally on a Mac or a single Linux box, or your app only serves a handful of internal users. It is the perfect \"from scratch\" starting point.**Use vLLM if:** You are moving toward a real-world production environment, you have multiple GPUs, or you are building a public-facing API. If throughput and latency under load are your primary KPIs, vLLM is the only serious choice.\n\nFor a high-performance AI workflow, the ideal setup is often using Ollama for rapid prototyping and switching to vLLM for the final deployment phase to ensure the system doesn't collapse under pressure.\n\n[Next Choosing the wrong canton in Switzerland can literally cost you →](/en/threads/6218/)\n\n[a library of Claude prompt techniques](https://tanyan888.com/), with plenty of directly applicable cases.", "url": "https://wpnews.pro/news/vllm-beats-ollama-by-20x-once-you-hit-high-concurrency", "canonical_source": "https://promptcube3.com/en/threads/6300/", "published_at": "2026-08-14 16:45:25+00:00", "updated_at": "2026-08-14 16:49:53.390693+00:00", "lang": "en", "topics": ["artificial-intelligence", "machine-learning", "large-language-models", "ai-infrastructure", "ai-tools"], "entities": ["vLLM", "Ollama", "Llama 3.1 8B", "NVIDIA A100 40GB", "PagedAttention", "llama.cpp"], "alternates": {"html": "https://wpnews.pro/news/vllm-beats-ollama-by-20x-once-you-hit-high-concurrency", "markdown": "https://wpnews.pro/news/vllm-beats-ollama-by-20x-once-you-hit-high-concurrency.md", "text": "https://wpnews.pro/news/vllm-beats-ollama-by-20x-once-you-hit-high-concurrency.txt", "jsonld": "https://wpnews.pro/news/vllm-beats-ollama-by-20x-once-you-hit-high-concurrency.jsonld"}}