vLLM vs Ollama: Concurrency Limits and Serving Setup

The choice between Ollama and vLLM comes down to concurrency. In Red Hat's benchmark on an NVIDIA A100-PCIE-40GB GPU, vLLM reached a peak throughput of 793 TPS, while Ollama peaked at 41 TPS.1

When to Pick Ollama vs. vLLM Across User Tiers

Teams must decide their deployment architecture based on active user count and workload type. Ollama is recommended for single workstations or shared setups of 2 to 4 researchers with dedicated GPUs, where personal interaction and straightforward toolchains matter most. Conversely, vLLM is recommended for shared servers with 5 or more concurrent users needing continuous service. In an internal knowledge assistant implementation, Ollama's P95 latency grew from 3 seconds to over a minute when users climbed from 3 to 40. Migrating to vLLM lowered P95 latency back under 2 seconds on identical hardware, proving that queuing overhead quickly overwhelms Ollama once multiple users make concurrent calls.2

llama.cpp GGUF Runtime vs. vLLM PagedAttention

The performance gap between these two engines traces directly to memory design. Ollama is built on llama.cpp, relies on the GGUF model format, and runs bare-metal without containers. It reserves a fixed memory block per model and processes requests sequentially by default, leaving secondary queries waiting in queue. In contrast, vLLM serves HuggingFace-format models and relies on PagedAttention, an attention algorithm inspired by classical virtual memory and paging techniques in operating systems. vLLM achieves near-zero waste in KV cache memory and enables flexible sharing of KV cache within and across requests. Evaluations show vLLM improves popular LLM throughput by 2-4x with the same latency level compared to FasterTransformer and Orca. Beyond PagedAttention, vLLM lists performance optimizations including continuous batching, speculative decoding, chunked prefill, parallel sampling, beam search, tensor and pipeline parallelism, and prefix caching.243

Throughput and Latency at Scale on an NVIDIA A100

In Red Hat's benchmark testing on a single NVIDIA A100-PCIE-40GB GPU using driver 550.144.03, CUDA 12.4, OpenShift, Python 3.13.5, vLLM 0.9.1, and Ollama 0.9.2, the two engines diverged immediately under load. At peak throughput, vLLM recorded a P99 latency of 80 ms compared to 673 ms for Ollama. Across all tested concurrency levels from 1 to 256 concurrent users, vLLM demonstrated higher throughput and lower latency. While vLLM dynamically scales its execution pipeline as requests arrive, Ollama's sequential queuing forces subsequent requests to wait, compounding total response delays across larger user groups.1

Workstation Latency on the NVIDIA RTX Pro 4500

Workstation deployments highlight the massive gap in per-user responsiveness. On an NVIDIA RTX Pro 4500 with 32GB GDDR7 running Llama 3.1 8B, single-user Ollama achieves 134 t/s with approximately 500ms TTFT. On the same hardware, vLLM BF16 reaches 2,031 t/s with approximately 25ms TTFT, supporting roughly 67 concurrent users at 30 t/s. Running vLLM NVFP4 delivers even higher capacity, reaching 4,870 t/s with approximately 13ms TTFT, which supports approximately 160 concurrent users at 30 t/s. Four independent Ollama instances across four GPUs provide 148 t/s per user with zero contention, demonstrating that Ollama requires isolated hardware instances rather than unified engine batching to sustain fast generation speeds.2

For a task generating 256-token responses across multiple consecutive steps, inference-only completion time takes roughly 60 to 80 seconds on single-user Ollama versus 3 to 5 seconds on vLLM BF16 and 1 to 3 seconds on vLLM NVFP4. The cumulative difference in time-to-first-token and token throughput compounds rapidly during multi-turn exchanges.2

Hardware Constraints and Quantization Support

Hardware compatibility marks the strongest boundary between the two frameworks. Ollama operates on Linux, Windows, and macOS, supporting NVIDIA, Apple Silicon/Metal, and AMD Radeon GPUs alongside CPU execution. Ecosystem adoption reflects this accessibility: Ollama reached 52 million monthly downloads in Q1 2026, marking a 520-fold rise from 100,000 downloads in Q1 2023, while llama.cpp surpassed 100,000 GitHub stars in March 2026. Furthermore, more than 60 percent of quantized models on Hugging Face ship in the GGUF format established by llama.cpp. However, vLLM serves HuggingFace-format models with support for FP16, AWQ, GPTQ, and NVFP4 quantization. Specifically, NVFP4 quantization support on NVIDIA Blackwell architecture requires vLLM and is unsupported on Ollama. Memory footprints also dictate setup requirements: loading the Qwen/Qwen3-14B model in vLLM required 27.5185 GiB of GPU memory, whereas a 32B model using Q4 quantization fits within a single 32GB GPU and delivers around 36 t/s locally on Ollama.352

Tuning Ollama for Parallel Requests

Operating Ollama in shared settings requires manual environment adjustments. Ollama is configured out-of-the-box to handle a default maximum of 4 requests in parallel. Setting OLLAMA_NUM_PARALLEL to 32 was the highest stable parallelism value achieved on an NVIDIA A100 GPU during Red Hat's testing. However, tuned Ollama's inter-token latency became erratic with large spikes at higher concurrency levels due to potential head-of-line blocking. Memory retention also influences server readiness: Ollama models stay loaded in memory for a default duration of 5 minutes following a request. To monitor throughput internally, response generation speed in tokens per second in Ollama's API is calculated by dividing eval_count by eval_duration and multiplying by 10^9, as all duration values in Ollama's API are returned in nanoseconds.16

Choose Ollama for local development on macOS or CPU environments where concurrency remains below 5 users. Migrate to vLLM when deploying to production servers with NVIDIA GPUs to handle high concurrency and multi-step agent workloads efficiently.