Ollama vs vLLM - Which Fits Your Deployment

Ollama vs. vLLM: Choosing the Right LLM Serving Stack

Running large language models locally or on-premises is no longer a niche practice. Researchers, engineers, and development teams are increasingly serving models in-house to maintain data control, reduce cloud costs, and support agentic AI workflows that make dozens of inference calls per task.

Ollama and vLLM are the two most widely adopted tools for this, but they solve different problems. Choosing the wrong one at the wrong stage of deployment creates real performance bottlenecks, and understanding the tradeoffs upfront saves time and avoidable infrastructure issues down the road.

This post breaks down how each tool works, where each fits, and how to make the call.

Comparing Ollama and vLLM

Ollama is a lightweight, single-command LLM server built on llama.cpp. It uses the GGUF model format, runs on bare metal without containers, and exposes a REST API on localhost with an OpenAI-compatible mode. The install-to-inference experience takes minutes, and no DevOps background is required.

• Ollama addresses the need for fast, frictionless local model access for individual users and developers
• Ollama optimizes for single-user responsiveness and ease of use

vLLM is a GPU-first LLM inference engine built for throughput and scale. It runs via Docker or a Python environment, serves HuggingFace-format models with support for FP16, AWQ, GPTQ, and NVFP4 quantization, and exposes the same OpenAI-compatible API. Setup requires more configuration, but unlocks capabilities Ollama is not designed to provide.

• vLLM addresses the need for high-throughput, concurrent inference in shared or production environments
• vLLM optimizes for memory efficiency and simultaneous request handling at scale

The Core Difference: Requests and Concurrency

Ollama processes requests sequentially by default. Under single-user conditions this is fine, but as concurrent users increase, requests queue up and latency compounds. Tuning Ollama for parallelism helps at the margins but does not resolve the underlying architecture.

• Ollama reserves a fixed memory block per model; one active model per instance with sequential request handling
• Ollama processes one request at a time; additional requests wait in queue

vLLM was built around two technologies that directly address concurrent load: PagedAttention and continuous batching. Together they allow vLLM to serve many simultaneous requests on the same hardware without the queuing overhead that Ollama introduces.

• vLLM uses PagedAttention to allocate GPU KV cache memory in small pages on demand, freeing memory for more simultaneous requests
• vLLM uses continuous batching to pull new requests into the execution pipeline as soon as compute is available

The practical result is significant. At 128 concurrent connections, vLLM delivers substantially higher throughput and faster time-to-first-token compared to Ollama, which begins to fail requests entirely under that load. One documented case saw an internal knowledge assistant's P95 latency jump from 3 seconds to over a minute when user count grew from 3 to 40 on Ollama. Migrating to vLLM brought P95 latency back to under 2 seconds on the same hardware.

Performance Benchmarks at a Glance (NVIDIA RTX Pro 4500)

The numbers below are measured results from a single NVIDIA RTX Pro 4500 (32GB GDDR7) running Llama 3.1 8B. They illustrate how serving stack choice affects throughput, latency, and concurrent capacity on identical hardware.

A few things to note about these numbers:

• Ollama's 134 t/s is single-user throughput, it does not scale with additional concurrent users on the same instance
• Four independent Ollama instances across four GPUs each deliver 148 t/s per user with zero contention, which is a valid deployment pattern for small teams where each researcher needs a dedicated lane
• NVFP4 support on Blackwell requires vLLM and is not supported on Ollama; BF16 results apply more broadly
• The 4x fleet result uses four independent vLLM instances. The number provided is a projection.
• Model format differs between tools: Ollama uses GGUF models, while vLLM serves HuggingFace-format models in FP16, AWQ, GPTQ, or NVFP4. Throughput differences reflect both architecture and quantization choices.

What the numbers mean for agent loops

Agent loops turn serving behavior into a compounding problem. When a workflow needs 20 to 100 sequential model calls, per-step latency and concurrency limits add up quickly. That is where vLLM’s batching and KV-cache efficiency translate into practical time savings.

Raw throughput figures do not communicate much on their own. The table below translates them into 20-step agent completion time, which is a more practical unit for teams running agentic workflows.

When reading a streamed response, the difference between 25ms and 500ms TTFT is barely perceptible. For an agent making 20 sequential inference calls, that gap accumulates into meaningful task completion time differences.

Larger model sizes on the same hardware?

The benchmarks above use 8B models, but the same hardware can run larger models without moving to the cloud. A 32B model at Q4 quantization fits on a single 32GB card and delivers around 36 t/s on Ollama, fully local with no cloud dependency. For most agentic instruction-following and tool-use tasks, 8B to 14B models are sufficient; 32B is available when reasoning complexity demands it.

What do these numbers not cover?

Inference time is not the same as total task completion time. Real agent workflows include tool execution, web search, file I/O, and human review, so end-to-end runtime can still be minutes. The goal is to keep inference from becoming the bottleneck, and at these throughput levels, it usually is not.

Choosing Between Ollama and vLLM

The choice between Ollama and vLLM is primarily a question of how many users are sharing the hardware and what stage of deployment you are in. The table below captures the most common scenarios.

A practical approach for most teams is to start with Ollama during local development and prototyping, then migrate to vLLM when concurrent demand or throughput requirements outgrow what Ollama can handle. The migration is a URL change and a model format conversion, not a rewrite. Any agent code running against Ollama works against vLLM without modification once the endpoint is updated.

Popular agentic frameworks including LangChain, LangGraph, CrewAI, and AutoGen connect to both tools through the same OpenAI-compatible API. No framework-level changes are needed when moving between serving stacks.

FAQ: Ollama vs vLLM

When is Ollama the better option?

Choose Ollama for quick local prototyping or a single-user workstation where simplicity matters more than concurrency. Models can be loaded quickly, even for non-technical users.

When is vLLM the better option?

Use vLLM when multiple users or agents need to run concurrently, or when you need predictable latency and higher throughput. vLLM also supports NVFP4 natively on NVIDIA Blackwell GPUs. Setup is a little more technical but not difficult to learn.

Do I need to change my agent framework code to switch?

Usually no. Most frameworks can point to either tool via the same OpenAI-compatible API. The main change is the endpoint and model format.

Can both tools meet on-prem and sensitive-data requirements?

Yes—both can serve models on your own hardware so prompts, context, and outputs stay on-prem

Conclusion

Ollama and vLLM are complementary tools, not competing ones. Ollama removes all friction from getting a model running locally, which makes it the right starting point for most practitioners. vLLM removes throughput and concurrency as limiting factors, which makes it the right infrastructure choice when deployment scales beyond a single user.

The decision point is straightforward. If you are a single user running models on your own hardware, Ollama is sufficient and significantly easier to operate. Once you are sharing hardware across a team, running concurrent agentic sessions, or need predictable latency under load, vLLM is the correct choice. Most teams will use both at different points, and the path between them is intentionally low-friction.

If you have questions on what kinds of models you can run on any given deployments, talk to our engeineers today at Exxact to help build you your GPU system today. Whether it’s an NVIDIA DGX Spark or a node of NVIDIA DGX B300 Servers.