vLLM is the default choice for production API serving, PagedAttention and continuous batching deliver up to 24x higher throughput than Ollama under concurrent load, it supports an OpenAI-compatible API out of the box, and setup takes minutes. Ollama is built for local development and single-user prototyping; it runs models in one command but degrades fast under concurrent requests (response times jump from 2 seconds to 45+ seconds). TensorRT-LLM extracts maximum performance from NVIDIA GPUs through aggressive kernel optimization and quantization, but requires a multi-step compilation workflow and deeper systems expertise, expect 30+ minutes to build an optimized engine for an 8B model. Choose vLLM when you need production throughput with reasonable setup effort, Ollama when you're prototyping or running models locally for personal use, and TensorRT-LLM when you're squeezing every millisecond on dedicated NVIDIA hardware and have the engineering team to maintain it.
Ollama is exceptional for one user on one laptop and falls apart the moment 15 people hit it simultaneously, response times jump from 2 seconds to nearly a minute. "Make Ollama faster" is the wrong question; Ollama was never designed for concurrent serving in the first place.
That same confusion shows up across teams self-hosting LLMs. The inference server you choose determines your throughput ceiling, your latency floor, and how much engineering time you'll spend keeping everything running. vLLM, Ollama, and TensorRT-LLM each solve different problems, and picking the wrong one costs months. Here's what actually matters when choosing between them.
What Each Inference Server Is Built For
These three tools aren't competing products solving the same problem. They're built for fundamentally different use cases, and understanding that distinction saves you from the most common deployment mistakes.
vLLM is a high-throughput inference engine designed for production API serving. Its core innovation, PagedAttention, borrows virtual memory concepts from operating systems to manage GPU memory dynamically. Instead of pre-allocating large contiguous memory blocks for each request's KV cache, vLLM divides memory into small pages allocated on demand. Combined with continuous batching (processing new requests as they arrive rather than waiting for fixed batch sizes), vLLM achieves throughput numbers that justify its position as the default choice for most production deployments. For a head-to-head comparison with SGLang, the other leading production engine, see our SGLang vs vLLM benchmark analysis.
Ollama is a local model runner built for simplicity. One command downloads and runs a model. No configuration files, no GPU tuning, no infrastructure decisions. It wraps llama.cpp with a clean CLI and REST API, making it the fastest path from "I want to try this model" to actually running it. Ollama is exceptional at what it's designed for, local experimentation, personal assistants, edge deployments where only one person is using the model at a time.
TensorRT-LLM is NVIDIA's performance optimization framework that compiles models into highly optimized GPU kernels. It squeezes maximum performance from NVIDIA hardware through graph optimization, kernel fusion, and aggressive quantization. The tradeoff is complexity, you're not just serving a model, you're compiling it through a multi-step build process that requires understanding batch sizes, sequence lengths, and quantization strategies before you serve a single request.
Throughput and Latency: The Numbers That Matter
Benchmark comparisons can be misleading when they test synthetic workloads nobody runs in production. Here's what consistent across real production deployments.
vLLM Performance Profile
On production workloads with concurrent users, vLLM consistently delivers strong throughput. In Red Hat's benchmarking against Ollama using identical hardware, vLLM achieved 793 tokens per second compared to Ollama's 41 TPS, a 19x difference. P99 latency sat at 80ms versus Ollama's 673ms. The v0.6.0 release brought a 2.7x throughput improvement and 5x latency reduction on Llama 8B (single H100) by fixing CPU bottlenecks that were consuming over 60% of execution time on overhead rather than GPU computation. vLLM's strength is sustained throughput under concurrent load. Where other servers degrade as users pile on, vLLM's continuous batching keeps GPU utilization high by inserting new requests into the processing pipeline at the iteration level rather than waiting for entire batches to complete.
Ollama Performance Profile
Ollama performs well for single-user workloads. Response times for individual requests are competitive, often within 10-20% of vLLM for a single user on the same hardware. The problem surfaces with concurrency. Ollama processes requests sequentially by default. Even with OLLAMA_NUM_PARALLEL configured, response times degrade dramatically under load. Teams report latencies jumping from 2 seconds to 45+ seconds with just 10 concurrent users. There's also the cold start issue. Ollama unloads models from memory after 5 minutes of inactivity by default. The first request after idle triggers a model reload that can take 30 seconds to 3+ minutes depending on model size and storage speed. For a personal tool this is acceptable. For a production API, it's a dealbreaker.
TensorRT-LLM Performance Profile
TensorRT-LLM achieves the lowest single-request latency of the three on NVIDIA hardware. NVIDIA's published benchmarks show Llama 3.3 70B (FP4) reaching 10,613 tokens per second on a B200 GPU. On H100s, the throughput advantage over vLLM is typically 15-30% for the same model and configuration, with even larger gains when using FP8 or FP4 quantization that TensorRT-LLM handles natively. The catch: these numbers assume a properly compiled and tuned engine. An unoptimized TensorRT-LLM build can actually perform worse than vLLM because you're paying compilation overhead without getting the optimization benefits. The performance ceiling is highest with TensorRT-LLM, but the floor is also lowest if you don't invest the engineering time to tune it. For context on broader latency optimization strategies, see our guide on fixing slow LLM latency in production.
Setup Complexity and Time to First Inference
How long it takes to go from "I have a model" to "I'm serving requests" varies dramatically across these three tools. This is often the deciding factor for teams with limited infrastructure experience.
ollama run llama3.1
pip install vllm vllm serve meta-llama/Llama-3.1-8B-Instruct
Ollama: Minutes to Running
That's it. Ollama downloads the model, configures GPU acceleration if available, and drops you into an interactive session. Expose it as an API with ollama serve and you have a REST endpoint. Total setup time: under 5 minutes, including model download. No Python environment, no dependency conflicts, no CUDA version mismatches. This simplicity is genuinely valuable for prototyping and local development.
vLLM: Minutes to Production-Ready
vLLM starts an OpenAI-compatible API server with sensible defaults. You get continuous batching, PagedAttention, and multi-GPU support without configuring anything. For custom needs, environment variables and CLI flags control tensor parallelism, quantization, max model length, and GPU memory utilization. Total setup time: 5-15 minutes depending on model download speed and whether you need custom configuration. The OpenAI-compatible API means any application built against the OpenAI SDK works with vLLM with just a base URL change. This drop-in compatibility eliminates weeks of integration work for teams migrating from API providers to self-hosted models.
TensorRT-LLM: Hours to Optimized
TensorRT-LLM requires a multi-step workflow: convert model checkpoints to TensorRT-LLM format, compile an optimized engine with your target batch sizes and sequence lengths, then deploy the engine for serving. A typical setup: Engine compilation alone takes 30+ minutes for an 8B model on H100. For a 70B model with FP8 quantization, expect 1-2 hours. Change your batch size requirements or sequence length? Recompile. Switch quantization strategies? Recompile. This rigidity is the cost of the performance optimization, the engine is compiled for specific parameters, not dynamically adaptive like vLLM.
- 1. Install the TensorRT-LLM container or pip package (with specific CUDA and TensorRT version requirements)
- 2. Convert the Hugging Face checkpoint to TensorRT-LLM format
- 3. Build the engine with
trtllm-build, specifying max batch size, max input/output lengths, quantization, and plugin configurations - 4. Deploy using the Triton Inference Server or the built-in serving layer
Cost and Infrastructure Considerations
Self-hosting models means paying for compute directly instead of through API margins. But the infrastructure cost varies significantly based on which serving framework you choose and how efficiently it uses your hardware.
GPU Memory Efficiency
vLLM's PagedAttention achieves near-zero waste in KV cache memory. A Llama 3.1 70B model that might consume 95% of an A100's 80GB VRAM under naive allocation leaves meaningful headroom under vLLM for larger batch sizes. This translates directly to serving more concurrent users on the same hardware, fewer GPUs needed for the same throughput target. Ollama's memory management is straightforward but less efficient at scale. Each model instance occupies a fixed memory footprint. Running multiple model instances for concurrency means multiplying memory requirements linearly. On a single GPU, you're practically limited to one model instance. TensorRT-LLM's memory efficiency depends on how well you've tuned the engine. With proper FP8 or FP4 quantization, TensorRT-LLM can fit models into significantly less VRAM than the other two options, enabling larger models on smaller GPUs or higher batch sizes on the same hardware.
Operational Cost Beyond Compute
Hardware is only part of the equation. Engineering time spent on infrastructure is often the larger cost, especially for smaller teams. Ollama requires almost zero operational overhead, until it doesn't meet your needs and you have to migrate. vLLM requires standard production infrastructure knowledge: monitoring, log management, auto-scaling configuration. It's comparable to running any production Python service. TensorRT-LLM carries the highest operational cost. Engine recompilation for model updates, CUDA version management, Triton Inference Server configuration, and performance tuning all require dedicated engineering time. Teams running TensorRT-LLM in production typically allocate 10-20 hours per month to maintenance, time that doesn't exist at most startups. For a deeper analysis of the cloud-versus-self-hosted cost tradeoff, see our breakdown of cloud vs on-premise AI infrastructure costs.
When to Choose Each Framework
Across deployments ranging from single-developer prototypes to multi-thousand-user production systems, here's how the decision usually shakes out.
Choose vLLM When:
You need production API serving with multiple concurrent users. This is the most common scenario, and vLLM handles it well without requiring infrastructure expertise beyond standard DevOps skills. The typical migration pattern, moving an AI-powered support tool from OpenAI's API to self-hosted Llama 3.1 on vLLM, delivers meaningful inference cost reductions while holding sub-100ms P95 latency under hundreds of concurrent users. You want OpenAI API compatibility. If your application is built against the OpenAI SDK and you want the option to switch between hosted and self-hosted models without code changes, vLLM's compatible API makes this trivial. Point your client at a different base URL and you're running locally. Your team doesn't have deep GPU optimization expertise. vLLM's defaults are production-reasonable. You don't need to understand kernel fusion or engine compilation to get good performance. The gap between default and optimized vLLM is much smaller than the gap between default and optimized TensorRT-LLM.
Choose Ollama When:
You're prototyping or running models for personal use. Ollama's one-command setup is unbeatable for trying out models, building proof-of-concepts, or running a local AI assistant. A common pattern: Ollama on developer laptops for testing prompt changes, vLLM on the production cluster for serving. You're deploying to edge devices or single-user scenarios. Embedded systems, personal workstations, or dedicated single-user terminals are valid Ollama use cases. The simplicity advantage persists as long as concurrency isn't a requirement. You want to try models before committing to infrastructure. Ollama's model library and simple CLI make it the fastest way to evaluate whether a model works for your use case before investing in production infrastructure.
Choose TensorRT-LLM When:
Latency is your competitive advantage and you have NVIDIA hardware. Real-time trading signal generation, live voice assistants, or any application where 20ms versus 50ms directly impacts user experience or revenue. On latency-critical workloads on H100 hardware, a tuned TensorRT-LLM engine can roughly halve P99 latency relative to a default vLLM deployment, after a couple of weeks of engine optimization. You're operating at scale where 15-30% efficiency gains justify the complexity. Running 50+ GPUs serving millions of requests daily? A 20% throughput improvement means 10 fewer GPUs, potentially $200,000+ annually in infrastructure savings. At that scale, the engineering investment in TensorRT-LLM optimization pays for itself quickly. Your team includes ML infrastructure engineers. TensorRT-LLM rewards expertise. Without someone who understands CUDA, kernel optimization, and the TensorRT compilation pipeline, you'll spend more time fighting the tool than benefiting from it.
Common Migration Paths and Mistakes
Teams rarely pick the right inference framework on the first try. Understanding the common progression saves you from the mistakes that keep recurring.
The Typical Journey
Most teams follow a predictable path: start with Ollama for prototyping, migrate to vLLM when they need production serving, and evaluate TensorRT-LLM only when they hit specific latency or scale requirements that vLLM can't meet. This progression makes sense, each step adds complexity proportional to the actual need. The mistake is skipping steps. Teams that jump straight to TensorRT-LLM for a prototype waste weeks on compilation and configuration when they should be validating whether their model even solves the problem. Conversely, teams that try to stretch Ollama into production serving burn months working around concurrency limitations that a migration to vLLM would solve in a day.
Migration Gotchas
Ollama to vLLM: Straightforward, but model format matters. Ollama uses GGUF format (from llama.cpp), while vLLM works with Hugging Face format natively. You may need to re-download models in the right format rather than converting. The API differences are minimal, both support chat completion endpoints, though request/response schemas differ slightly. vLLM to TensorRT-LLM: This is where complexity jumps. You're moving from a dynamic inference engine to a compiled one. Every model update requires engine recompilation. Performance testing workflows need to account for build times. Your CI/CD pipeline gets significantly more complex. Budget two weeks for the migration and tuning, not two days. TensorRT-LLM back to vLLM: More common than you'd expect. Teams that invested in TensorRT-LLM for marginal performance gains find the maintenance burden isn't worth the 15-20% latency improvement. Moving back to vLLM is easier than moving forward, fewer compilation steps, simpler deployment pipeline. For broader guidance on reducing inference costs across any serving framework, see our guide on reducing LLM token costs.
Making the Decision for Your Team
The inference serving landscape keeps evolving. SGLang is emerging as a compelling alternative to vLLM with strong structured output performance. NVIDIA continues simplifying TensorRT-LLM's setup. Ollama keeps adding features. But the core tradeoffs remain stable.
vLLM trades maximum possible latency for setup simplicity and production readiness. It's the right default for 80% of self-hosted LLM deployments, the safe pick when requirements aren't yet clear.
Ollama trades scalability for accessibility. Use it where simplicity matters and concurrency doesn't. Stop trying to make it a production server. For a deep-dive into the Ollama-to-vLLM decision specifically, see our dedicated Ollama vs vLLM comparison.
TensorRT-LLM trades engineering time for raw performance. It earns its complexity at scale, but most teams overestimate their need for it and underestimate the maintenance cost.
Start with what matches your current constraints, team size, hardware, traffic expectations. The inference server is infrastructure, not product. Get it working, validate your model actually solves the problem, and optimize the serving layer when the bottleneck is actually inference performance rather than the hundred other things that could be wrong first.
Frequently Asked Questions
Quick answers to common questions about this topic
TensorRT-LLM typically achieves lower per-request latency because it compiles models into optimized NVIDIA GPU kernels. However, vLLM often matches or exceeds TensorRT-LLM on throughput, requests served per second, thanks to PagedAttention and continuous batching. For most production workloads where you're serving many concurrent users, vLLM's throughput advantage matters more than raw single-request latency.
