Strategic Technology Assessment
vLLM vs Ollama
Making the Case for Production‑Grade LLM Serving
March 2026 · Infrastructure & AI Platform Team
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Agenda
What We'll Cover
01
The Challenge
Why LLM serving matters for our business
02
Ollama vs vLLM
What each tool does and how they differ
03
Benchmarks & Cost Impact
Performance data and dollar savings
04
Risks & Migration Plan
Honest assessment and phased rollout
05
Recommendation
Our proposed next steps
The Problem
The LLM Serving Challenge
Every AI product needs an inference engine. Choosing the wrong one costs you money, speed, and reliability at scale.
Scalability
Handle hundreds of concurrent users without degradation. A single API call is easy — the 100th concurrent one reveals your engine's true limits.
GPU Efficiency
GPUs cost $2-8/hr each. The difference between 40% and 95% memory utilization means the difference between 10 GPUs and 3.
Production Reliability
Your serving engine must handle failures gracefully, expose metrics for monitoring, and integrate with existing infrastructure.
Overview
What is Ollama?
A popular tool for running LLMs locally — great developer experience, designed for individual use
Ollama wraps llama.cpp in a user-friendly CLI, making it trivially easy to pull and run open-source models locally. One command — ollama run llama3 — and you're running inference.
One-command install and model download
Beautiful CLI and desktop app experience
Great model library with Modelfile customization
Runs on CPU, Apple Silicon, and NVIDIA GPUs
Scaling Considerations
No continuous batching — requests queue sequentially
Single GPU only — no tensor parallelism
No built-in load balancing or clustering
Limited observability & monitoring tooling
Best for: Development & Prototyping
Overview
What is vLLM?
A production-grade inference engine born from UC Berkeley research, built for scale
Born from UC Berkeley research, vLLM introduced PagedAttention — a breakthrough in GPU memory management that achieves near-zero waste, enabling up to 24x higher throughput than naive implementations.
PagedAttention
Virtual memory for KV cache — 95%+ GPU utilization
Continuous Batching
Dynamic request scheduling without waiting
Tensor Parallelism
Split models across multiple GPUs seamlessly
Speculative Decoding
Use draft models to accelerate generation
95%+
GPU Memory Utilization
Built for Production
Head to Head
Architecture Comparison
| Feature |
vLLM |
Ollama |
| Memory Management |
✓ PagedAttention (virtual paging) |
– Standard pre-allocation |
| Request Batching |
✓ Continuous batching |
✗ Sequential processing |
| Multi-GPU Support |
✓ Tensor & pipeline parallelism |
✗ Single GPU only |
| API Compatibility |
✓ Full OpenAI-compatible API |
✓ OpenAI-compatible API |
| Model Formats |
✓ HuggingFace, AWQ, GPTQ, GGUF |
– GGUF via Modelfile |
| Distributed Serving |
✓ Ray-based multi-node |
✗ Not supported |
| Monitoring |
✓ Prometheus + Grafana native |
– Basic API stats |
| Setup Complexity |
– Moderate (Python, CUDA) |
✓ Minimal (single binary) |
Performance
Throughput Under Load
Tokens per second across increasing concurrency (Llama 3 70B, A100 80GB)
19×
Source: Red Hat AI Platform Team, "LLM Serving Engine Benchmarks" (2024) · Reproduced on A100 80GB, Llama 3 70B, output length 512 tokens
ROI
Cost Analysis
Translating throughput gains into real infrastructure savings
Current: Ollama Setup
GPUs required (100 concurrent users)10× A100
Cost per GPU (on-demand)$3.00/hr
Daily cost (24h)$720/day
Annual cost
$262,800
Proposed: vLLM Setup
GPUs required (100 concurrent users)3× A100
Cost per GPU (on-demand)$3.00/hr
Daily cost (24h)$216/day
Annual cost
$78,840
Annual Savings
$0K
70% cost reduction
Based on A100 on-demand pricing.
Reserved instances reduce both, but the
relative savings remain the same.
"
The right serving engine turns a 10-GPU cluster into a 3-GPU one — while serving more users, faster.
— Operational insight from Red Hat AI Platform benchmarks
Enterprise Ready
Production Features
vLLM ships with everything you need for enterprise deployment
Multi-LoRA Serving
Serve multiple fine-tuned adapters from a single base model simultaneously
OpenAI-Compatible API
Drop-in replacement — swap endpoint URL, keep your existing code
Prometheus Metrics
Native integration with Grafana dashboards for real-time monitoring
Distributed Inference
Scale across multiple nodes with Ray for large model deployments
Speculative Decoding
Use smaller draft models to predict tokens and verify in parallel
Structured Output
JSON mode and guided generation for reliable structured responses
Prefix Caching
Cache system prompts across requests for faster TTFT
Quantization Support
AWQ, GPTQ, FP8, and INT8 — run larger models on fewer GPUs
Container Ready
Official Docker images and Kubernetes Helm charts
Positioning
When to Use Each Tool
They're not competitors — they serve different stages of the AI lifecycle
- Local development & experimentation
- Quick prototyping and model testing
- Individual developer workstations
- Learning and demos
- Low-traffic internal tools
- Customer-facing production APIs
- High-concurrency serving (10+ users)
- Multi-GPU / multi-node deployments
- Cost-optimized inference clusters
- Enterprise SLA requirements
Due Diligence
Risks & Mitigations
We've assessed the key concerns — here's how we address each one
Learning Curve
Low Risk
vLLM uses the same OpenAI-compatible API. Existing code needs only an endpoint URL change. Python-native tooling familiar to our ML team.
Project Maturity
Mitigated
30K+ GitHub stars, backed by UC Berkeley. Used in production by Alibaba, Samsung, and numerous AI startups. Active 2-week release cycle.
Migration Downtime
Mitigated
Phased rollout eliminates downtime. Run vLLM alongside Ollama during pilot. Blue-green deployment with instant rollback capability.
CUDA/GPU Dependency
Accepted
Requires NVIDIA GPUs with CUDA. This is already our target infra. vLLM also supports AMD ROCm as a fallback path.
Roadmap
Migration Plan
A phased approach to adopting vLLM with minimal risk
Phase 1
Pilot
Deploy vLLM in staging alongside Ollama. Benchmark with real workloads. Validate API compatibility.
Week 1–2
Phase 2
Staged Rollout
Migrate non-critical workloads first. Set up Prometheus monitoring. Train the team on operations.
Week 3–4
Phase 3
Full Production
Complete migration of all production workloads. Optimize GPU allocation. Decommission redundant infra.
Week 5–6
Recommendation
Adopt vLLM for Production
19× throughput at scale — serve more users on existing hardware
73% infrastructure savings — same workload, fewer GPUs
Zero code changes — OpenAI-compatible API, drop-in swap
Let's Start the Pilot This Sprint
Keep Ollama for local development · Deploy vLLM for everything production