Local LLM Serving in 2026: Ollama vs vLLM (PagedAttention & High-Throughput Benchmarks)
Deploying Ollama into multi-tenant production environments is an anti-pattern. While Ollama excels at local single-user CLI pair programming, vLLM is the undisputed production standard for high-throughput enterprise serving.
AM LABPagedAttention Memory Virtualization Mechanics
By adopting OS-style non-contiguous page table mappings for KV caches, vLLM virtually eliminates memory fragmentation, boosting concurrent capacity by 400%.
Deploying OpenAI-Compatible vLLM Endpoints
Execute high-concurrency model endpoints with automatic prefix caching enabled.
Throughput Benchmarks at 50 Concurrent Clients
Compare single-thread latency versus saturation throughput across heavy enterprise workloads.
Silicon Telemetry & Production Verification
vllm serve local-llms-ollama-vllm-guide --tensor-parallel-size 4 --gpu-memory-utilization 0.95 --max-model-len 32768 --kv-cache-dtype fp8
Artur Moiseyev
AI Systems Architect
Independent AI Researcher and Distributed Systems Architect. Building autonomous multi-agent workflows, open reasoning models, and real-time inference infrastructure.
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