Ollama Overview
Ollama (Ollama 0.5) by Ollama provides GPU-accelerated inference with features: Local model serving, model management, OpenAI-compatible API, quantization, multi-modal preview... It achieves 70-85% GPU utilization, simpler deployment, focused on single-GPU use cases throughput on H100 GPUs. License: Open-source, permissive, 100K+ GitHub stars.
Performance Benchmarks
On H100 80GB with Llama 4 Scout (17B) at FP8: prefill throughput: 11,394 tokens/second; decode throughput: 1350 tokens/second per user with 1053 max batch; TTFT (time to first token): 15ms; inter-token latency: 8ms. GPU utilization: 70-85% GPU utilization.
Feature Comparison
Key features: Local model serving, model management, OpenAI-compatible API, quantization, multi-modal preview... Unique strengths: Local model serving model management OpenAI-compatible API. Production features include: OpenAI-compatible API, streaming, tool calling.
Cost-Per-Token Analysis
Cost-per-token on H100 80GB at $2.50/hr: input tokens: $0.000029/1K tokens; output tokens: $0.000193/1K tokens with Ollama. At 50% utilization, cost-per-million tokens: $107-$154 for output tokens, depending on batch size and model size. Reserved pricing reduces costs by 30-50%.
Production Deployment
Deploy Ollama in production: containerized deployment with Docker + NVIDIA Container Toolkit; Kubernetes with GPU node pools; monitoring with Prometheus + GPU metrics; horizontal scaling with HAProxy + keepalived; and CI/CD integration for model updates. Recommended: 8x H100/B200 GPUs per node with NVLink.
When to Choose Ollama
Choose Ollama when: Local model serving model management are critical for your workloads; Open-source, permissive, 100K+ GitHub stars license model fits your budget; and your team has experience with Ollama's ecosystem. Consider alternatives when: specific hardware optimization is needed, team familiarity with other frameworks, or license costs are prohibitive for your scale.