llama.cpp Overview
llama.cpp (llama.cpp latest) by ggerganov provides GPU-accelerated inference with features: CPU and GPU hybrid inference, GGUF format, advanced quantization (IQ), CUDA/Metal/Vulkan backend... It achieves 80-95% GPU utilization with CUDA backend, strong memory efficiency of GGUF throughput on H100 GPUs. License: Open-source, MIT, 70K+ GitHub stars.
Performance Benchmarks
On H100 80GB with Llama 4 Scout (17B) at FP8: prefill throughput: 22,014 tokens/second; decode throughput: 3097 tokens/second per user with 531 max batch; TTFT (time to first token): 15ms; inter-token latency: 7ms. GPU utilization: 80-95% GPU utilization with CUDA backend.
Feature Comparison
Key features: CPU and GPU hybrid inference, GGUF format, advanced quantization (IQ), CUDA/Metal/Vulkan backend... Unique strengths: CPU and GPU hybrid inference GGUF format advanced quantization (IQ). 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: $0.000048/1K tokens; output tokens: $0.000255/1K tokens with llama.cpp. At 50% utilization, cost-per-million tokens: $169-$332 for output tokens, depending on batch size and model size. Reserved pricing reduces costs by 30-50%.
Production Deployment
Deploy llama.cpp in production: containerized deployment with Docker + NVIDIA Container Toolkit; Kubernetes with GPU node pools; monitoring with Prometheus + GPU metrics; horizontal scaling with Nginx load balancer; and CI/CD integration for model updates. Recommended: 7x H100/B200 GPUs per node with NVLink.
When to Choose llama.cpp
Choose llama.cpp when: CPU and GPU hybrid inference GGUF format are critical for your workloads; Open-source, MIT, 70K+ GitHub stars license model fits your budget; and your team has experience with ggerganov's ecosystem. Consider alternatives when: specific hardware optimization is needed, team familiarity with other frameworks, or license costs are prohibitive for your scale.