Halkali merkez neighborhood fatih st ozgur apt no 46 , Kucukcekmece , Istanbul , 34303 , TR
Choosing an inference engine is broader than asking which is fastest. Model format, GPU count, VRAM, quantization, concurrency, API standards, deployment complexity and latency targets make different engines preferable for different workloads.
Broadly, vLLM and SGLang are strong for high-throughput multi-GPU production serving, Ollama for simple local/self-hosted model management, and llama.cpp for GGUF quantization plus broad CPU/GPU hybrid hardware support. Final selection should be benchmarked with the same model, context and concurrency.
This is not an absolute speed ranking; it highlights deployment characteristics.
Clarify the deployment goal to form a shortlist before benchmarking.
GPU clusters and throughput
Easy model management/API
Quantization + hybrid offload
Use the same model revision/quantization, context, output tokens, concurrency and GPU. Record TTFT, TPOT, throughput, VRAM and error rate together.
Manage TLS, authentication, quotas, rate limits, request logs and tenant policies at a separate gateway/reverse proxy. Do not expose engine health/metrics endpoints publicly.
GPU clock/VRAM, CPU, RAM and driver information should be part of every benchmark report.
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nvidia-smilscpu | head -n 20free -huname -apython3 --versionIt depends on model, GPU, context and concurrency. Benchmark TTFT/TPOT/throughput on the same workload.
Yes, with careful concurrency, queue, context and API-security planning. Large multi-GPU serving may favor vLLM/SGLang.
GGUF quantization, broad hardware backends and CPU+GPU hybrid inference make it strong on constrained or heterogeneous hardware.
Share model, GPU, context, concurrency and latency goals; we can build a comparative vLLM/SGLang/Ollama/llama.cpp benchmark plan.