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Optimized for approachable local model execution and developer experience.
Halkali merkez neighborhood fatih st ozgur apt no 46 , Kucukcekmece , Istanbul , 34303 , TR
A single tokens/s screenshot cannot compare three engines. Ollama emphasizes approachable local usage, vLLM high-throughput OpenAI-compatible serving, and SGLang an advanced serving runtime with caching optimizations. We publish no performance numbers without identical model, quantization and GPU.
The benchmark generator creates a test protocol, not performance results. Fill result cells only from measurements using identical model files, quantization, context, GPU, driver and thermal conditions.
Ollama can excel for simple local developer workflows. vLLM is a strong candidate for multi-user OpenAI-compatible serving. SGLang is worth testing for prefix-heavy agent/RAG workloads and advanced serving optimization. Benchmark with your own prompt distribution.
Ollama can excel for simple local developer workflows. vLLM is a strong candidate for multi-user OpenAI-compatible serving. SGLang is worth testing for prefix-heavy agent/RAG workloads and advanced serving optimization. Benchmark with your own prompt distribution.
Compare Ollama, vLLM and SGLang fairly on the same GPU/model across usability, OpenAI APIs, batching, prefix caching, multi-GPU and a repeatable benchmark matrix.
Optimized for approachable local model execution and developer experience.
Focused on continuous batching and OpenAI-compatible serving.
Provides advanced serving optimizations including prefix-oriented caching.
Hardware, model and quantization must stay fixed across engines.
Local chat, one API client, 100 concurrent users and agentic RAG place different demands on a serving engine.
| Profile | Priority | First candidate |
|---|---|---|
| Local developer | Setup simplicity | Ollama |
| OpenAI API / multi-user | Throughput + batching | vLLM |
| Agent/RAG repeated prefix | Prefix/cache efficiency | SGLang / vLLM |
| Model lab | Fast model iteration | Ollama / vLLM |
“OpenAI-compatible” does not imply every vendor feature is identical. Smoke-test chat, responses, embeddings, tool calling and structured outputs separately.
KV-cache allocation, CUDA graphs, kernels and runtime overhead differ by engine. Measure peak VRAM and OOM margin instead of merely “model fits.”
watch -n 0.5 nvidia-sminvidia-smi --query-compute-apps=pid,process_name,used_memory --format=csvRepeated agent system prompts, tool schemas and RAG context can make prefix-cache behavior important in production.
| Test | Input pattern |
|---|---|
| Cold | Different 4k each request |
| Warm prefix | Same 4k prefix + 256 unique |
| RAG | 8k shared docs + question |
Results are invalid if engines receive different prompt distributions. Freeze the prompt corpus and record its SHA256.
sha256sum benchmark-prompts.jsonlnvidia-smi --query-gpu=name,driver_version,memory.total,temperature.gpu,power.draw --format=csvReport 2×GPU throughput divided by 1×GPU throughput alongside latency changes. Total tokens/s alone can hide interconnect overhead.
| Engine | 1 GPU tok/s | 2 GPU tok/s | Scaling |
|---|---|---|---|
| Ollama | — | — | — |
| vLLM | — | — | — |
| SGLang | — | — | — |
Weight deployment simplicity, API compatibility, p95 latency, throughput, VRAM headroom, observability and upgrade risk according to your workload.
Choose concurrency and prompt profile to generate a repeatable test matrix.
Build a staging benchmark on an Eka Sunucu GPU server with identical model/driver conditions for Ollama, vLLM and SGLang.
Primary documentation and technical references used by this guide.
Continue with related infrastructure and implementation guides.
LLM Serving Comparison
There is no valid universal answer without fixing model, quantization, GPU, prompts and concurrency. vLLM targets high-throughput serving, but benchmark your workload.
It is worth evaluating for workloads such as agent/RAG where repeated prefixes and advanced serving optimizations matter.
Format support differs. Use equivalent weights/quantization where possible and disclose format differences.
No. It generates repeatable test conditions; actual results must be measured on the GPU server.