Comprehensive comparison of LLM inference and serving engines. Choose the right tool for your use case based on performance, features, and hardware requirements.
| Engine |
Best For |
Throughput |
Memory |
Hardware |
Ease of Use |
| vLLM |
Production serving |
⭐⭐⭐⭐⭐ |
⭐⭐⭐⭐⭐ |
Wide |
⭐⭐⭐⭐ |
| SGLang |
Multi-turn conversations |
⭐⭐⭐⭐⭐ |
⭐⭐⭐⭐ |
NVIDIA/AMD |
⭐⭐⭐ |
| Ollama |
Local development |
⭐⭐⭐ |
⭐⭐⭐ |
Wide |
⭐⭐⭐⭐⭐ |
| TGI |
HuggingFace ecosystem |
⭐⭐⭐⭐ |
⭐⭐⭐⭐ |
NVIDIA |
⭐⭐⭐⭐ |
| TensorRT-LLM |
NVIDIA optimization |
⭐⭐⭐⭐⭐ |
⭐⭐⭐⭐⭐ |
NVIDIA only |
⭐⭐ |
| LMDeploy |
Mixed precision |
⭐⭐⭐⭐ |
⭐⭐⭐⭐ |
Wide |
⭐⭐⭐ |
| Feature |
vLLM |
SGLang |
| Developer |
UC Berkeley Sky Lab |
LMSYS (Chatbot Arena) |
| Core Technology |
PagedAttention |
RadixAttention |
| Throughput (H100) |
12,553 tok/s |
16,215 tok/s (+29%) |
| Output Throughput |
413 tok/s |
894 tok/s (+116%) |
| TTFT |
103 ms |
79 ms |
| ITL |
7.14 ms |
6.03 ms |
| Memory Waste |
<4% |
~5% |
| Hardware |
NVIDIA, AMD, Intel, TPU |
NVIDIA, AMD |
| Multi-turn |
Good |
Excellent (+10%) |
| Setup |
Simple |
Moderate |
| Community |
73.9k stars |
Growing fast |
- ✅ Broad hardware support needed (TPU, Intel, AWS)
- ✅ Maximum model compatibility required
- ✅ Memory-constrained environments
- ✅ Rapid prototyping (simpler setup)
- ✅ Encoder-decoder models (T5, BART)
- ✅ Heterogeneous GPU clusters
- ✅ Multi-turn conversations (chatbots, agents)
- ✅ Maximum throughput is critical
- ✅ Low latency requirements (sub-100ms TTFT)
- ✅ DeepSeek models (MLA-optimized)
- ✅ Structured output generation
- ✅ NVIDIA or AMD GPUs only
| Feature |
vLLM |
Ollama |
| Primary Use |
Production serving |
Local development |
| Performance |
High throughput |
Good for single-user |
| GPU Support |
Multi-GPU, distributed |
Single GPU focus |
| API |
OpenAI-compatible |
Custom API |
| Model Format |
HuggingFace |
Custom (GGUF-based) |
| Quantization |
AWQ, GPTQ, FP8 |
GGUF (Q4, Q5, Q8) |
| Memory Efficiency |
PagedAttention |
GGML memory mgmt |
| Ease of Use |
Moderate |
Very easy |
| Docker |
Official images |
Official images |
| Community |
73.9k stars |
100k+ stars |
- ✅ Production API endpoints
- ✅ Multi-user concurrent access
- ✅ OpenAI API compatibility needed
- ✅ Maximum throughput required
- ✅ Enterprise deployments
- ✅ Local development and testing
- ✅ Single-user applications
- ✅ Easy setup is priority
- ✅ Running on consumer hardware
- ✅ macOS with Apple Silicon
| Feature |
vLLM |
TGI |
| Developer |
UC Berkeley |
HuggingFace |
| Performance |
2-4× FasterTransformer |
Optimized for HF models |
| Memory |
PagedAttention (<4% waste) |
Custom memory mgmt |
| Hardware |
Wide (NVIDIA, AMD, TPU) |
NVIDIA primary |
| HF Integration |
Direct loading |
Native integration |
| Quantization |
AWQ, GPTQ, FP8 |
GPTQ, AWQ, EETQ |
| API |
OpenAI-compatible |
OpenAI-compatible |
| Docker |
vllm/vllm-openai |
ghcr.io/huggingface/text-generation-inference |
| Community |
73.9k stars |
10k+ stars |
- ✅ Maximum throughput needed
- ✅ Better memory efficiency required
- ✅ Broader hardware support
- ✅ Multi-GPU tensor parallelism
- ✅ Deep HuggingFace integration needed
- ✅ Using HF Inference Endpoints
- ✅ GPTQ quantization priority
- ✅ HuggingFace ecosystem tools
| Feature |
vLLM |
TensorRT-LLM |
| Developer |
UC Berkeley |
NVIDIA |
| Performance |
Excellent |
Best on NVIDIA |
| Hardware |
Wide |
NVIDIA only |
| Setup Complexity |
Moderate |
High |
| Model Support |
Broad (HF models) |
Curated models |
| Optimization |
General |
NVIDIA-specific |
| FP8 Support |
Yes |
Yes (optimized) |
| Multi-GPU |
Tensor/Pipeline parallel |
Multi-GPU optimized |
- ✅ Non-NVIDIA hardware
- ✅ Quick deployment needed
- ✅ Broad model support required
- ✅ Flexibility over max performance
- ✅ NVIDIA GPUs only
- ✅ Maximum performance critical
- ✅ Production at scale on NVIDIA
- ✅ Engineering resources available
| Engine |
License |
Hardware |
Best Use Case |
| vLLM |
Apache-2.0 |
Wide |
General production serving |
| SGLang |
Apache-2.0 |
NVIDIA/AMD |
Multi-turn conversations |
| TGI |
Apache-2.0 |
NVIDIA |
HuggingFace ecosystem |
| TensorRT-LLM |
Apache-2.0 |
NVIDIA |
NVIDIA-optimized deployments |
| LMDeploy |
Apache-2.0 |
Wide |
Mixed precision inference |
| DeepSpeed-MII |
MIT |
NVIDIA |
Microsoft ecosystem |
| Engine |
License |
Hardware |
Best Use Case |
| Ollama |
MIT |
Wide |
Local development |
| LM Studio |
Proprietary |
Wide |
Desktop GUI |
| GPT4All |
MIT |
CPU/GPU |
CPU inference |
| KoboldCpp |
AGPL-3.0 |
CPU/GPU |
Creative writing |
| text-generation-webui |
AGPL-3.0 |
Wide |
Web UI for testing |
| Engine |
License |
Hardware |
Specialization |
| Triton Inference Server |
BSD-3 |
Wide |
Multi-framework serving |
| BentoML |
Apache-2.0 |
Wide |
ML model serving |
| Ray Serve |
Apache-2.0 |
Wide |
Scalable serving |
| Anyscale |
Commercial |
Wide |
Managed Ray |
| Engine |
Tokens/sec |
Relative |
| SGLang |
16,215 |
+29% |
| vLLM |
12,553 |
Baseline |
| TGI |
~10,000 |
-20% |
| Ollama |
~6,000 |
-52% |
| HF Transformers |
~2,500 |
-80% |
| Engine |
TTFT (ms) |
Relative |
| SGLang |
79 |
Fastest |
| vLLM |
103 |
Baseline |
| TGI |
~120 |
+17% |
| Ollama |
~200 |
+94% |
| Engine |
Memory Waste |
Concurrent Requests |
| vLLM |
<4% |
2-4× baseline |
| SGLang |
~5% |
2-3× baseline |
| TGI |
~10% |
2× baseline |
| HF Transformers |
60-80% |
1× baseline |
| Feature |
vLLM |
SGLang |
Ollama |
TGI |
TensorRT-LLM |
| OpenAI API |
✅ |
✅ |
❌ |
✅ |
❌ |
| PagedAttention |
✅ |
❌ |
❌ |
❌ |
❌ |
| RadixAttention |
❌ |
✅ |
❌ |
❌ |
❌ |
| Multi-GPU |
✅ |
✅ |
❌ |
✅ |
✅ |
| Quantization |
AWQ/GPTQ/FP8 |
AWQ/GPTQ/FP8 |
GGUF |
GPTQ/AWQ |
FP8/INT8 |
| Prefix Caching |
✅ |
✅ |
❌ |
✅ |
✅ |
| Speculative Decoding |
✅ |
✅ |
❌ |
✅ |
✅ |
| Multi-LoRA |
✅ |
✅ |
❌ |
✅ |
✅ |
| Chunked Prefill |
✅ |
✅ |
❌ |
✅ |
❌ |
| TPU Support |
✅ |
❌ |
❌ |
❌ |
❌ |
| AMD Support |
✅ |
✅ |
✅ |
Limited |
❌ |
| Apple Silicon |
✅ |
❌ |
✅ |
❌ |
❌ |
# Before: HuggingFace
from transformers import pipeline
pipe = pipeline("text-generation", model="meta-llama/Llama-2-7b-chat-hf")
result = pipe("Hello, my name is", max_length=100)
# After: vLLM
from vllm import LLM, SamplingParams
llm = LLM(model="meta-llama/Llama-2-7b-chat-hf")
params = SamplingParams(max_tokens=100)
result = llm.generate("Hello, my name is", params)
# Ollama API
curl http://localhost:11434/api/generate -d '{
"model": "llama2",
"prompt": "Hello"
}'
# vLLM API (OpenAI-compatible)
curl http://localhost:8000/v1/completions -H "Content-Type: application/json" -d '{
"model": "llama-2-7b",
"prompt": "Hello",
"max_tokens": 100
}'
# TGI Docker
docker run --gpus all -p 8080:80 \
ghcr.io/huggingface/text-generation-inference:latest \
--model-id meta-llama/Llama-2-7b-chat-hf
# vLLM Docker
docker run --gpus all -p 8000:8000 \
vllm/vllm-openai:latest \
--model meta-llama/Llama-2-7b-chat-hf
-
What is your primary use case?
- Production API → vLLM, SGLang, TGI
- Local development → Ollama, LM Studio
- Maximum performance → TensorRT-LLM, SGLang
-
What hardware do you have?
- NVIDIA only → Any engine
- AMD → vLLM, SGLang, Ollama
- TPU → vLLM
- Apple Silicon → Ollama, vLLM (CPU)
- CPU only → Ollama, GPT4All
-
What are your performance requirements?
- Maximum throughput → SGLang, vLLM
- Low latency → SGLang, vLLM
- Good enough → Ollama, TGI
-
What is your deployment environment?
- Cloud/Kubernetes → vLLM, TGI, TensorRT-LLM
- Docker → All engines
- Desktop → Ollama, LM Studio
- Edge → Ollama, GPT4All
-
What models do you need?
- HuggingFace models → vLLM, TGI
- GGUF models → Ollama
- Custom models → TensorRT-LLM
| Scenario |
Recommended Engine |
| General production serving |
vLLM |
| Chatbots and multi-turn |
SGLang |
| Local development |
Ollama |
| HuggingFace ecosystem |
TGI |
| NVIDIA-only deployment |
TensorRT-LLM |
| Apple Silicon Mac |
Ollama |
| CPU inference |
Ollama, GPT4All |
| Maximum throughput |
SGLang |
| Best memory efficiency |
vLLM |
| Easiest setup |
Ollama |
Any questions?
Feel free to contact us. Find all contact information on our contact page.