vLLM vs SGLang vs LMDeploy: Fastest LLM Inference Engine in 2026?
SGLang and LMDeploy are the fastest LLM inference engines in 2026, both delivering approximately 16,200 tokens per second on H100 GPUs. vLLM follows at around 12,500 tokens per second, a 29% gap.
The best engine depends on your workload: SGLang excels at multi-turn conversations, LMDeploy dominates quantized model serving, and vLLM offers the most mature ecosystem for general production use.
That 29% throughput gap translates to roughly $15,000 in monthly GPU savings when serving a million requests daily. The difference compounds as traffic grows.
Three engines dominate open-source LLM serving: vLLM, SGLang, and LMDeploy. Each uses different architecture. Each wins in different scenarios. The following comparison draws from benchmark data across 13+ inference engines, explains the architectural differences driving performance gaps, and provides guidance for selecting the right one.
Quick Comparison: vLLM vs SGLang vs LMDeploy
| Feature | vLLM | SGLang | LMDeploy |
|---|---|---|---|
| Throughput (H100, Llama 3.1 8B) | ~12,500 tok/s | ~16,200 tok/s | ~16,100 tok/s |
| Core Technology | PagedAttention | RadixAttention | TurboMind (C++) |
| Multi-turn Performance | Good | Excellent (10-20% faster) | Good |
| Quantization Support | Int4, AWQ, GPTQ | FP4/FP8/Int4/AWQ/GPTQ | Best-in-class (2.4x faster at Int4) |
| Time to First Token | Excellent at low concurrency | Best with cache hits | Lowest overall |
| Setup Complexity | Easy (pip install) | Moderate | Moderate |
| Community Size | Largest | Growing (400K+ GPUs) | Medium |
| OpenAI API Compatible | Yes | Yes | Yes |
| Best For | General production, broad compatibility | Agentic workflows, chat, prefix reuse | Quantized models, memory-constrained |
SGLang and LMDeploy tie for raw throughput. vLLM trails by 29% but compensates with ecosystem maturity and easier deployment.
What is an LLM Inference Engine?
An LLM inference engine runs trained language models efficiently. It handles the computational work of generating text from prompts. Without optimization, a 7B parameter model takes seconds per response. With the right engine, responses arrive in milliseconds.
Four metrics matter most:
Throughput measures tokens generated per second. Higher throughput means more requests handled per GPU. The difference between 12,000 and 16,000 tokens per second means 33% more capacity from identical hardware.
Time to First Token (TTFT) measures how quickly users see the first word. For chat applications, this determines perceived responsiveness. Target under 100ms.
Inter-Token Latency (ITL) measures the gap between subsequent tokens during streaming. Consistent ITL creates smooth output. Variable ITL causes stuttering.
GPU Memory Consumption determines model size limits and concurrent request capacity. Lower memory per request means more requests per GPU.
vLLM: The Production Standard
vLLM became the default for production LLM serving because it solved memory fragmentation first. Before PagedAttention, running LLMs meant wasting 60-80% of GPU memory on fragmented KV caches.
PagedAttention Explained
Traditional inference engines allocate one contiguous memory block per sequence. External fragmentation accumulates as sequences of different lengths complete and free memory in scattered chunks.
PagedAttention treats the KV cache like virtual memory. Storage breaks into fixed-size pages (typically 16 tokens each) allocated on demand. When a sequence needs more cache space, vLLM assigns the next available page regardless of physical location.
Memory utilization improves from roughly 30% to over 90%. Throughput increases 2-4x compared to older inference stacks at similar latency.
vLLM Performance Data
On A100 80GB GPUs running Llama 2 7B Chat at float16 precision, vLLM delivers moderate throughput with high GPU memory consumption, similar to TensorRT-LLM. Output quality matches the base model with no degradation.
Continuous batching handles variable-length requests efficiently. New requests join the batch immediately rather than waiting for fixed batch windows, reducing average latency under load.
When vLLM Fits
vLLM works best when ecosystem maturity matters more than maximum performance. Documentation is extensive. Most open-source models work immediately. GitHub issues contain answers to common problems.
Good scenarios for vLLM:
- Teams prioritizing stability over optimization
- Workloads with primarily single-turn interactions
- Projects requiring broad model compatibility
- Organizations using Ray or Kubernetes integrations
The tradeoff shows in benchmarks. On H100 hardware with optimized configurations, vLLM peaks around 12,500 tokens per second while SGLang and LMDeploy reach 16,200. That 29% gap costs real money at volume.
SGLang: Multi-Turn and Agentic Workloads
SGLang emerged from UC Berkeley research, developed by some of the same researchers behind vLLM. The design question was different: optimize for LLM programs instead of independent requests.
The answer is RadixAttention, delivering up to 5x faster inference for workloads with shared prefixes.
RadixAttention Explained
Traditional engines discard the KV cache after each request completes. SGLang stores cached prefixes in a radix tree, a data structure representing shared token sequences efficiently. Each node holds a sequence of tokens and its associated KV cache pages.
When a new request arrives, the runtime checks for matching prefixes in the radix tree. Matches reuse cached computation. No redundant work for tokens already processed.
Cache hit rates vary by workload:
- Few-shot learning with shared examples: 85-95% (vs 15-25% with PagedAttention)
- Multi-turn chat with conversation history: 75-90% (vs 10-20%)
- Code analysis with common patterns: 60-80% (vs 5-15%)
- Single requests with no sharing: 0% (equivalent performance)
SGLang Performance Data
Independent testing on H100 GPUs running Llama 3.1 8B shows SGLang delivering approximately 16,200 tokens per second, 29% faster than vLLM. Multi-turn workloads gain an additional 10-20% from RadixAttention cache hits. Time to first token drops significantly when cache hits occur.
SGLang v0.4 introduced a zero-overhead batch scheduler keeping CPU scheduling overhead under 2% of total time. Previous versions spent 15-25% on scheduling.
Structured Output Generation
SGLang includes a compressed finite state machine for constrained decoding. When outputs need specific formats like JSON or XML, SGLang decodes multiple tokens at once by precomputing valid continuations. JSON decoding runs 3x faster compared to unconstrained generation with post-processing validation.
When SGLang Fits
SGLang wins for:
- Customer support chatbots with multi-turn conversations
- Coding assistants maintaining shared context
- Agentic workflows with repeated template prefixes
- RAG applications reusing document context across queries
SGLang now powers over 400,000 GPUs across xAI, AMD, NVIDIA, LinkedIn, Cursor, and major cloud providers.
The tradeoff: smaller community than vLLM. Edge cases may lack documentation or prior solutions.
LMDeploy: Maximum Speed for Quantized Models
LMDeploy takes a different approach. vLLM and SGLang are Python-first with native kernels for hot paths. LMDeploy's TurboMind engine is pure C++.
Python interpreter overhead disappears entirely. For latency-sensitive applications, this matters.
TurboMind Architecture
TurboMind is a C++ and CUDA inference backend implementing:
- Persistent batching for continuous request handling
- Blocked KV caching for efficient memory management
- Optimized CUDA kernels for attention computation
- Native weight-only quantization support
LMDeploy claims 1.8x higher request throughput than vLLM. Independent benchmarks confirm this, particularly for quantized models.
LMDeploy Performance Data
On A100 80GB GPUs serving Llama 3 70B with 4-bit quantization at 100 concurrent users, LMDeploy delivers 700 tokens per second with the lowest time to first token across all tested engines. Int4 inference runs 2.4x faster than FP16. 70B models fit on single GPUs with quantization.
When LMDeploy Fits
LMDeploy wins for:
- Quantized model serving (Int4, Int8, AWQ, GPTQ)
- Memory-constrained deployments requiring model compression
- Applications needing minimum TTFT
- Teams comfortable with C++ backend and NVIDIA-specific code
The tradeoff: TurboMind optimizes specifically for NVIDIA GPUs. A PyTorch backend exists for flexibility but runs slower. ROCm support exists but with less maturity.
Benchmark Methodology and Results
Our open-source benchmarks repository tests 13+ inference engines with reproducible methodology.
Test Configuration
- Hardware: A100 80GB GPU, H100 80GB GPU
- Models: Llama 2 7B Chat, Mistral 7B v0.1 Instruct
- Precisions: Float32, Float16, Int8, Int4
- Batch size: 1 (single request latency focus)
- Max tokens: 512
- Repetitions: 10 runs per configuration
Results by Precision
Float16 Performance (A100 80GB, Llama 2 7B):
| Engine | Throughput | GPU Memory |
|---|---|---|
| TensorRT-LLM | Highest | High |
| DeepSpeed-MII | Moderate | High |
| vLLM | Moderate | High |
| CTranslate2 | Stable | Moderate |
| LlamaCPP | Stable | Low |
Int4 Quantized Performance:
| Engine | Throughput | GPU Memory |
|---|---|---|
| ExLlamaV2 | Very High | Moderate |
| AutoAWQ | Good | Lowest |
| LlamaCPP | Stable | Low |
| vLLM | Good | Moderate |
Observations
TensorRT-LLM delivers highest raw throughput but demands significant setup effort and GPU memory. Engineering overhead rarely justifies the gains except at massive volume.
vLLM consumes similar memory to TensorRT-LLM without matching its throughput. The flexible architecture has measurable performance costs.
For Int4 quantized models, ExLlamaV2 provides the best speed-to-memory ratio. AutoAWQ uses the least memory; ExLlamaV2 runs faster with slightly more consumption.
Quality degradation from quantization is minimal across most engines. TensorRT-LLM maintains nearly identical outputs across all precisions. CTranslate2 occasionally produces truncated responses.
LlamaCPP offers the best balance of speed, memory usage, and output quality for resource-constrained environments.
Production Considerations
Benchmarks measure theoretical maximums. Production adds constraints.
Concurrency Scaling
vLLM's throughput scales with concurrent load, strong for high-traffic applications. At 64 concurrent users on H200 GPUs, vLLM delivers near-instantaneous TTFT with consistent ITL.
SGLang scales similarly with additional gains when prefix caching applies. At 100 concurrent users with shared context, RadixAttention adds 10-20% over vLLM.
LMDeploy maintains lowest TTFT across all concurrency levels, ideal for latency-sensitive applications.
Memory Pressure
When GPU memory fills, performance degrades non-linearly. Behavior differs by engine:
- vLLM: PagedAttention handles fragmentation gracefully
- SGLang: RadixAttention may evict cached prefixes under pressure
- LMDeploy: Aggressive quantization options help avoid pressure entirely
Model Compatibility
vLLM supports the broadest model range with minimal configuration. SGLang covers most popular architectures but verify newer or unusual models before committing. LMDeploy focuses on mainstream models with TurboMind optimization.
Decision Framework
Choose vLLM when:
- Deploying your first production LLM application
- Broad model compatibility is required
- Team prioritizes stability and documentation
- Single-turn interactions dominate
Choose SGLang when:
- Multi-turn conversations are primary
- Building agents with repeated prefixes
- Structured output generation (JSON, XML) is critical
- Testing shows significant cache hit rates for your prompts
Choose LMDeploy when:
- Quantized model serving is required for hardware constraints
- Raw decoding speed is top priority
- Lowest possible TTFT is needed
- Team is comfortable with NVIDIA-specific tooling
Choose TensorRT-LLM when:
- Operating at massive volume (millions of daily requests)
- Dedicated MLOps engineers manage the pipeline
- Maximum throughput justifies setup complexity
- NVIDIA lock-in is acceptable
Frequently Asked Questions
Which is faster: vLLM or SGLang?
SGLang is faster. On H100 GPUs running Llama 3.1 8B, SGLang delivers approximately 16,200 tokens per second compared to vLLM's 12,500. That's a 29% throughput advantage. The gap widens for multi-turn conversations where RadixAttention enables 75-95% cache hit rates, adding another 10-20% improvement.
Is LMDeploy better than vLLM?
LMDeploy outperforms vLLM on throughput and latency, particularly for quantized models. TurboMind delivers 1.8x higher request throughput and 2.4x faster Int4 inference compared to FP16 baselines. vLLM offers broader model compatibility and larger community. Choose LMDeploy for maximum speed. Choose vLLM for ecosystem maturity.
What is RadixAttention and why does it matter?
RadixAttention stores computed key-value tensors in a radix tree data structure, enabling efficient prefix matching across requests. When multiple prompts share common prefixes (system prompts, few-shot examples, conversation history), RadixAttention reuses cached computation instead of recalculating. This delivers up to 5x faster inference for prefix-heavy workloads like chatbots and agentic systems.
Can I use SGLang or LMDeploy with any LLM model?
Both engines support most popular open-source models including Llama, Mistral, Qwen, DeepSeek, Gemma, and GPT variants. SGLang supports over 50 model architectures and works with most Hugging Face models. LMDeploy focuses on mainstream architectures with TurboMind optimization. For unusual or very new models, verify compatibility before committing. vLLM still offers broadest model support.
How much GPU memory do I need for these inference engines?
Memory requirements depend on model size, precision, and concurrent request capacity. For Llama 2 7B at FP16 precision, expect approximately 14-16GB GPU memory for model weights alone, plus KV cache overhead. vLLM and SGLang consume similar amounts (high). LMDeploy with Int4 quantization reduces requirements by 4x, fitting 70B models on single A100 80GB GPUs. AutoAWQ offers lowest memory consumption among tested engines.
Conclusion
The inference engine landscape has matured significantly. vLLM established the foundation with PagedAttention and remains the safe choice for teams prioritizing stability and broad compatibility. SGLang leads for multi-turn and agentic workloads, with RadixAttention delivering measurable improvements that grow with volume. LMDeploy offers the fastest path for teams deploying quantized models on constrained hardware.
The 29% throughput gap between vLLM and the leading engines represents real cost differences. At volume, choosing the right engine can reduce GPU spend by tens of thousands monthly.
Recommendation: start with vLLM for initial deployment, then benchmark SGLang and LMDeploy against your actual workload patterns. The fastest engine is whichever matches your specific use case. Run your own tests with your models, prompts, and hardware. Published benchmarks provide guidance, but your traffic patterns determine which optimizations matter.
For teams building on fine-tuned models, the Prem AI provides infrastructure for training, evaluating, and deploying custom models with your choice of inference backend. Our open-source benchmarks offer reproducible testing across 13+ engines for validating performance claims.
Best Reads:
- Introducing Benchmarks v2 - Open-source benchmark methodology comparing 13+ inference engines
- Small Language Models for Edge Deployment - When smaller models make more sense than optimizing inference
- Transformer Inference Techniques - PagedAttention, FlashAttention, and optimization strategies
- Self-Hosting Fine-Tuned Models - Deployment with vLLM, Ollama, and NVIDIA NIM
- How to Save 90% on LLM API Costs - Cost optimization beyond inference engine selection