Choice of serving stack materially shapes production LLM economics. The 2026 landscape includes vLLM (dominant general open-weight serving), Hugging Face TGI (Text Generation Inference), SGLang (strong on structured generation and MoE), NVIDIA TensorRT-LLM (peak NVIDIA hardware throughput), LMDeploy (Alibaba\u2019s open serving), MLC-LLM (cross-platform), Ollama (consumer), and llama.cpp (CPU and edge). This page consolidates the comparison.
Key Findings
- vLLM is the dominant open-weight production serving stack in 2026 with the broadest model support, mature continuous batching, and PagedAttention KV cache management.
- SGLang emerged in 2025-2026 as the leading stack for structured generation, MoE serving, and complex reasoning workloads with explicit support for control flow over LLM calls.
- NVIDIA TensorRT-LLM achieves peak throughput on H100, H200, and B200 hardware but requires more complex deployment and offers narrower model support.
- Hugging Face TGI remains widely deployed in production, particularly in Hugging Face Inference Endpoints and within EU sovereign cloud deployments.
- LMDeploy from Alibaba is the dominant serving stack for Chinese Qwen deployments with strong MoE support and competitive throughput on Chinese AI chips (Huawei Ascend).
Serving Stack Comparison (May 2026)
| Stack | Lead Maintainer | License | Strengths |
|---|---|---|---|
| vLLM | UC Berkeley Sky Computing + community | Apache 2.0 | Broad model support, mature KV cache |
| SGLang | UC Berkeley + LMSys | Apache 2.0 | Structured generation, MoE, prefix caching |
| TensorRT-LLM | NVIDIA | Apache 2.0 | Peak NVIDIA hardware throughput |
| TGI (Text Generation Inference) | Hugging Face | Apache 2.0 (with notice) | HF ecosystem integration |
| LMDeploy | Shanghai AI Lab / Alibaba | Apache 2.0 | Chinese model and chip support |
| MLC-LLM | CMU + community | Apache 2.0 | Cross-platform (mobile, edge, web) |
| llama.cpp | Georgi Gerganov + community | MIT | CPU, Apple Silicon, broad hardware |
| Ollama | Ollama team | MIT (CLI), various server | Consumer ease-of-use, local-first |
| NIM (NVIDIA Inference Microservices) | NVIDIA | Closed (commercial) | NVIDIA enterprise platform |
Throughput Comparison (Llama 3.1 70B Single H100 FP8)
| Stack | Throughput (tokens/sec, batch=1) | Throughput (tokens/sec, batch=256) |
|---|---|---|
| TensorRT-LLM | ~62 | ~5,400 |
| vLLM 0.7 | ~58 | ~5,100 |
| SGLang | ~56 | ~4,900 |
| TGI | ~52 | ~4,300 |
| LMDeploy | ~55 | ~4,700 |
Model Support
| Stack | MoE Support | Multimodal | Long-Context | Quantization Formats |
|---|---|---|---|---|
| vLLM | Yes (extensive) | Yes (Qwen2.5-VL, Llama 4) | Yes | FP8, AWQ, GPTQ, FP16, INT8 |
| SGLang | Yes (best) | Yes | Yes | FP8, AWQ, GPTQ |
| TensorRT-LLM | Yes | Limited | Yes | FP8, FP4, INT8, INT4 |
| TGI | Yes | Yes | Yes | FP8, AWQ, EETQ |
| LMDeploy | Yes (Chinese-MoE optimised) | Yes (InternVL) | Yes | AWQ, INT4 |
| MLC-LLM | Limited | Limited | Limited | INT4, INT8, FP16 |
| llama.cpp | Yes (limited) | Limited | Yes | GGUF (all variants) |
| Ollama | Yes (via llama.cpp) | Yes (via llama.cpp) | Yes | GGUF |
Deployment Recommendations
| Scenario | Recommended Stack |
|---|---|
| Production server, broad model support | vLLM 0.7+ |
| Production server, peak throughput on NVIDIA | TensorRT-LLM |
| Production server, MoE-heavy workload | SGLang or vLLM |
| Structured generation / control flow | SGLang |
| Hugging Face Inference Endpoints | TGI |
| Chinese AI chip deployment (Huawei Ascend) | LMDeploy |
| Cross-platform (browser, mobile) | MLC-LLM |
| CPU / Apple Silicon / heterogeneous | llama.cpp or Ollama |
| Enterprise NVIDIA-supported deployment | NIM (NVIDIA Inference Microservices) |
| Consumer / developer local | Ollama or LM Studio |
Strategic Context
Three patterns shape the 2026 serving stack landscape. First, vLLM is the production-default open serving stack and continues to gain features (MoE, multimodal, long-context) faster than competitors. Second, SGLang carved out the structured-generation and MoE niche by tightly integrating control flow over LLM calls. Third, hardware specialisation: TensorRT-LLM on NVIDIA, LMDeploy on Ascend, MLX-LM on Apple Silicon mean that the optimal stack varies by hardware target.
Brand Visibility Implications
Serving stack selection is a high-traffic technical procurement decision. AI assistant queries about "vLLM vs SGLang", "best LLM serving 2026", "TensorRT-LLM deployment", and similar terms drive direct production decisions. Brands selling LLM serving platforms, AI infrastructure, and inference cloud services face strong AI-mediated discovery surface for this category.
Methodology
Throughput data compiled from primary maintainer disclosures, community benchmarking, and the LLM Inference Benchmarking community resources through 23 May 2026. Stack feature comparison from primary documentation. Updated quarterly.
How Presenc AI Helps
Presenc AI monitors brand visibility on serving stack queries across ChatGPT, Claude, Gemini, and Perplexity. For LLM serving platforms, AI infrastructure brands, and inference cloud services, the platform identifies the prompts driving procurement-research traffic and the gaps where new content unlocks share of voice.