2026 INFERENCE FRAMEWORK LANDSCAPE
The inference framework landscape has consolidated around four major options in 2026. vLLM holds 45-50% market share, SGLang 25-30%, TensorRT-LLM 15-20%, and NVIDIA Dynamo 5-10% with rapid growth. Framework selection directly determines GPU topology requirements, supported quantization formats, and achievable throughput, making it a first-order economic decision for GPU rental.
vLLM 2026: THE INCUMBENT
vLLM 0.8+ adds expert parallelism for MoE models, reducing H100 requirements for Mixtral and Mistral SMoE by 30-40%. FP8 support is mature with 95% of H100 theoretical throughput. Multi-node tensor parallelism now supports up to 32 GPUs. vLLM's broadest model support remains the primary advantage, with automatic prefix caching reducing KV cache by 20-40% for shared-prefix workloads.
| Feature | vLLM | SGLang | TRT-LLM | Dynamo |
|---|---|---|---|---|
| Expert parallelism | Yes | Yes | Yes | Yes |
| FP8 support | Mature | Mature | Mature | Native |
| Prefix caching | 20-40% | 30-50% | 15-25% | 25-35% |
| Max GPUs | 32 | 16 | 64 | 128 |
| Model support | Excellent | Good | Limited | NVIDIA only |
| Community | Large | Growing | Medium | Small |
SGLANG: RADICAL OPTIMIZATION
SGLang 0.6+ achieves 1.5-2.2x throughput over vLLM on structured generation tasks through constrained decoding optimizations. RadixAttention prefix caching delivers 30-50% KV cache reduction for shared-prefix workloads. However, model support remains narrower with 60-70% of vLLM's coverage. Best for high-throughput single-model deployments where maximum throughput is the priority.
TENSORRT-LLM: THE MAXIMUM OPTIMIZATION PATH
TensorRT-LLM delivers the highest single-GPU throughput at 95-98% of theoretical H100/B200 limits. The compiler optimization requires 4-8 hours per model graph compilation, and model changes require recompilation. FP8 and INT4-FP6 quantization support is comprehensive. Best for production deployments serving a static set of models with maximum hardware utilization.
NVIDIA DYNAMO: THE NEW ENTRANT
NVIDIA Dynamo 1.0 provides 1.8-2.5x throughput improvements through disaggregated prefill-decode architecture. Native integration with NVLink and InfiniBand reduces inter-node latency. Free with NVIDIA GPU rentals on Lambda, CoreWeave, and RunPod. Early benchmarks show 25-40% better throughput than vLLM on B200 for Llama 4 70B. Limited to NVIDIA GPUs and requires NVLink for multi-node.
SELECTION GUIDE
Choose vLLM for broad model support, ease of experimentation, and mixed workload serving. Choose SGLang for high-throughput single-model deployments with structured outputs. Choose TensorRT-LLM for static production workloads needing maximum hardware utilization. Choose Dynamo when deploying on B200/B300 clusters with NVLink, especially for latency-sensitive serving where disaggregated architecture delivers 40-60% lower TTFT.