TensorRT-LLM vs SGLang: Blackwell FP4 Throughput, KV Cache Paging, and Speculative Decoding Showdown
The release of NVIDIA's Blackwell GPU architecture, featuring native 4-bit floating-point (FP4) Tensor Cores and fifth-generation NVLink interconnects, has redefined the performance frontier for enterprise large language model inference. To harness this hardware acceleration, engineering teams must choose between two prominent serving paradigms: NVIDIA's compiler-optimized TensorRT-LLM framework…
The NVIDIA Blackwell GPU architecture, which includes native 4-bit floating-point (FP4) Tensor Cores and fifth-generation NVLink interconnects, has revolutionized the performance landscape for enterprise large language model inference. To leverage this hardware acceleration, engineering teams must select between two leading serving strategies: NVIDIA's compiler-optimized TensorRT-LLM framework and SGLang's dynamic RadixAttention runtime.
TensorRT-LLM focuses on deep hardware kernel fusion to achieve maximum tokens-per-second density, while SGLang addresses the multi-turn agent bottleneck using a trie-based KV cache retention system. During a hardware battle, the two approaches are pitted against each other using NVIDIA's B200 SXM (192GB HBM3e) and 8x H100 SXM5 systems, serving Meta Llama 3.1 70B across FP4, FP8, and speculative decoding configurations.
Production Value indicates that this evaluation can help platform teams lower enterprise cluster compute costs by 50%, from $24,000 to $12,800 monthly.
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