The Financial SLA: Why Sub-Second Time-to-First-Token Matters
In automated trading intelligence and conversational document analysis, latency isn't a vanity metric—it directly dictates whether an insight is actionable before market conditions shift. When serving Llama-3-70B to concurrent analytical sessions, our target was strict: Time-to-First-Token (TTFT) under 180ms and sustained inter-token latency below 25ms under 50+ concurrent requests.
The Contenders: vLLM vs NVIDIA TensorRT-LLM
We benchmarked two industry-leading inference serving architectures on a cluster of 8x NVIDIA H100 (80GB SXM5) GPUs interconnected via NVLink:
- vLLM (v0.6.2): Utilizes PagedAttention to manage KV-cache memory allocation dynamically like OS virtual memory paging. Highly flexible with standard PyTorch integration.
- TensorRT-LLM (v0.12.0): NVIDIA's optimized C++ inference framework featuring custom fused kernels, in-flight batching, FP8 GEMM kernels, and fine-tuned KV cache quantization.
The Empirical Results
| Framework | Precision | TTFT (p95) | Tokens/sec (Throughput) | Max Concurrent Requests |
|---|---|---|---|---|
| vLLM | FP16 | 240ms | 1,420 t/s | 64 |
| vLLM (AWQ) | INT4 | 165ms | 2,180 t/s | 96 |
| TensorRT-LLM | FP8 | 112ms | 3,850 t/s | 128+ |
Our Recommendation
If your team prioritizes rapid experimentation, custom LoRA adapter switching, and standard Python deployment, vLLM is unmatched in developer velocity. However, for fixed production topologies where maximizing GPU saturation and achieving lowest possible TTFT is critical, TensorRT-LLM with FP8 quantization delivers nearly 2.7x higher throughput per dollar on modern Hopper architecture.
Peer-Reviewed Engineering Article✓ Fact Checked
Authored by senior engineering practitioners. Verified for production reproducibility and accuracy.
Elena Rostova
Senior ML Infrastructure EngineerSpecializing in large-scale model quantization, GPU kernel optimization, and high-throughput inference engines.
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