Benchmarking Production LLM Inference: A Technical Comparison of Leading Engines

In the high-stakes world of deploying large language models (LLMs) at scale, a tech firm faces a dilemma: their current inference setup struggles to handle surging user queries, leading to delayed responses and escalating GPU costs. The choice of inference engine becomes critical, determining not just speed but also efficiency in resource utilization. This analysis examines four prominent open-source and proprietary stacks—vLLM, NVIDIA TensorRT-LLM, Hugging Face Text Generation Inference (TGI) v3, and LMDeploy—based on their architectural strengths, performance metrics, and suitability for production workloads. Drawing from recent benchmarks, it highlights key trade-offs in throughput, latency, and memory management, offering insights into evolving trends in AI serving infrastructure.

Key Architectural Innovations in LLM Inference Stacks

Modern LLM inference has shifted from simple generation loops to sophisticated systems engineering, where optimizations in key-value (KV) caching, batching, and quantization directly influence operational metrics like tokens per second and cost per million tokens. These engines address common bottlenecks in production environments, such as handling concurrent requests on GPU fleets. Performance varies by hardware—primarily NVIDIA GPUs—and workload types, including short chats versus long-context tasks like retrieval-augmented generation (RAG). While benchmarks show significant gains, results are model-specific and hardware-dependent; for instance, FP8 precision on newer GPUs like H100 can amplify advantages, but real-world gains may differ by 10-20% based on unverified deployment variables.

Performance Benchmarks and Throughput Comparisons

Each engine employs distinct strategies to maximize throughput and minimize latency tails, with implications for scaling AI services amid growing demand. vLLM sets an open baseline with its PagedAttention mechanism, treating the KV cache like virtual memory to reduce fragmentation. This allows packing more sequences into VRAM, yielding 2-4x higher throughput than legacy systems like FasterTransformer for similar latencies, particularly on longer sequences.

• Continuous batching in vLLM merges incoming requests dynamically, enabling near-linear scaling with concurrency until memory or compute limits are hit.
• On typical chat workloads, P50 latency stays low at moderate loads, but P99 can rise under high queue pressure or prefill-intensive queries.
• KV waste is near zero, supporting prefix sharing across requests, though multi-tenancy requires external routing to multiple instances.

NVIDIA’s TensorRT-LLM pushes hardware limits on its GPUs, integrating custom kernels, inflight batching, and quantization down to FP4/INT4. Public evaluations on H100 versus A100 hardware reveal stark differences:

• FP8 mode achieves over 10,000 output tokens/second at peak for 64 concurrent requests, with ~100 ms time-to-first-token (TTFT).
• Compared to A100, H100 delivers up to 4.6x higher max throughput and 4.4x faster TTFT on equivalent models.
• For low-latency scenarios, batch size 1 configurations drop TTFT below 10 ms, though at reduced overall throughput.

This engine optimizes both prefill (via tensor parallelism) and decode phases (via CUDA graphs and kernel fusion), making it ideal for high-volume deployments. However, its tight NVIDIA coupling limits portability, a trend underscoring vendor-specific optimizations in the AI hardware market. Hugging Face TGI v3 emphasizes long-prompt handling through chunking and prefix caching, acting as a versatile gateway with pluggable backends. Benchmarks for prompts exceeding 200,000 tokens show:

• A conversation reply processing in 27.5 seconds on vLLM reduces to ~2 seconds in TGI v3, a reported 13x speedup.
• It processes 3x more tokens within the same GPU memory by minimizing repeated prefill via prefix caches, with lookup overhead in microseconds.
• Chunking splits long inputs for efficient KV management, while continuous batching integrates new requests seamlessly.
• For short prompts, metrics align closely with vLLM; long-context gains improve both P50 and P99 latencies by an order of magnitude.

Multi-backend support enables routing to TensorRT-LLM for priority traffic or lighter runtimes for others, facilitating hybrid multi-tenant setups. This reflects a broader industry shift toward modular architectures for diverse workloads. LMDeploy, from the InternLM ecosystem, prioritizes compression via TurboMind, focusing on blocked KV caches and aggressive quantization. It claims up to 1.8x higher request throughput than vLLM, driven by persistent batching, dynamic splitting, and optimized CUDA kernels.

• Blocked KV and INT8/INT4 quantization reduce memory bandwidth, enabling larger models on mid-range GPUs.
• Weight quantization like 4-bit AWQ maintains tokens/second while supporting tensor parallelism.
• A built-in proxy handles multi-model, multi-GPU routing based on request metadata.

These features suit open models like InternLM or Qwen, where resource constraints are common, highlighting quantization’s role in democratizing access to high-performance inference.

Implications for Production Deployments and Market Trends

The choice of engine hinges on workload profiles, with no universal winner—TensorRT-LLM excels in raw NVIDIA performance for latency-sensitive chats, TGI v3 for RAG-heavy analytics, vLLM for straightforward open-source integration, and LMDeploy for quantized open-model serving. Mixing stacks, such as TensorRT-LLM for core traffic and vLLM for experiments, is increasingly common, as teams measure cost per million tokens against actual token distributions.

"Production LLM serving is now a systems problem… the choice of inference stack drives your tokens per second, tail latency, and ultimately cost per million tokens on a given GPU fleet."

Market trends point to rising adoption of paged/blocked KV and continuous batching across engines, potentially lowering barriers for edge AI deployments. Quantization and speculative decoding further compress costs, but interoperability challenges persist in multi-vendor environments. Uncertainties remain in cross-hardware benchmarks; for example, non-NVIDIA GPUs may see 20-30% performance variance not fully captured here. As AI inference demands intensify, evaluating these engines against your specific traffic patterns could optimize efficiency—would you prioritize throughput for user-facing apps or memory savings for long-context analytics in your next deployment?