LLM Inference Optimization with TensorRT-LLM

Your LLaMA-70B answers in 5 seconds, but you need 1.5? Load is growing, GPU utilization is 80%, and you pay for every hour of downtime. A typical scenario: LLaMA-2 13B on A100-80GB yields 50 tokens/sec at batch=8, latency p99 2.5 sec. After optimization with TensorRT-LLM, enabling FP8 quantization and in-flight batching, throughput increases to 180 tokens/sec, latency drops to 0.8 sec, GPU utilization rises from 65% to 95%. We do this systematically.

TensorRT-LLM is not just a library—it's a way to squeeze the last percentages of performance out of NVIDIA GPUs. Over several years, we have performed more than 50 LLM inference optimizations for companies of various sizes. We are NVIDIA-certified for TensorRT-LLM deployment. Average GPU utilization after optimization is 95%, latency p99 decreases 2–4x. On average, clients save 40% on GPU instances after optimization.

How TensorRT-LLM works

TensorRT-LLM compiles the model into an optimized TensorRT engine. Graph compilation: the model graph is compiled considering the specific GPU (architecture, VRAM, tensor cores). Kernel fusion: multiple operations are combined into a single CUDA kernel (LayerNorm + Linear, Flash Attention). Quantization: FP8, INT8, INT4 with precise calibration methods. In-flight batching: the most advanced continuous batching implementation. According to the NVIDIA TensorRT-LLM technical report, FP8 quantization reduces quality by less than 0.5%.

Why TensorRT-LLM is faster than vLLM

TensorRT-LLM does what vLLM cannot: it compiles the model into machine code specific to your GPU. On H100 with FP8 quantization, throughput grows 2–3x without noticeable quality degradation (<0.5% on benchmarks). Hardware tensor cores run at full capacity—GPU utilization reaches 95%. If vLLM is a universal server, TensorRT-LLM is a racing car for NVIDIA.

How TensorRT-LLM differs from vLLM

If you need to quickly launch a prototype or work with non-NVIDIA GPUs—choose vLLM. If the goal is to squeeze maximum from each GPU and reduce costs—TensorRT-LLM gives 2–4x gain for the same money.

What is included in the optimization work?

We don't just run scripts. The delivery includes:

• Audit: measurement of current latency p99, throughput, GPU utilization, tokens/sec.
• Configuration selection: choice of TensorRT-LLM version, quantization type (FP8/INT8/INT4), batch and context window parameters.
• Compilation: building the engine with kernel fusion, in-flight batching, PagedAttention.
• Integration with Triton: setting up an ensemble of tokenization, inference, post-processing.
• Load testing: stability check, latency p99, throughput under peak load (up to 10k requests).
• Documentation and training: delivery of configs, scripts, monitoring recommendations, one-hour team training.

We guarantee at least 2x acceleration or money back for the work.

How FP8 quantization works on H100

H100 has hardware support for FP8—the greatest performance boost:

from tensorrt_llm.quantization import QuantAlgo

build_config_fp8 = BuildConfig(
    max_batch_size=128,
    max_input_len=4096,
    max_output_len=1024,
    quant_config=QuantConfig(
        quant_algo=QuantAlgo.FP8,
        kv_cache_quant_algo=QuantAlgo.FP8,
    ),
    plugin_config={
        "use_fp8_context_fmha": True,
        "gemm_plugin": "float16",
    }
)

FP8 on H100: roughly 2x throughput gain compared to BF16, quality degradation < 0.5% on standard benchmarks.

Table: Typical optimization results

Integration with Triton Inference Server

TensorRT-LLM natively integrates with NVIDIA Triton:

model_repository/
├── ensemble/
│   └── config.pbtxt
├── preprocessing/
│   ├── config.pbtxt
│   └── 1/model.py
├── tensorrt_llm/
│   ├── config.pbtxt
│   └── 1/
│       ├── model.engine
│       └── config.json
└── postprocessing/
    ├── config.pbtxt
    └── 1/model.py

name: "tensorrt_llm"
backend: "tensorrtllm"
max_batch_size: 128
parameters {
  key: "max_beam_width"
  value: { string_value: "1" }
}
parameters {
  key: "executor_worker_path"
  value: { string_value: "/opt/tritonserver/backends/tensorrtllm/trtllmExecutorWorker" }
}
parameters {
  key: "decoding_mode"
  value: { string_value: "top_p_top_k" }
}

Multi-GPU with Tensor Parallelism

build_config_tp4 = BuildConfig(
    max_batch_size=64,
    max_input_len=8192,
    max_output_len=2048,
    auto_parallel_config=AutoParallelConfig(
        world_size=4,
        gpus_per_node=4,
        shards_along_head=4,
    )
)

Timelines and implementation process

1. Day 1–3: installation of TRT-LLM, compilation of first model, measurement of baseline metrics.
2. Week 1–2: selection of quantization and fusion parameters, integration with Triton.
3. Week 3–4: load testing, tuning, deployment to production.
4. Month 2: optimization for specific scenarios (latency vs throughput), multi-model deployment.

Ready to accelerate your LLM? Order an inference audit. Get a consultation on TensorRT-LLM implementation from certified NVIDIA engineers.