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Deploying Nemotron with TensorRT-LLM

🟢 NVIDIA Nemotron & NeMo Ecosystem⏱ 25 min⭐ 175 BASE XP⌨ HANDS-ON LAB

Compiling Nemotron into Optimized TensorRT-LLM Engines

To execute massive frontier models like NVIDIA Nemotron-4-340B with maximum throughput and minimum latency across GPU clusters, NVIDIA provides TensorRT-LLM. Rather than executing raw PyTorch graphs with Python interpreter overhead, TensorRT-LLM compiles the network into an optimized runtime engine utilizing custom CUDA kernels, fused Multi-Head/Grouped-Query Attention (FMHA/GQA), and hardware-accelerated FP8 quantization.

Key TensorRT-LLM Compilation Optimizations
  • Fused FP8 Attention: Leverages Hopper/Blackwell Tensor Cores using native E4M3 and E5M2 floating-point precisions to double computational throughput.
  • Paged KV Cache: Eliminates GPU memory fragmentation by allocating Key-Value attention tensors in discrete blocks (e.g., 64 tokens per block) similar to virtual memory paging.
  • In-Flight Batching (IFB): Continuously schedules newly arriving requests alongside ongoing decode iterations, achieving up to 4x throughput compared to static batching.
# Build TensorRT-LLM engine for Nemotron with 8-way Tensor Parallelism
python3 -m tensorrt_llm.commands.build \
  --checkpoint_dir /models/nemotron-4-340b-instruct-tp8 \
  --output_dir /engines/nemotron_fp8_tp8 \
  --gemm_plugin float8 \
  --gpt_attention_plugin float8 \
  --tokens_per_block 64 \
  --paged_kv_cache enable \
  --remove_input_padding enable \
  --max_batch_size 64 \
  --max_input_len 4096 \
  --max_output_len 2048
⌨ HANDS-ON LABBuild TensorRT-LLM FP8 Engine
⭐ +200 XP

Compile a 8-way Tensor Parallel Nemotron engine with fused FP8 attention kernels.

1Trigger the TensorRT-LLM build command targeting 8 GPUs with FP8 attention plugins.
lab-sandbox — simulated environment
INFINITY LAB SANDBOX v2.6 — simulated shell
Type the command for the current objective. Helpers: "hint", "solution", "clear".
$
OBJECTIVE 1 / 1 — type "hint" if stuck
SYNAPSE VERIFICATION
QUERY 1 // 2
What does the '--gpt_attention_plugin float8' flag accomplish in TensorRT-LLM?
Translates Python code to JavaScript
Enables 100% CPU inference fallback
Fuses attention kernels in FP8 precision directly on Hopper/Blackwell Tensor Cores
Disables all memory caching