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Deploying Nemotron with TensorRT-LLM

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

Compiling Nemotron into TensorRT-LLM Engines

To run large dense models like Nemotron across multiple GPUs with minimal latency, NVIDIA provides TensorRT-LLM. It compiles the PyTorch graph into an optimized execution engine with fused GEMM kernels and in-flight 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 // 1
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