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Deployment Guide for Nemotron v3 Super on TensorRT LLM - Blackwell & Hopper Hardware

Introduction

This deployment guide provides step-by-step instructions for running the NVIDIA Nemotron v3 Super 120B-A12B model using TensorRT LLM. Nemotron v3 Super is a hybrid architecture model combining Mixture-of-Experts (MoE) with SSM (Mamba) and attention layers, delivering 120B total parameters with only 12B active parameters per token for efficient inference. This guide covers model access, environment setup, server configuration, and inference validation.

Prerequisites

  • GPU: NVIDIA Blackwell or Hopper Architecture
  • OS: Linux
  • Drivers: CUDA Driver 575 or Later
  • Docker with NVIDIA Container Toolkit installed
  • Python3 and python3-pip (Optional, for accuracy evaluation only)

Models

All models are available under the nvidia/nvidia-nemotron-v3 collection on Hugging Face.

GPU Requirements

Nemotron v3 Super 120B-A12B has 120B total parameters. The minimum GPU memory required depends on the precision:

Checkpoint Minimum GPUs (H100/H200 80GB) Minimum GPUs (B200/GB200 192GB)
BF16 4x H100/H200 2x B200/GB200
NVFP4 2x H100/H200 1x B200/GB200

Deployment Steps

Run Docker Container

Run the docker container using the TensorRT LLM NVIDIA NGC image.

docker run --rm -it \
--ipc=host \
--gpus all \
-p 8000:8000 \
-v ~/.cache:/root/.cache:rw \
--name tensorrt_llm \
nvcr.io/nvidia/tensorrt-llm/release:x.y.z \
/bin/bash

Note:

  • The command mounts your user .cache directory to save the downloaded model checkpoints which are saved to ~/.cache/huggingface/hub/ by default. This prevents having to redownload the weights each time you rerun the container. If the ~/.cache directory doesn't exist please create it using $ mkdir ~/.cache.
  • You can mount additional directories and paths using the -v <host_path>:<container_path> flag if needed, such as mounting the downloaded weight paths.
  • The command also maps port 8000 from the container to your host so you can access the LLM API endpoint from your host.
  • See the https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tensorrt-llm/containers/release/tags for all the available containers. The containers published in the main branch weekly have rcN suffix, while the monthly release with QA tests has no rcN suffix. Use the rc release to get the latest model and feature support.

If you want to use latest main branch, you can choose to build from source to install TensorRT LLM, the steps refer to https://nvidia.github.io/TensorRT-LLM/latest/installation/build-from-source-linux.html

Recommended Performance Settings

We maintain YAML configuration files with recommended performance settings in the examples/configs directory. These config files are present in the TensorRT LLM container at the path /app/tensorrt_llm/examples/configs. You can use these out-of-the-box, or adjust them to your specific use case.

TRTLLM_DIR=/app/tensorrt_llm # change as needed to match your environment
EXTRA_LLM_API_FILE=${TRTLLM_DIR}/examples/configs/curated/nemotron-3-super-throughput.yaml

Note: if you don't have access to the source code locally, you can manually create the YAML config file using the code in the dropdown below.

:class: dropdown

```{literalinclude} ../../../examples/configs/curated/nemotron-3-super-throughput.yaml
---
language: shell
prepend: |
  EXTRA_LLM_API_FILE=/tmp/config.yml

  cat << EOF > ${EXTRA_LLM_API_FILE}
append: EOF
---
```

Launch the TensorRT LLM Server

Below are example commands to launch the TensorRT LLM server with the Nemotron v3 Super model from within the container.

NVFP4 model (recommended, lowest memory footprint):

trtllm-serve nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-NVFP4 --host 0.0.0.0 --port 8000 --reasoning_parser nano-v3 --tool_parser qwen3_coder --config ${EXTRA_LLM_API_FILE}

After the server is set up, the client can now send prompt requests to the server and receive results.

LLM API Options (YAML Configuration)

These options provide control over TensorRT LLM's behavior and are set within the YAML file passed to the trtllm-serve command via the --config argument.

tensor_parallel_size

  • Description: Sets the tensor-parallel size. This should typically match the number of GPUs you intend to use for a single model instance. For BF16, use 4 or more GPUs on H100/H200. For NVFP4, 2 GPUs on H100/H200 may suffice.

moe_expert_parallel_size

  • Description: Sets the expert-parallel size for Mixture-of-Experts (MoE) models. Like tensor_parallel_size, this should generally match the number of GPUs you're using.

kv_cache_free_gpu_memory_fraction

  • Description: A value between 0.0 and 1.0 that specifies the fraction of free GPU memory to reserve for the KV cache after the model is loaded. Since memory usage can fluctuate, this buffer helps prevent out-of-memory (OOM) errors.
  • Recommendation: If you experience OOM errors, try reducing this value to 0.7 or lower.

max_batch_size

  • Description: The maximum number of user requests that can be grouped into a single batch for processing. The actual max batch size that can be achieved depends on total sequence length (input + output).

max_num_tokens

  • Description: The maximum total number of tokens (across all requests) allowed inside a single scheduled batch.

max_seq_len

  • Description: The maximum possible sequence length for a single request, including both input and generated output tokens. We won't specifically set it. It will be inferred from model config.

trust_remote_code

  • Description: Allows TensorRT LLM to download models and tokenizers from Hugging Face. This flag is passed directly to the Hugging Face API.

cuda_graph_config

  • Description: A section for configuring CUDA graphs to optimize performance.

  • Options:

    • enable_padding: If true, input batches are padded to the nearest cuda_graph_batch_size. This can significantly improve performance.

      Default: false

    • batch_sizes: List of batch sizes for which CUDA graphs will be pre-captured.

      Recommendation: Set this to cover the range of batch sizes you expect in production.

See the TorchLlmArgs class for the full list of options which can be used in the YAML configuration file.

Testing API Endpoint

Basic Test

Start a new terminal on the host to test the TensorRT LLM server you just launched.

You can query the health/readiness of the server using:

curl -s -o /dev/null -w "Status: %{http_code}\n" "http://localhost:8000/health"

When the Status: 200 code is returned, the server is ready for queries. Note that the very first query may take longer due to initialization and compilation.

After the TensorRT LLM server is set up and shows Application startup complete, you can send requests to the server.

curl http://localhost:8000/v1/chat/completions -H "Content-Type: application/json"  -d '{
    "model": "nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-NVFP4",
    "messages": [
        {
            "role": "user",
            "content": "What is the capital of France?"
        }
    ],
    "max_tokens": 512,
    "temperature": 0.7,
    "top_p": 0.95
}' -w "\n"

Here is an example response:

{
  "id": "chatcmpl-abc123def456",
  "object": "chat.completion",
  "created": 1759022940,
  "model": "nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-NVFP4",
  "choices": [
    {
      "index": 0,
      "message": {
        "role": "assistant",
        "content": "The capital of France is Paris. Paris is not only the capital but also the largest city in France, known for its rich history, culture, art, and iconic landmarks such as the Eiffel Tower, the Louvre Museum, and Notre-Dame Cathedral."
      },
      "logprobs": null,
      "finish_reason": "stop"
    }
  ],
  "usage": {
    "prompt_tokens": 15,
    "completion_tokens": 58,
    "total_tokens": 73
  }
}

Troubleshooting Tips

  • If you encounter CUDA out-of-memory errors, try reducing max_batch_size, max_num_tokens, or kv_cache_free_gpu_memory_fraction.
  • Ensure your model checkpoints are compatible with the expected format.
  • For performance issues, check GPU utilization with nvidia-smi while the server is running.
  • If the container fails to start, verify that the NVIDIA Container Toolkit is properly installed.
  • For connection issues, make sure the server port (8000 in this guide) is not being used by another application.
  • Nemotron v3 Super is a hybrid SSM/attention model with MoE — ensure you have sufficient GPU memory for the full 120B parameter weights even though only 12B parameters are active per token.

Benchmarking Performance

To benchmark the performance of your TensorRT LLM server you can leverage the built-in benchmark_serving.py script. To do this, first create a wrapper bench.sh script.

cat <<'EOF' > bench.sh
#!/usr/bin/env bash
set -euo pipefail

# Adjust the model name based on which Nemotron v3 Super variant you're benchmarking
MODEL_NAME="nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-NVFP4"

concurrency_list="1 2 4 8 16 32 64 128"
multi_round=5
isl=1024
osl=1024
result_dir=/tmp/nemotron_super_output

for concurrency in ${concurrency_list}; do
    num_prompts=$((concurrency * multi_round))
    python -m tensorrt_llm.serve.scripts.benchmark_serving \
        --model ${MODEL_NAME} \
        --backend openai \
        --dataset-name "random" \
        --random-input-len ${isl} \
        --random-output-len ${osl} \
        --random-prefix-len 0 \
        --random-ids \
        --num-prompts ${num_prompts} \
        --max-concurrency ${concurrency} \
        --ignore-eos \
        --tokenize-on-client \
        --percentile-metrics "ttft,tpot,itl,e2el"
done
EOF
chmod +x bench.sh

To achieve max throughput, with attention DP on, one needs to sweep up to concurrency = max_batch_size * num_gpus.

If you want to save the results to a file add the following options.

--save-result \
--result-dir "${result_dir}" \
--result-filename "concurrency_${concurrency}.json"

For more benchmarking options see benchmark_serving.py

Run bench.sh to begin a serving benchmark. This will take a long time if you run all the concurrencies mentioned in the above bench.sh script.

./bench.sh