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About Model Profiles

NVIDIA NIM microservices use model engines tuned for specific GPU models, GPU counts, precision, and other settings. These combinations are called model profiles. Each profile has a unique 64-character hexadecimal profile ID. The NIM selects a profile automatically based on available GPUs or can use a profile ID that you specify.

The model manifest is stored at /opt/nim/etc/default/model_manifest.yaml in the container.

NVIDIA Llama 3.1 NemoGuard 8B ContentSafety Model Profiles

The model requires 48 GB of GPU memory. NVIDIA developed and tested the microservice using these GPUs:

  • B200
  • H200
  • H100
  • H100 NVL
  • A100
  • A100 SXM4 40 GB
  • A10G
  • L40S
  • A6000

You can use one GPU with the required capacity or two GPUs that meet the capacity. For details about locally buildable and generic model profiles, see NIM for LLMs model profiles.

Locally Buildable Model Profiles

PrecisionTensor parallelismEngineProfile ID
BF162TensorRT-LLM (locally buildable)375dc0ff86133c2a423fbe9ef46d8fdf12d6403b3caa3b8e70d7851a89fc90dd
BF164TensorRT-LLM (locally buildable)54946b08b79ecf9e7f2d5c000234bf2cce19c8fee21b243c1a084b03897e8c95
BF161TensorRT-LLM (locally buildable)ac34857f8dcbd174ad524974248f2faf271bd2a0355643b2cf1490d0fe7787c2

Generic Model Profiles

PrecisionTensor parallelismEngineProfile ID
BF161vLLM4f904d571fe60ff24695b5ee2aa42da58cb460787a968f1e8a09f5a7e862728d
BF162vLLM7fa4a5a68c0338f16aef61de94977acfdacb7cabd848d38c49c48d2f639f04b3
BF164vLLMc84b2a068e56a551906563035ed77f88c88cbe1a63c6768fb2d4a9e0af1e67ba