Nemotron 70B Self-Hosted

Nvidia’s Nemotron 70B (Llama-3.1-Nemotron-70B-Instruct) builds on Llama 3.1 70B with additional RLHF and Nvidia’s own tuning recipes. On our dedicated GPU hosting its hardware requirements match stock Llama 3.1 70B, but its behaviour differs in ways worth knowing.

Contents

• VRAM (same as Llama 70B)
• Deployment
• Quality differences

VRAM

Identical to Llama 3.1 70B:

Fits on a single 6000 Pro 96GB at FP8, or two 5090s at INT4 tensor-parallel.

Deployment

python -m vllm.entrypoints.openai.api_server \
  --model nvidia/Llama-3.1-Nemotron-70B-Instruct-HF \
  --dtype bfloat16 \
  --max-model-len 16384 \
  --gpu-memory-utilization 0.93

For quantised FP8 serving:

--model neuralmagic/Llama-3.1-Nemotron-70B-Instruct-HF-FP8 \
--quantization fp8

Quality

Nemotron tends to:

• Score higher on Arena-Hard and MT-Bench than stock Llama 3.1 70B
• Produce more verbose, structured responses
• Follow complex instructions more reliably
• Be slightly less creative on open-ended writing

For chat products where response quality matters more than personality, Nemotron is often the better pick. For creative writing or open-ended generation, stock Llama can feel less constrained.

Compare against Llama 3.3 70B (the newer base release) and Qwen 2.5 72B.