Running Nemotron 3 Locally: Hardware Requirements, Cost & Performance Benchmarks

Running Nemotron 3 locally gives you privacy, lower long-run cost, and full control of latency. This guide focuses on planning the hardware, choosing inference settings, and benchmarking in a way that reflects real workloads.

Start with the right model tier

• Nano is the best place to begin for single-GPU testing or small clusters.
• Super targets higher-end infra where throughput and quality matter more.

Hardware planning without guesswork

1) Estimate memory

Local inference memory is driven by:

• Model weights (affected by precision / quantization)
• KV cache (grows with context length)
• Batch size and concurrency

Rule of thumb: lower precision and smaller batch sizes reduce VRAM pressure. If you need very long context, the KV cache becomes the dominant cost.

2) Choose a quantization strategy

• FP16 / BF16: highest quality, highest VRAM.
• INT8 / INT4: faster and smaller, with some quality trade-off.

Start with a quantized Nano run, then scale up once you confirm your workload.

3) Single GPU vs multi GPU

• Single GPU: fastest to get running, great for early evaluation.
• Multi GPU: required for larger models at higher precision or higher throughput.

Benchmarking that matters

Do not rely on generic benchmarks alone. Use a small, repeatable set of tasks that mirror your actual product needs.

Suggested benchmark set

1. Long document summarization (multi-section prompt)
2. Tool calling workflow (3 to 5 steps)
3. Codebase analysis (large repo snapshot)
4. Long-context Q&A (1M-token window)

Metrics to track

• Tokens per second
• Time to first token
• Total latency
• Failure rate
• Cost per run (local vs API)

Cost modeling (simple version)

1. Estimate your daily token volume.
2. Calculate local GPU cost per day (depreciation + power).
3. Compare to API pricing at the same volume.

You do not need perfect numbers to make the first decision. The goal is to see whether local is clearly better for your workload.

A quick deployment checklist

• Pick Nano or Super based on model size and target latency.
• Choose precision and quantization based on VRAM limits.
• Benchmark with your real workloads, not only public tests.
• Track total cost per run and failure rates.

Once you have stable results, you can decide whether to stay local or move to hosted inference for scale.