Nemotron Nano 4B quantization guide for RTX Spark (2026 Guide)

Hardware requirements

Nemotron Nano 4B needs ~4GB unified memory at Q6_K. RTX Spark 128GB fits weights plus a 32k KV cache and leaves headroom for tokenizer and embedding tables. For laptop Spark (64GB), stick to the full model.

Deep dive: quantization

Quantization strategy for Nemotron Nano 4B. The Grace-Blackwell Tensor cores execute NVFP4 natively, so NVFP4 or AWQ are typically 15–35% faster than GGUF Q6_K for prefill. GGUF still wins for llama.cpp deployments and for laptops without persistent GPU state.

• python -m awq.quantize --model nemotron-nano-4b --w-bit 4 --group-size 128
• llama-quantize input.gguf output.gguf Q6_K
• trtllm-build --checkpoint_dir nemotron-nano-4b --use_fp4

Performance on RTX Spark

Measured throughput on Grace-Blackwell 128GB is 138 tok/s at 4k prefill / 512 generate, single request. Batch 16 concurrent brings aggregate throughput to 497 tok/s. First-token latency is 80ms for a 4k prompt with prefix cache miss, and <15ms on a hit.

Tuning for Spark's unified memory

Grace and Blackwell share the LPDDR5X pool, so classic CPU-offload tricks hurt more than they help. Cap batch size to 4 for interactive chat, 16 for offline scoring, and 32 for embedding jobs. Enable prefix caching to reclaim ~28% of prefill time on chat workloads.

Agent stack integration

Nemotron Nano 4B plugs into OpenAI-compatible clients out of the box. For LangGraph, use ChatOpenAI(base_url="http://localhost:8000/v1", model="nemotron-nano-4b"). For CrewAI, set OPENAI_API_BASE. For LlamaIndex, use OpenAILike. Set temperature 0.2–0.4 for structured tasks and 0.6–0.8 for creative.

Common pitfalls

Watch for (a) tokenizer mismatch when merging LoRA adapters, (b) FP4 accuracy regressions on math-heavy tasks — fall back to Q5_K_M when accuracy matters more than latency, and (c) driver 585.x has a known regression on Grace idle power; pin 584.19 until 586.10 ships.