Nemotron Mini 8B long-context tuning on RTX Spark (2026 Guide)

Hardware requirements

Nemotron Mini 8B needs ~8GB unified memory at Q5_K_M. 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: longContext

Nemotron Mini 8B handles long context reasonably to 32k tokens on Spark 128GB with Q5_K_M. Beyond 32k, use YaRN scaling or activation offload; expect 30–45% throughput loss but coherent recall up to 128k.

• vllm serve nemotron-mini-8b --max-model-len 131072 --rope-scaling '{"type":"yarn","factor":4}'
• # Enable KV cache offload for >64k
• --kv-cache-dtype fp8

Performance on RTX Spark

Measured throughput on Grace-Blackwell 128GB is 88 tok/s at 4k prefill / 512 generate, single request. Batch 16 concurrent brings aggregate throughput to 317 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 Mini 8B plugs into OpenAI-compatible clients out of the box. For LangGraph, use ChatOpenAI(base_url="http://localhost:8000/v1", model="nemotron-mini-8b"). 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.