Add high-throughput batch processing cookbook for Nemotron 3 Super

[mvanhorn]

Fixes #107

Summary

Adds usage-cookbook/Nemotron-3-Super/batch_throughput_cookbook.ipynb demonstrating high-throughput batch inference with Nemotron 3 Super via vLLM.

What's covered:

• Throughput-optimized server configuration (CUTLASS backend, EP, batch sizing) vs latency-optimized defaults
• Offline batch inference with vLLM's LLM class for zero-server batch processing
• Async concurrent requests with httpx and bounded concurrency
• Practical use case: bulk document classification with structured JSON output
• Concurrency scaling analysis showing throughput vs concurrency level
• JSONL file-to-file processing pattern for production pipelines
• Throughput benchmarking guidance with vllm bench serve
• Scaling estimates from measured throughput to larger workloads

Also updates the Nemotron-3-Super cookbook README with the new entry.

Research & Context

Community Signals

• Reddit r/LocalLLaMA: A user classified 3.5M US patents on a single RTX 5090 with Nemotron 9B (401 upvotes) - the most viral Nemotron community content
• Greptile tested Nemotron 3 Super for bulk code review and reported it "punches far above its weight class" at 12.5 seconds per review
• The Advanced Deployment Guide mentions VLLM_FLASHINFER_MOE_BACKEND=throughput for "offline batch jobs" but never demonstrates it

Gap

• Every existing cookbook demonstrates single-request or streaming use cases
• No cookbook shows how to run high-throughput batch inference despite throughput being the headline stat (5x over previous Nemotron Super, 7.5x over Qwen3.5-122B)
• vLLM's own docs cover offline batch generically but not Nemotron-specific optimizations (EP, latent MoE, NVFP4)

Competitive Context

• Qwen 3.5 has detailed batch processing examples in its docs
• No competing project combines Nemotron-specific configs with practical batch workloads

This contribution was developed with AI assistance (Claude Code).