The Math Says Cloud Wins. The Math is Wrong.

TL;DR: For pure cost optimization, OpenRouter beats local inference in most scenarios. An RTX 6000 Blackwell Pro Q-Max running vLLM at full batch utilization (~505 tok/s) saves $0.30/M tokens vs cloud—but at 8 hours/day, that’s 5+ years to break even on ~$8,500 hardware. Local wins when you need: (1) sub-100ms latency (local delivers 88ms vs 760ms cloud for Gemma), (2) data privacy (your prompts never leave your network), (3) batch processing at scale (10M+ tokens/month), or (4) custom/fine-tuned models. For occasional use, stick with OpenRouter—Gemma 3 27B at $0.06/M tokens is nearly free.

In Part 1, I established the throughput and latency numbers. Now let’s answer the real question: when does buying an ~$8,500 GPU actually pay off?

The Break-Even Math

With vLLM running at optimal concurrency (4 requests), local inference costs $0.21/M tokens vs OpenRouter’s $0.51/M for Qwen 3 VL 30B. That’s a savings of $0.30 per million tokens.

To break even on the RTX 6000 Blackwell Pro Q-Max:

$8,500 ÷ $0.30 per M tokens = 28.3 billion tokens

At ~505 tokens/second with constant utilization:

• Per hour: 1.82M tokens
• Per 8-hour day: 14.5M tokens
• Per month (8hr/day): 436M tokens

Time to break even at different usage levels:

The math is sobering. Even at 8 hours/day of continuous batched inference, you’re looking at 5+ years to recover your hardware investment compared to paying OpenRouter by the token.

But Wait—What About Gemma?

Here’s where the model choice matters enormously. Gemma 3 27B on OpenRouter costs just $0.058/M tokens. Local inference on the RTX 6000 Blackwell Pro Q-Max costs $1.66/M tokens at 65 tok/s.

Gemma 3 27B: Local vs Cloud

Cloud is 29x cheaper on pure cost. You would need to generate 5.3 trillion tokens locally just to match what you’d spend on the GPU—that’s never happening.

For Gemma 3 specifically, cloud wins decisively on cost. The only reason to run it locally is latency (88ms vs 760ms TTFT) or privacy.

The RTX 5090 Alternative

What about the consumer option? The RTX 5090 at $3,200 and 750W can run the smaller models:

For Qwen 3 VL 30B at 186 tok/s:

• RTX 6000: $0.58/M tokens
• RTX 5090: $0.34/M tokens

The 5090 is cheaper per token for models that fit, but you lose access to the 120B class entirely. And at $0.34/M vs OpenRouter’s $0.51/M, break-even is still 1.6 billion tokens—19 months at 8hr/day batched usage.

When Local Actually Wins

The spreadsheet says cloud wins. But spreadsheets miss three things:

1. Latency-Critical Applications

*Cloud TTFT varies widely—GPT-OSS ranged from 638ms to 7+ seconds depending on cold starts.

For interactive applications—chatbots, coding assistants, real-time document analysis—that 88ms vs 760ms difference is the gap between “snappy” and “sluggish.” Users notice.

The vLLM batched result of 37ms TTFT is remarkable. You can serve 4 concurrent users with near-instant response times while maintaining ~505 tok/s aggregate throughput.

2. Privacy and Data Control

Every prompt sent to OpenRouter leaves your network. For:

• Internal documents
• Customer data
• Proprietary code
• Medical/legal/financial information

…local inference isn’t just preferred, it may be required. No amount of OpenRouter’s cost advantage matters if your compliance team vetoes cloud AI.

3. High-Volume Batch Processing

The break-even math assumes you’re comparing local to cloud for the same workload. But local hardware enables workloads that cloud pricing makes prohibitive:

At 10 billion tokens/month—achievable with 24/7 batch processing—you’re saving $3,000/month. That pays off the GPU in 3 months.

The question isn’t “can I justify local for my current workload?” It’s “what would I build if inference were essentially free after hardware costs?”

The Decision Framework

Use OpenRouter when:

• Volume is under 500M tokens/month
• Latency tolerance is 500ms+
• Data isn’t sensitive
• You want zero infrastructure overhead
• You’re using Gemma 3 (it’s absurdly cheap in cloud)

Buy local hardware when:

• You need sub-100ms response times
• Data privacy is non-negotiable
• Volume exceeds 1B tokens/month
• You want to run custom/fine-tuned models
• You’re running 24/7 batch jobs

The hybrid approach:

• Local for development and privacy-critical production
• Cloud for burst capacity and low-volume use cases
• Different models for different purposes (Gemma 3 cloud, GPT-OSS 120B local)

What I’m Actually Doing

After running these numbers, here’s my setup:

1. RTX 6000 Blackwell Pro Q-Max with Ollama for interactive use—Ollama’s dynamic model loading and low latency make it worth the premium for development work
2. vLLM for batch jobs when I need to process large document sets
3. OpenRouter for Gemma 3 when I just need a quick answer and don’t care about latency
4. Local for anything touching client data—no exceptions

The GPU won’t pay for itself on pure token economics. But the warmth rising through the floorboards on a cold January night? That’s included for free.

Benchmarks run January 5, 2026. Hardware: RTX 6000 Blackwell Pro Q-Max. Models: GPT-OSS 120B, Gemma 3 27B, Qwen 3 VL 30B (all 4-bit quantized). Network: 25 Gbps LAN, 2.5 Gbps WAN with 5ms latency. OpenRouter pricing via API. Local power measured at wall. Your results may vary—especially if your basement doesn’t need heating.