Break-Even Analysis vs OpenAI API on an RTX 5090

The classic question: at what OpenAI API spend does a dedicated RTX 5090 start costing less? The answer depends on model choice and utilisation but the math is straightforward on our dedicated GPU hosting.

Contents

• The numbers
• 5090 throughput
• Break-even point
• Caveats

Numbers

Assume (Q2 2026 pricing):

• OpenAI GPT-4o-mini: ~$0.15/M input, ~$0.60/M output
• Self-hosted Llama 3.3 70B INT4 on 5090: fixed monthly hosting fee
• Average request: 1000 input + 400 output tokens

5090 Throughput

Llama 3.3 70B at INT4 on a 5090 runs via tensor parallel with a second 5090, or INT4 on 6000 Pro 96GB as single-card. For the single 5090 case, 30B models fit natively. Let’s compute for Qwen 2.5 32B INT4:

• Batch 8: ~420 tokens/sec aggregate output
• Per month (24/7): ~1.1 billion output tokens
• At 71.4% input : 28.6% output ratio: ~2.75 billion input tokens

Break-Even

OpenAI cost for that traffic equivalent:

• Input: 2.75B × $0.15/M = $412
• Output: 1.1B × $0.60/M = $660
• Total: ~$1,072/month

If a dedicated 5090 server costs ~$400-500/month on our UK hosting, you break even at roughly 40-50% utilisation. Above that, self-hosted is cheaper. Below that, API is cheaper.

Caveats

• GPT-4o-mini is not Llama 3.3 70B quality. For GPT-4o-class quality, pricing is 10x and break-even comes at much lower utilisation.
• Dedicated hosting gives you unlimited throughput up to hardware capacity – no rate limits or per-token tax
• Data residency and privacy may be worth the dedicated cost even below break-even
• Multi-model flexibility – run embeddings and reranker on the same box for no extra cost

See annual TCO comparison and SaaS unit economics.