Phi-3-mini on RTX 5060 Ti 16GB Monthly Cost

Phi-3-mini on the RTX 5060 Ti 16GB delivers the lowest cost per million tokens of any serious self-hosted LLM on our hosting. Small model plus huge concurrency on 16 GB is a volume-economics machine.

Contents

• Throughput
• Monthly capacity
• Cost per million tokens
• Where it pays back
• Rules for picking Phi-mini

Throughput

Phi-3-mini BF16 on 5060 Ti benefits enormously from batching:

• Batch 1: ~135 t/s
• Batch 16: ~1,100 t/s aggregate
• Batch 32: ~1,400 t/s aggregate
• Batch 64: ~1,550 t/s aggregate peak

Monthly Capacity

At 50% utilisation on batch 32:

• Output tokens: ~1.8 billion/month
• Input tokens (3:1): ~5.5B/month
• Blended: ~7.3B tokens/month

Cost Per Million Tokens

At ~£300/month dedicated hosting:

• Blended cost per million tokens: £300 / 7,300 = ~£0.04 per M tokens
• At 80% utilisation (high-QPS backend): ~£0.025 per M tokens

Compare to APIs:

• OpenAI GPT-4o-mini blended: ~$0.30/M – 10-15x more expensive
• Together Phi-3 (if offered): ~$0.10/M – 2-3x more expensive
• Anthropic Haiku: ~$2.50 blended – 60x+ more expensive

Where It Pays Back

• High-volume classification and tagging (20k+ decisions/hour)
• Lightweight chat with many concurrent users
• Structured output extraction at scale
• Routing layer before hitting a larger model
• Social listening, sentiment analysis
• Content moderation at volume

Pick Phi-mini When

• Your task is bounded (classification, extraction) rather than open-ended
• Volume > 100k requests/day
• Per-request latency budget < 500 ms
• Model quality above Phi-mini’s ceiling is not needed

For workloads needing broader reasoning, use Llama 3 8B on the same card.

See also: deployment guide, classification use case.