RTX 5060 Ti 16GB for Phi-3

Microsoft’s Phi-3 family is built for exactly the hardware profile the RTX 5060 Ti 16GB offers: small-to-mid-sized dense models with strong benchmarks and compact footprints. A single Blackwell GB206 card can run Phi-3-mini at 285 tokens per second, Phi-3-small comfortably at BF16, and Phi-3-medium 14B at AWQ with room to spare. This guide covers each variant, its VRAM envelope on a UK Gigagpu node, and where Phi-3 outperforms heavier alternatives.

Contents

• The Phi-3 family
• VRAM and precision
• Throughput numbers
• Where Phi-3 wins
• Deployment
• Choosing a variant

The Phi-3 family

Phi-3 is Microsoft’s “small language model” line: 3.8B, 7B and 14B parameter dense transformers trained on heavily curated data. Quality per parameter is unusually high – Phi-3-mini routinely beats larger 7B models on reasoning benchmarks – which makes the family ideal for latency-sensitive or high-concurrency workloads.

VRAM and precision

Blackwell’s 5th-gen tensor cores run FP8 natively, so Phi-3-mini and Phi-3-small prefer FP8 W8A8. Phi-3-medium is better served by AWQ int4 to leave room for a long context cache.

Throughput numbers

Measured with vLLM 0.6 on a warm engine, prompt 256 tokens, output 256 tokens, BS as shown.

Where Phi-3 wins

Phi-3 shines in bounded, well-scoped tasks where throughput matters more than open-ended generation quality:

• Classification and routing – intent detection, content moderation, ticket triage. Phi-3-mini hits 1,400+ classifications/s with structured output.
• Short chat turns – FAQ bots, status queries, confirmation dialogues. 0.6-0.9s end-to-end responses.
• Function calling – Phi-3-small handles OpenAI-style tool invocation reliably.
• Agent subtasks – Phi-3-mini is excellent as a cheap inner loop under a bigger orchestrator.
• Long-context summarisation – the 128k variants ingest full reports in a single pass.

Deployment

# Phi-3-mini 128k, FP8, high concurrency
docker run -d --gpus all -p 8000:8000 vllm/vllm-openai:v0.6.3 \
  --model microsoft/Phi-3-mini-128k-instruct \
  --quantization fp8 \
  --max-model-len 32768 \
  --gpu-memory-utilization 0.85 \
  --enable-prefix-caching

# Phi-3-medium 14B, AWQ, quality-first
docker run -d --gpus all -p 8000:8000 vllm/vllm-openai:v0.6.3 \
  --model casperhansen/phi-3-medium-4k-instruct-awq \
  --quantization awq_marlin \
  --max-model-len 4096 \
  --gpu-memory-utilization 0.88

Choosing a variant

• Need raw throughput for short prompts -> Phi-3-mini FP8.
• Need 100k+ context on cheap hardware -> Phi-3-mini-128k.
• Want quality close to Llama 3 8B with lower latency -> Phi-3-small FP8.
• Want Llama 3 70B-tier answers on one card -> Phi-3-medium AWQ.

See also: Phi-3 mini benchmark, Llama 3 8B benchmark, Qwen 14B benchmark, vLLM setup, prefix caching.