RTX 5060 Ti 16GB for Yi 9B

Yi-1.5-9B from 01.AI is a solid open mid-tier LLM with particularly strong bilingual (Chinese + English) performance. It fits comfortably on the RTX 5060 Ti 16GB at our UK dedicated GPU hosting, with headroom for decent context lengths.

Contents

• Model overview
• VRAM and fit
• Throughput
• Deployment
• Yi vs peers
• When to pick Yi

Model Overview

Yi-1.5-9B-Chat is 01.AI’s updated instruction-tuned variant. 48 layers, 4 KV heads (GQA), 128 head dim, native context 4k with extensions to 16k and 32k available. Licensed under the Yi Series Model License – permissive for most commercial use with an attribution requirement.

VRAM and Fit

Throughput

• FP8 + FP8 KV decode at batch 1: ~95 t/s
• AWQ INT4 decode at batch 1: ~115 t/s
• Aggregate at batch 16 (FP8): ~490 t/s
• Prefill (FP8): ~5,200 tokens/sec

Slightly slower than Llama 3.1 8B per token because of the larger layer count (48 vs 32), but comparable overall performance.

Deployment

python -m vllm.entrypoints.openai.api_server \
  --model 01-ai/Yi-1.5-9B-Chat \
  --quantization fp8 \
  --kv-cache-dtype fp8 \
  --max-model-len 16384 \
  --enable-prefix-caching \
  --gpu-memory-utilization 0.90

Uses the standard apply_chat_template path in Transformers – no custom formatting required.

Yi vs Peers at the 9-12B Tier

When to Pick Yi

• Bilingual Chinese + English products where you want a permissive licence
• General chat where you want quality comparable to Gemma 2 9B at similar VRAM
• Long-context tasks – the 32k variant is a clean fit at AWQ INT4 + FP8 KV
• As a backup / alternative to Gemma 2 9B if its licence terms don’t suit you

For English-only workloads Gemma 2 9B or Qwen 2.5 7B-Instruct usually win. For stronger coding, pick Qwen 2.5 Coder 14B AWQ.

See also: Gemma 2 9B benchmark, Mistral Nemo 12B, Qwen 2.5 7B, FP8 deployment, AWQ guide.