RTX 5060 Ti 16GB for Gemma 2 9B

Gemma 2 9B is Google’s open-weights mid-size model. On the RTX 5060 Ti 16GB at our hosting it is a comfortable FP8 fit with good production performance.

Contents

• Fit
• Deployment
• Performance
• Chat template
• When to pick Gemma

Fit

• FP16 / BF16: ~18 GB – does not fit with KV cache
• FP8: ~9 GB – comfortable
• AWQ INT4: ~5.5 GB – very comfortable

Deployment

python -m vllm.entrypoints.openai.api_server \
  --model google/gemma-2-9b-it \
  --dtype bfloat16 \
  --quantization fp8 \
  --max-model-len 8192 \
  --gpu-memory-utilization 0.92

Gemma 2’s native context is 8192. Do not push higher – quality degrades. For long-context workloads pick Mistral Nemo 12B.

Performance

• FP8 batch 1 decode: ~78 t/s
• AWQ batch 1 decode: ~95 t/s
• FP8 batch 16 aggregate: ~480 t/s
• TTFT 1k prompt (FP8): ~220 ms

Chat Template

Gemma has a specific template with <start_of_turn> role markers. vLLM auto-detects from tokeniser config when you send OpenAI-format messages – no manual template needed.

When to Pick Gemma

Gemma 2 9B is strong on:

• Factual Q&A
• Summarisation
• Following strict safety constraints (aligned conservatively)

Weaker on:

• Creative generation (more restricted)
• Edge-case topics (refuses more aggressively than Mistral/Llama)
• Long context

For less-restrictive responses consider Mistral 7B.

See also: Gemma 9B benchmark, monthly cost.