Picking your first dedicated AI server is loaded with unknowns. The RTX 5060 Ti 16GB on our hosting is the lowest-risk choice: serves every mainstream open model, hits practical capacity, and is forgiving to configure.
Contents
What Runs Immediately
- Llama 3.1 8B FP8 at 32k context – vLLM one-liner
- Mistral 7B v0.3 FP8
- Qwen 2.5 14B AWQ at 16k context
- Phi-3 mini / Llama 3.2 1B-3B at any quantisation
- Stable Diffusion 1.5, SDXL, FLUX.1-schnell FP8
- Whisper large-v3 / Turbo transcription
- BGE / Nomic embedding servers (TEI)
- QLoRA fine-tuning up to 14B
What Doesn’t (Without Tricks)
- Llama 3.1 70B – needs Q2 GGUF + CPU offload (slow)
- Mixtral 8x7B – tight, considers CPU offload
- FLUX.1-dev FP16 – needs FP8 conversion first
- 128k context on 14B model – possible but KV-constrained
- Full fine-tune of 7B+ – VRAM insufficient, use LoRA/QLoRA
Day 1 Stack
# 1. Install CUDA and driver (usually preinstalled by provider)
# 2. Install Python + uv
curl -LsSf https://astral.sh/uv/install.sh | sh
# 3. Create vLLM env and run first model
uv venv --python 3.12
source .venv/bin/activate
uv pip install vllm
# 4. Serve
python -m vllm.entrypoints.openai.api_server \
--model meta-llama/Llama-3.1-8B-Instruct \
--quantization fp8 \
--kv-cache-dtype fp8 \
--max-model-len 32768 \
--gpu-memory-utilization 0.90In 20 minutes you have an OpenAI-compatible LLM API.
Common Early Pitfalls
- Trying FP16 on 8B: doesn’t fit with KV. Use FP8 or AWQ.
- Forgetting
--max-model-len: vLLM allocates full native context by default, often OOMs. Set explicitly. - Running without FP8 KV cache: halves your context for free – enable it.
- Ignoring prefix caching: one flag, huge TTFT win on chat.
- Missing driver update: Blackwell needs driver 560+. Verify with
nvidia-smi.
Your First AI Server
Blackwell 16GB – runs everything mainstream. UK dedicated hosting.
Order the RTX 5060 Ti 16GBSee also: first day checklist, sanity test, driver install, vLLM setup, FP8 Llama deployment.
