RTX 5060 Ti 16GB as OpenAI API Drop-In

Most OpenAI-SDK code works unchanged against any backend that speaks the same HTTP protocol – vLLM, TGI, TensorRT-LLM and others all implement the relevant routes. A RTX 5060 Ti 16GB via our dedicated GPU hosting running vLLM is a drop-in replacement for api.openai.com on Llama, Mistral or Qwen-class workloads. This guide covers the swap, the gotchas, and the cost math.

Contents

• Deploy vLLM
• The client swap
• Compatibility matrix
• Gotchas
• Cost comparison

Deploy vLLM

Run on the 5060 Ti with an FP8-native model and alias it to the OpenAI model name your code expects:

python -m vllm.entrypoints.openai.api_server \
  --model neuralmagic/Meta-Llama-3.1-8B-Instruct-FP8 \
  --quantization fp8 \
  --served-model-name gpt-4o-mini \
  --host 0.0.0.0 --port 8000 \
  --api-key sk-your-secret \
  --max-model-len 8192

Expected throughput on Blackwell 16 GB: ~112 tokens/s single-stream, ~1,800 t/s aggregate across 100 concurrent users. See the Llama 3 8B benchmark.

The Client Swap

Change two environment variables. Most SDKs read these automatically:

OPENAI_API_BASE=https://llm.your-domain.com/v1
OPENAI_API_KEY=sk-your-secret

Or in Python explicitly:

from openai import OpenAI
client = OpenAI(
    base_url="https://llm.your-domain.com/v1",
    api_key="sk-your-secret",
)
resp = client.chat.completions.create(
    model="gpt-4o-mini",                  # still the alias
    messages=[{"role":"user","content":"hello"}],
)
print(resp.choices[0].message.content)

LangChain, LlamaIndex, Instructor, Marvin, DSPy, Haystack and Semantic Kernel all accept either the env vars or a base_url argument – no code changes beyond configuration.

Compatibility Matrix

Gotchas

• Model name – if your code hardcodes gpt-4o-mini, alias it with --served-model-name; otherwise the SDK rejects the response
• Context length – Llama 3.1 8B supports 128k but vLLM’s KV cache on 16 GB VRAM is finite; set --max-model-len 8192 or 16384 to leave headroom
• Token counting – OpenAI’s tiktoken for GPT-4 gives different counts than Llama’s tokenizer; don’t compare bills like-for-like
• Rate limits – no hidden tier or TPM limits on your own box; add your own via nginx limit_req if needed
• Safety filtering – OpenAI adds a moderation layer; self-hosted does not, so add Llama Guard or a classifier in front
• Embeddings model – serve BGE-M3 or E5-large on a second port, set a --served-model-name of text-embedding-3-small for drop-in

Cost Comparison

See also: vLLM setup, FP8 deployment, Llama 3 8B benchmark, Docker CUDA setup, first-day checklist.