can I run LLM inference on my own GPUs?

Yes — you can run LLM inference on your own GPUs, and it’s a very common setup.

What you need

• A GPU with enough VRAM
  • Small models: can run on 8–12 GB VRAM
  • Medium models: often need 16–24 GB+
  • Larger models: may need multiple GPUs or quantization
• A runtime/framework
  • Popular options:
    • PyTorch + Hugging Face Transformers
    • vLLM for high-throughput serving
    • llama.cpp for CPU/GPU quantized inference
    • TensorRT-LLM for NVIDIA-focused optimization
• The model weights
  • Open-source models like Llama, Mistral, Qwen, Gemma, etc., depending on license
• CUDA / GPU drivers
  • For NVIDIA GPUs, usually CUDA-compatible drivers and libraries

Common approaches

• Single-user / local use
  • Run the model directly in Python or with a local server
• API server
  • Host the model on your GPU machine and expose an HTTP endpoint
• Optimized serving
  • Use vLLM or TGI (Text Generation Inference) for better batching and latency

Things to watch out for

• VRAM usage: model size + KV cache can be the real bottleneck
• Quantization: 8-bit or 4-bit can dramatically reduce memory use
• Throughput vs latency: serving one request vs many concurrent requests changes the setup
• Licensing: some models allow self-hosting, others have restrictions

Example setup

If you have an NVIDIA GPU:

1. Install driver + CUDA-compatible PyTorch
2. Install transformers, accelerate, and optionally vllm
3. Download a model
4. Run inference locally or serve via an API

If you want, I can help you choose:

• the right model for your GPU
• the best framework
• or give you a step-by-step setup for your exact hardware.