Running large language models on your own hardware gives you full control over data residency, model weights, and request latency. For teams with strict compliance requirements or existing GPU clusters, on-premise deployment is often the default choice. The reality is more complex than downloading a checkpoint and running a script. You need to size hardware for peak throughput, select a serving engine that handles continuous batching, and build a production API layer that handles authentication, rate limiting, and model versioning. If infrastructure maintenance pulls engineering time away from product work, managed inference platforms such as Oxlo.ai offer a fully OpenAI-compatible API with flat per-request pricing, no cold starts, and more than 45 models ready for production.
Assess Hardware and Requirements
Before pulling a model from Hugging Face, calculate the memory footprint. A 70 billion parameter model at 16-bit precision requires roughly 140 GB of VRAM just for weights. Add overhead for the KV cache, activation buffers, and the serving framework, and a single node of consumer GPUs is often insufficient. Quantization is the most common mitigation. GPTQ and AWQ can reduce the weight memory by 50% to 75%, though you should benchmark perplexity to confirm quality retention for your use case. For models such as Llama 3.3 70B or DeepSeek R1 671B MoE, both of which are available on Oxlo.ai, on-premise deployment typically means multiple A100 or H100 GPUs with tensor parallelism across NVLink bridges. Record your targets for time-to-first-token and throughput in tokens per second,
