Open Source LLM Deployment (Self-Hosted)

Self-hosted open source LLM deployment puts Llama 3, Mistral, Phi-3, and Mixtral on your own infrastructure. We handle model selection, GPU configuration, inference optimization, and production monitoring.

Run Your Own LLM. Keep Your Data Private. Control Your Costs.

Self-hosted open source LLM deployment puts a production-grade language model on your own infrastructure. You get the capabilities of GPT-4-class models without sending data to third-party APIs, without per-token pricing that grows unpredictably, and without depending on a provider who might change their terms, pricing, or model behavior at any time.

We deploy Llama 3, Mistral, Phi-3, Mixtral, and other open-source models on your cloud or on-premise servers, optimized for your specific hardware, latency requirements, and use case.

Why Self-Host an LLM

  • Data privacy - Your data never leaves your infrastructure. Mandatory for healthcare, legal, financial, and government applications with strict data residency requirements.
  • Cost control - No per-token pricing. At high query volumes (thousands of requests per day), self-hosting costs a fraction of commercial API pricing.
  • Model stability - Commercial providers update their models without warning. Self-hosted models produce consistent outputs until you choose to update them.
  • Customization - Fine-tune, quantize, and modify the model to match your exact requirements. No restrictions on use case or output format.
  • No rate limits - Scale to whatever throughput your hardware supports without throttling or API quotas.

What Our Deployment Service Covers

  • Model selection - We recommend the right model based on your task complexity, latency targets, and available hardware. Llama 3 70B for complex reasoning. Mistral 7B for fast, efficient inference. Mixtral for best quality-to-cost ratio.
  • Infrastructure setup - We configure GPU servers, container orchestration, and networking on AWS, GCP, Azure, or your on-premise hardware.
  • Inference optimization - We apply quantization (GPTQ, AWQ, GGUF), vLLM or TGI for serving, KV cache optimization, and batching to maximize throughput and minimize latency.
  • API layer - We build an OpenAI-compatible API endpoint so your applications can switch from commercial APIs to self-hosted with minimal code changes.
  • Monitoring and operations - We set up GPU utilization monitoring, latency tracking, error alerting, and automated restart procedures.

Deploy Your Own LLM

Book a free consultation. We will assess your use case, recommend the right model and hardware configuration, and give you a cost comparison between self-hosting and commercial API pricing for your projected usage.

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