Self-Hosted, Low-Latency LLM Inference

Self-hosted inference means running an open-weight language model on infrastructure a business controls, rather than sending every request to a third-party API and paying per token. It fits businesses with data residency or compliance requirements that rule out sending data to external providers, teams with high enough request volume that per-token API pricing becomes more expensive than running dedicated hardware, and products where response time is critical enough that network round-trips to an external API introduce unacceptable delay. FreyreSoft sets this up using serving frameworks such as vLLM to get high-throughput, low-latency inference from open-weight models on GPU infrastructure, choosing model size and quantization to fit the available hardware and expected load, and building the batching, autoscaling, and monitoring around the deployment so it holds up under real production traffic rather than only performing well in isolated benchmarks. The result runs inside infrastructure the business controls, on-premises or in a private cloud account, instead of behind a third party's API.

Why not just call a third-party API?

Third-party LLM APIs charge per token, which scales linearly with usage and can become the largest infrastructure line item once a product has meaningful traffic, with no ceiling on cost as volume grows. Sending every request off-site also means sending potentially sensitive data to an external provider, which is a blocker for businesses in regulated industries or with contractual data residency requirements that a public API cannot satisfy. And every external API call carries network latency on top of the model's own processing time, which is a real problem for products where response time is part of the user experience, such as real-time voice, live agent assistance, or interactive tools where a noticeable delay changes how the product feels to use.

How does FreyreSoft set up self-hosted inference?

FreyreSoft deploys open-weight models using serving frameworks built for throughput, most often vLLM, which handles request batching and memory management specifically to keep GPU utilization high under concurrent load rather than serving one request at a time. Model selection and quantization are matched to the available hardware and the expected request pattern, since a smaller or quantized model running efficiently on modest GPUs often meets latency targets that a larger unoptimized deployment would miss. The deployment is built with autoscaling so GPU capacity grows and shrinks with actual traffic instead of running fixed infrastructure sized for peak load around the clock, along with monitoring for GPU utilization, queue depth, and per-request latency so degradation is visible before it affects users. Depending on the business's requirements, this runs on-premises, in a private cloud account, or on GPU infrastructure the business controls directly, with the specific hardware and deployment topology chosen around data residency needs, expected concurrency, and cost targets rather than a single fixed template.

Technology we typically use

• vLLM serving engine
• Open-weight model families
• Model quantization (GPTQ, AWQ)
• GPU infrastructure and autoscaling
• PyTorch and Hugging Face
• Latency and throughput monitoring

What a project like this usually involves

1. Sizing and quantizing a model for the available GPU hardware
2. Setting up vLLM for batched, high-throughput serving
3. Building autoscaling around actual traffic instead of fixed capacity
4. Adding monitoring for GPU utilization, queue depth and latency
5. Choosing on-premises or private cloud deployment based on data residency needs

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