Open-Source LLM Deployment Llama, Mistral & Qwen, Privately

We deploy the major open-weight families, including Llama, Mistral and Mixtral, Qwen, and Gemma, across scales from roughly 7B up to large mixture-of-experts models. The choice is driven by your workloads rather than by leaderboards. We benchmark a shortlist on your representative prompts, measuring accuracy, latency and cost per request on the hardware you plan to use. Because we expose the model behind a stable internal API, the decision is never permanent: you can switch from Llama to Qwen later without changing your applications.

The saving depends entirely on volume and utilisation. At low, sporadic usage a commercial API is cheaper because you pay nothing when idle. Above a sustained volume threshold, self-hosting becomes materially cheaper because your cost is fixed infrastructure that is amortised across every request, so cost per request falls as usage rises. For high-volume workloads such as document pipelines or organisation-wide assistants, we commonly see 50 to 70 percent reductions against per-token pricing. We prove the crossover point with a break-even analysis on your real token volumes before you spend anything on hardware.

Either works. If your governance requires it, we deploy on bare-metal GPUs in your own data centre or server room. If you prefer cloud, we deploy inside your own Australian VPC on AWS, Azure or GCP, so the model still runs within a tenancy you control and in an Australian region. Many organisations start in their VPC to validate the workload with minimal capital outlay, then migrate to on-premises hardware once the business case and utilisation are proven. The same model and configuration transfer directly between the two.

No. That is the central point of an open-source deployment. The model weights, the inference server and every prompt and response stay inside your boundary, whether that is on-premises or your private VPC. There is no call out to an external model vendor, no telemetry egress, and no cross-border data transfer. All audit logging is retained on your own infrastructure. This structural guarantee is why regulated Australian organisations choose open-weight deployment over hosted APIs.

Raw model weights served naively will underperform and waste GPU capacity. We serve models with production runtimes such as vLLM or TensorRT-LLM, which provide continuous batching and paged attention to keep GPUs busy under concurrent load. We apply INT8 or INT4 quantisation where appropriate to fit larger models on fewer GPUs while preserving quality, and we tune the KV cache and batching parameters against your target concurrency. Every deployment is load-tested to confirm it meets your latency and throughput targets before go-live.

For most applications it is a base-URL change. We expose your private open model through an OpenAI-compatible API, so code written against the standard chat and completions interface points at your endpoint with minimal modification. Streaming and function calling are supported, and per-team API keys with quotas replace the vendor keys you use today. Where prompt behaviour differs slightly between the old and new model, we help tune prompts during validation so output quality meets or exceeds what you had.