Resser Solutions

Infrastructure··9 min read

How to Deploy an LLM On-Premise in 2026 (Llama 3 on vLLM Guide)

A practical guide to deploying open-weights LLMs (Llama 3, Mistral, Qwen) on customer-owned GPUs with vLLM. What it costs, how to operate it.

Written byResser Solutions·Hire us for this →

How to deploy an LLM on-premise in 2026: pick an open-weights model (Llama 3 70B is the current default), serve it with vLLM behind a private OpenAI-compatible API, and front it with auth + cost telemetry + an eval loop. Sounds simple. The operating discipline is the hard part.

The minimal stack

  1. 01Hardware: 8× H100 80GB or 8× A100 80GB for Llama 3 70B at production throughput.
  2. 02Inference server: vLLM (default) or TensorRT-LLM (highest throughput).
  3. 03API gateway: OpenAI-compatible endpoint so client SDKs work unchanged.
  4. 04Auth + RBAC: API keys per tenant / per service.
  5. 05Observability: prometheus + grafana for latency / GPU util / errors.
  6. 06Cost telemetry: token-level accounting per tenant.
  7. 07Eval loop: regression suite on a refreshed dataset.

Quantization to fit smaller hardware

Don't have 8 H100s? Run Llama 3 70B in AWQ or GPTQ 4-bit quantization on 2× A100 80GB. Quality drop is small (1-2 percentage points on most evals). For smaller models (Llama 3 8B, Mistral 7B) a single L40S or even L4 is enough.

What it costs

SetupMonthly amortized
Single H100 node, self-hosted€4k - €7k
Single H100 node, rented (Lambda, CoreWeave)€8k - €15k
8× A100 cluster on-prem€12k - €20k
Build + ops engagement (one-time)€40k - €120k

FAQ

Frequently asked.

Which open-weights model is best for on-prem in 2026?

Llama 3 70B is the most-deployed for general reasoning. Qwen 2.5 family is competitive for code and structured outputs. Mistral / Mixtral for cost-efficient deployments. We benchmark on your data before locking the choice.

What GPUs do we need?

8× H100 80GB or 8× A100 80GB for unquantized 70B at production throughput. 2× A100 80GB or 1× H100 with 4-bit quantization for smaller workloads. Sub-7B models run on a single L40S or L4.

Can we deploy fully air-gapped?

Yes. Model weights, eval data, observability, admin tooling , all inside the perimeter. No outbound network calls. License terms for each model need to be reviewed; Llama 3 has specific commercial-use clauses.

How long does an on-prem build take?

Software build (LLM stack, RAG, eval, observability) typically 6-12 weeks. Hardware procurement is usually customer-led. Ongoing operations are best run as a monthly retainer for the first year.

Have a project like this? Send the brief.

We reply within one business day with a preliminary scope and a rough budget bracket.

Request project estimate→More notes →