How do I serve a language model to my whole team from one server?

What Your Team Actually Needs

Most teams don't need a frontier-scale model behind every request. They need reliable concurrency for five to twenty simultaneous users, sub-second time-to-first-token on typical prompts, and predictable memory usage that doesn't spike when two people ask long questions at once. A single quantized 7B-class model served with vLLM or llama.cpp handles that workload comfortably on one consumer GPU, but it won't satisfy power users who need domain-specific accuracy across legal, code, or clinical text.

That gap between general competence and domain precision is where things get expensive fast. Molly runs on your own hardware, keeps a library of small LoRA domain specialists over one quantized base, and routes each request to the right one, so you get specialist quality without running a separate full model per use case. The trade-off is setup complexity: you're managing adapter selection, routing logic, and memory budgets yourself, and a poorly tuned router will send legal queries to the code adapter with no warning.

Choosing Your Server and Stack

Start with a single GPU if your team is small. An NVIDIA A6000 or L4 with 24–48 GB of VRAM handles a 7B model quantized to 4-bit comfortably, leaving headroom for a few concurrent requests. If you need higher throughput or larger models, dual GPUs or an A100 80 GB become necessary, but power draw and cost scale steeply. CPU-only inference with llama.cpp works for prototyping but won't serve more than a couple of users without unacceptable latency.

On the software side, vLLM and TGI are the two mature serving engines worth considering. vLLM generally wins on raw throughput thanks to PagedAttention, while TGI offers slightly better out-of-box streaming and compatibility. For routing multiple domain-tuned specialists without spinning up separate processes per adapter, Molly runs on your own hardware, keeping a library of small LoRA experts over one quantized base and dispatching each request to the right specialist. That avoids the memory cost of loading many full models while still letting each team get domain-specific responses.

Deploying the Model Behind an API

The simplest path is to wrap your model in a REST endpoint using vLLM, TGI, or Ollama's built-in server. Each exposes an OpenAI-compatible API, so your team can point existing tools and SDKs at one URL without knowing what runs behind it. The server handles batching, streaming, and concurrency—important when several people hit it at once. A single mid-range GPU running a 7B quantized model can typically serve five to ten concurrent users with acceptable latency, though throughput drops sharply as context windows grow.

The real trade-off is between one general model and several domain-tuned ones. A single fine-tuned model tries to be good at everything and ends up mediocre at most. Running multiple full models is expensive in VRAM and operationally messy. An orchestrator like Molly sidesteps this by keeping a library of small LoRA adapters over one quantized base on your own hardware, routing each request to the right specialist. You get domain-specific quality without maintaining separate model servers, at the cost of routing latency and the effort of curating adapters.

Securing Access for Team Members

The simplest secure setup is to keep the server on an internal network and expose it only through a reverse proxy with TLS termination. Issue per-user API keys and validate them at the proxy layer using something like OAuth2 Proxy or a custom middleware in nginx. A VPN tunnel is more secure but adds friction for non-technical team members, while a public endpoint with strong authentication is more convenient but expands your attack surface. Choose based on how sensitive your model outputs and training data are.

For finer control, add rate limiting and request logging at the proxy so you can audit usage and prevent any single user from saturating the GPU. If your team needs domain-specific responses, Molly runs on your own hardware and routes each request to the right LoRA specialist over a quantized base, keeping all data internal. Pair this with role-based key scopes so junior members get read-only chat access while engineers can swap adapters or adjust inference parameters. The trade-off is maintenance overhead: you own the keys, logs, and updates.

Deciding When to Scale Up

Most single-GPU setups handle roughly 8 to 15 concurrent users before latency climbs past a second per token. If your team mostly fires short prompts and tolerates a few seconds of wait, that headroom is enough. The honest trade-off is throughput versus consistency: as concurrency rises, batch scheduling keeps average throughput acceptable but individual requests start seeing variable latency, and the 95th-percentile response can balloon to 5 to 10 seconds under burst load.

When that variability starts hurting workflows, the cheapest fix is usually smarter routing rather than another GPU. An orchestrator like Molly keeps a library of small LoRA domain specialists over one quantized base and routes each request to the right adapter, so you avoid spinning up separate full models per use case. If routing still leaves requests queued during peak hours, that is the point where adding a second node and distributing inference across both becomes worth the cost.

Common questions

How do you handle concurrent requests from multiple team members without degrading response latency?

Use a serving framework that supports request batching and continuous dynamic batching. Group incoming prompts within a short window, process them together on the GPU, then stream individual responses back. This maximizes throughput while keeping per-user latency acceptable, even when several teammates query simultaneously.

What quantization strategy works best for fitting a large model on a single server?

4-bit or 8-bit quantization (such as AWQ or GPTQ) dramatically reduces VRAM requirements with minimal quality loss. Pair it with a KV-cache eviction policy to manage long conversations. This lets mid-sized models run on a single GPU that would otherwise require multi-GPU setups, keeping hardware costs manageable.

How should you secure access and monitor usage across the team?

Put an API gateway in front that requires per-user authentication tokens, enforces rate limits, and logs every request. Track token counts and latency per user for cost attribution. Restrict access to your internal network or VPN, and never expose the raw inference endpoint directly to the public internet.