How to Run Multiple AI Models on Your Server

Step 1: Plan Your Model Portfolio

Start by categorizing the tasks your agents perform by complexity. Simple tasks include classification (sentiment analysis, topic categorization), short-form extraction (pulling names, dates, or amounts from text), format conversion, and template-based responses. These work well with 7B to 8B models, which are fast and consume minimal VRAM.

Medium-complexity tasks include document summarization, general question answering, email drafting, and standard code generation. Models in the 13B to 14B range handle these effectively and offer a good balance of quality and speed.

Complex tasks include multi-step reasoning, nuanced analysis, creative writing, complex code generation, and tasks requiring broad world knowledge. Models at 34B to 70B deliver noticeably better results on these tasks but consume significantly more VRAM and generate tokens more slowly.

Map your agent workflows to these categories. Most organizations find that 60 to 70 percent of their agent tasks are simple or medium complexity, meaning a small model handles the majority of workload. Only the remaining 30 to 40 percent needs a larger model. This distribution matters for VRAM planning.

Step 2: Configure Model Loading Strategy

Concurrent loading keeps multiple models loaded in VRAM simultaneously. This provides instant model switching with no loading delay. The limitation is VRAM: you can only load as many models as fit in GPU memory simultaneously. On a 24 GB RTX 4090, you could run a 7B model (4.5 GB at 4-bit) and a 13B model (8 GB at 4-bit) concurrently with VRAM remaining for KV caches. This approach works best when you have two or three models that are all used frequently.

On-demand loading loads models into VRAM when requested and unloads them when idle. Ollama uses this approach by default, with a configurable keep-alive timer that controls how long an idle model stays loaded before being evicted. This lets you access many models from a single GPU at the cost of loading latency (2 to 15 seconds depending on model size and storage speed) when switching between models. Configure the keep-alive duration based on your usage patterns: longer for models used frequently, shorter for models used occasionally.

Multiple inference servers run separate inference server instances, each hosting a different model. This provides complete isolation between models and works well when different models need different configuration parameters. The downside is higher infrastructure complexity. You can run multiple Ollama instances on different ports, or mix inference engines (Ollama for one model, vLLM for another) if different models benefit from different serving optimizations.

Step 3: Set Up Model Routing

Model routing sends each task to the appropriate model based on task characteristics. There are several approaches to implement routing.

Agent-level routing assigns each agent to a specific model. Your customer support agent uses a 7B model, your research agent uses a 34B model, your code review agent uses a code-specialized model. This is the simplest approach and works well when agents have consistent complexity requirements. Configure this in your orchestration platform by specifying the model name in each agent's configuration.

Task-level routing uses a lightweight classifier (which can itself be a small language model) to examine incoming requests and route them to the appropriate model. Simple queries go to the fast model, complex queries go to the large model. This adds a small overhead for the classification step but optimizes resource usage by keeping expensive models reserved for tasks that need them.

Fallback routing starts with a smaller model and escalates to a larger model if the response quality is insufficient. The agent attempts the task with the fast model first. If the response fails a quality check (low confidence, incomplete answer, or explicit uncertainty markers), the same task is retried with the larger model. This approach minimizes large model usage while ensuring quality when needed.

Step 4: Optimize VRAM Usage

When running multiple models, VRAM management becomes critical. Several techniques help you fit more capability into available memory.

Quantization selection: Use 4-bit quantization for models that run concurrently. The quality difference between 4-bit and 8-bit quantization is typically 1 to 3 percent on benchmarks, but the VRAM savings are significant. A 13B model at 4-bit uses approximately 8 GB versus 14 GB at 8-bit.

Context length limits: Set maximum context lengths appropriate to each model's use case. A classification model processing short inputs needs a 2K context, not 8K. Shorter context limits reduce KV cache memory, freeing VRAM for other models or additional concurrent sessions.

Model scheduling: If your workload has predictable patterns (heavy customer support during business hours, batch processing overnight), schedule model loading to match. Load the customer support model during the day and swap to the batch processing model at night. Ollama's API supports model loading and unloading on demand.

Embedding model placement: Embedding models for RAG pipelines are typically small (under 1 GB) and can run on CPU without significant performance impact, freeing GPU VRAM for inference models. Most embedding workloads are not latency-sensitive enough to require GPU acceleration.

Monitoring VRAM usage: Track GPU memory consumption with nvidia-smi or monitoring tools like Grafana with the NVIDIA DCGM exporter. Set alerts for when VRAM usage exceeds 90 percent, which indicates you are approaching the limit where out-of-memory errors could occur. When running multiple models concurrently, VRAM pressure increases as each active conversation allocates KV cache memory for its context window. Monitor peak usage during high-traffic periods to understand your true capacity ceiling, not just idle-state memory consumption.