Running Local LLMs with Ollama: 3 Levels from Laptop to Cluster-Scale Distributed Inference

Running an LLM locally with Ollama feels magical at first.

You download a model, type a few commands, and suddenly your laptop is chatting like ChatGPT. It’s simple, private, and perfect for quick demos, prototypes, or personal exploration.

But that’s just Level 1.

As you scale or handle more requests, reality hits: responses slow down, memory fills up, and a single machine can’t keep up.

That’s when most teams start climbing the three levels of LLM deployment, from local experiments to high-performance servers like vLLM, and eventually to full-scale distributed systems like Bento Inference Platform.

Level 1: Local LLMs with Ollama#

Many people begin their LLM journey with Ollama. It makes running open-source models locally incredibly easy, supporting a wide range of models like Llama, gpt-oss, Qwen and DeepSeek.

Why Ollama is popular:

• Free to download and use
• Quick install on macOS, Windows, and Linux with no complex setup
• Automatic model downloads and management
• Support for quantized models that fit into smaller GPUs or even CPUs
• Works fully offline, keeping both models and data private
• A growing collection of high-quality models ready to run out of the box

These make Ollama ideal for developers, researchers, and teams that need fast prototyping, lightweight internal demos, or a personal assistant.

Limitations of Ollama#

As soon as you go beyond single-user chat, the limitations become obvious.

Response time slows down quickly under load, and it’s not uncommon to see replies take over 30 seconds once your machine is saturated. Since Ollama is mainly designed for single-instance use, it does not support high concurrency. One or two users are often enough to max out the system.

You’re also limited in how much you can optimize. There’s no advanced batching or inference optimization, so performance plateaus fast. If you need structured outputs, things get even trickier; you need to manually write extra Pydantic models or custom parsing logic to clean up the responses first.

When to move up#

I suggest you move to the next level when:

• Your single machine can’t handle concurrent requests
• You need predictable latency or higher throughput
• You want more control and customization over inference behavior, model performance, or output structure

Level 2: high-performance runtimes#

Once you outgrow a single local machine, the next step is running your models on server-grade inference runtimes. Tools like vLLM, SGLang, TensorRT-LLM, and Modular MAX are built for serious performance. They deliver a massive performance jump compared to Ollama.

What these runtimes offer:

• Advanced inference optimizations. Techniques like continuous batching, PagedAttention, speculative decoding, and optimized GPU kernels to boost throughput and reduce latency.
• Production-ready inference APIs. Leverage open-source or fine-tuned models to build high-performance APIs for applications like internal AI assistants or chatbots.
• Designed for high-end GPUs. Built to run on data center hardware like A100, H100, and H200, squeezing out every possible token per second.

Limitations of high-performance runtimes#

These frameworks also have certain constraints you’ll quickly run into as your deployment grows.

• They are built to mainly run on data center GPUs, and aren’t optimized for consumer cards.
• Fault tolerance is limited. If the machine goes down, your model goes down. Horizontal scaling isn’t built in.
• Because they expose so many levers, performance tuning becomes a complex process. You need to manually adjust CUDA, kernel configs, batching, and runtime flags to achieve the desired throughput or latency goal. This can involve many rounds of manual optimization.

When to move up#

I suggest you move to the next level when you want to:

• Reliably serve production traffic
• Scale efficiently and automatically when traffic spikes
• Get optimal performance on large GPU clusters
• Run inference across multiple regions, clouds or providers

At that point, the next natural stage is distributed inference.

Level 3: Distributed inference#

At this level, you’re not just serving a single model. You’re operating an fully-functional inference system at scale. Models, GPUs, and traffic are distributed across nodes, clusters, regions, and even clouds. The goal becomes balancing speed, cost, quality, reliability, and data security across your entire infrastructure.

Ideally, this level should look like this:

• Running distributed GPU clusters that shard or replicate AI workloads across machines
• Autoscaling to handle dynamic traffic and maximize GPU utilization, including scale-to-zero during idle periods
• Managing inference cross regions and clouds and deploying in hybrid or on-prem environments as needed
• Being able to build complex systems like AI agents, RAG pipelines, and multi-model workflows

Challenges of distributed inference#

At this level, you are managing dozens (or hundreds) of moving pieces, and small inefficiencies can multiply quickly. Here are the core challenges facing AI teams:

• Coordinating scale across clusters or regions. At scale, traffic rarely arrives evenly. Some regions spike, others idle. Finding the most available GPUs, routing requests to the right model, replica and region, and balancing cost across clouds all require sophisticated scheduling. Without intelligent orchestration, you end up with congestion, long queues, and inflated GPU bills.
• Slow cold starts for GenAI workloads. Scaling models is much harder than scaling traditional microservices. Downloading multi-gigabyte weights and loading them into GPU memory can take 10+ minutes. This is unacceptable for real-time user-facing applications.
• Implementing distributed inference techniques. Techniques like tensor/pipeline parallelism, prefill-decode disaggregation, KV-aware routing, and KV-cache offloading can help improve performance, but they’re hard to implement correctly. They require tight coordination across workers/nodes, careful memory management, and deep knowledge of inference runtime internals. These efforts can easily drain engineering bandwidth and slow down real product innovation.
• Operational overhead. Running distributed inference means handing multiple clusters and heterogeneous GPUs. To ensure they function reliably, you need automated failure recovery and comprehensive observability. However, every new model, GPU type, and feature release add more complexity. Over time, this creates a “hidden ops tax” that grows with traffic.
• Compliance and data security. When inference spans clouds or data centers, you must ensure data isolation, enforce access controls, and meet regulatory requirements like GDPR. Some workloads must run only in private VPCs or on-prem environments. This is especially important for regulated industries like finance and government.

How the Bento Inference Platform can help#

Our Bento Inference platform greatly simplifies Level 3 by offloading the most complex operational burdens to a unified, production-grade system.

• Support for BYOC, cross-region, multi-cloud, on-prem, and hybrid deployments:

  • Autoscale rapidly to the location with the best GPU rates and availability
  • Keep models and data inside your VPC for security and compliance
  • Optimize costs across different deployment environments
  • Avoid vendor lock-in while maintaining maximum flexibility

• Fast autoscaling with scale-to-zero support to cut costs during idle time and maximize GPU utilization. Most teams using Bento see GPU utilization rates averaging 70% or higher.

• LLM routing and gateway to direct traffic to the right model, region, or replica with warm KV cache.

• Tailored inference optimization with built-in support for techniques like PD disaggregation, KV-aware routing, KV cache offloading, and speculative decoding.

• Built-in observability with LLM-specific metrics like Time to First Token (TTFT) and Inter-Token Latency (ITL).

Bento allows engineering teams to focus on building products instead of wrestling with infrastructure. It offers a future-proof foundation for the next generation of AI inference, which is scalable, secure, cost-efficient, and cloud-agnostic.

FAQs#

What is the best local LLM?#

There isn’t a single “best” local LLM. The right choice depends on your hardware, language requirements, and use case. Popular open-source LLMs like Llama, Mistral, Qwen, Phi, and Gemma all run well locally (especially when quantized) and are fully supported by tools like Ollama.

For domain-specific tasks, such as finance, healthcare, law, or internal company workflows, fine-tuned open-source models often outperform larger general-purpose ones. With a small, high-quality dataset, it’s common to see a fine-tuned 8B–14B model outperform much larger models in accuracy, relevance, and consistency.

In short, choose a model based on your needs, and don’t hesitate to fine-tune.

Learn more about leading open-source LLMs and fine-tuning.

Ollama vs. vLLM — which one should I choose?#

Choose Ollama if you want a simple, local setup for personal use, prototypes, or offline experiments. It handles model downloads and quantization automatically, making it ideal for beginners or quick testing.

Choose vLLM if you need production-grade performance on server-class GPUs. vLLM offers advanced features such as continuous batching and PagedAttention, making it far more efficient for building inference APIs.

What tools can help with distributed or multi-cloud inference?#

Platforms like Bento Inference Platform simplify distributed and multi-cloud LLM deployment by handling routing, autoscaling, observability, model management, and multi-region GPU scheduling. They let you run inference across AWS, GCP, Azure, on-prem, or hybrid setups while keeping data inside your VPC.

For teams building distributed inference systems from scratch, this is far easier than manually managing everything. Schedule a call with the Bento team if you need any help.

Why shouldn’t I just call the OpenAI API?#

Using the OpenAI API is the fastest way to get started with LLMs, but it isn’t always the best long-term solution. As your workload grows, several limitations begin to matter:

• Cost at scale
• Latency and reliability
• Data privacy and compliance
• Customization and control
• Vendor dependence

Learn more about serverless vs. self-hosted inference.

Conclusion#

Local LLMs with Ollama are a great starting point. It’s fast to set up, private, and perfect for experimentation. However, as soon as traffic grows or performance becomes critical, you naturally move into high-performance runtimes. And once your workloads spread across multiple GPUs, regions, or clouds, the final stage is distributed inference: a system that can autoscale, route intelligently, and meet strict reliability and compliance requirements.

Every team moves through these three levels at its own pace. Understanding where you are today and what the next level unlocks helps you build smarter and faster.

Here’s a quick summary of the journey:

Level	Environment	Who it’s for	Main limitation
Local	Laptop or small workstation	Individual developers, early prototyping	Limited compute and concurrency
High-performance runtimes	Single or few GPU servers	Small AI teams, internal or pilot systems	Manual setup and tuning
Distributed inference systems	Large GPU clusters, multi-region or multi-cloud setups	Enterprises running production workloads	Complex setup, management and orchestration overhead

 

If you're ready to go beyond local experimentation and want a path that scales without managing the operational burden yourself, Bento Inference Platform gives you a future-proof foundation for high-performance and distributed AI deployments.

• Join our community Slack to connect with other developers, ask questions, and share learnings from real-world deployments.
• Sign up for the Bento Inference Platform to access high-performance, autoscaling inference across regions, clouds, and on-prem environments.
• Talk directly to the Bento team about your workload, architecture, and deployment needs so we can help you design the best solution.