Why Ollama Breaks at Scale (And What to Do About It)

Ollama is fantastic. It's how I got started with local LLMs, and there's nothing faster for going from zero to chatting with a model. You run ollama run llama3 and you're up in seconds. But when you try to scale it for your team or deploy it in production, you'll hit some walls. We analyzed 200 GitHub issues from Ollama and vLLM to understand why.

The Pattern We Keep Seeing

We built a tool called IssueParser that uses LLMKube to analyze GitHub issues at scale. We pointed it at the Ollama and vLLM repositories and asked it to find common pain points around multi-GPU support and production deployment.

The results confirmed what we'd been hearing from users: Ollama excels at what it was designed for, but hits limitations when you need to scale beyond a single user.

Problem #1: Sequential Processing

By default, Ollama processes requests one at a time. Each request waits for the previous one to complete. This is fine when you're the only user, but becomes a bottleneck the moment you have two people sending requests.

There's a workaround: set OLLAMA_NUM_PARALLEL to allow concurrent requests. But even with this enabled, the parallelism has limits. As Collabnix noted: "Ollama may not be suitable when you need high-concurrency, real-time responses."

Problem #2: Multi-GPU Doesn't Scale

This one surprised us. You'd think that adding a second GPU would double your throughput, or at least help significantly. But we keep seeing reports like this one from r/LocalLLaMA:

The problem is architectural. Ollama can use multiple GPUs to load a single model (tensor parallelism), but it doesn't efficiently distribute separate requests across GPUs. Your second GPU often sits idle while the first one is maxed out.

Problem #3: Request Timeouts Under Load

When requests queue up, things start timing out. From GitHub Issue #1187:

This creates a frustrating user experience. Your application thinks the LLM is down when it's actually just busy. Retry logic kicks in, making the queue worse. It's not that Ollama is broken; it's just being asked to do something outside its design goals.

What About vLLM?

The common advice is "use vLLM for production." And vLLM is excellent. It's fast, it handles concurrency well, and it's the standard for production LLM serving. But it's not without its own challenges.

Our GitHub issue analysis found that vLLM users struggle with:

• NCCL initialization failures on newer GPU architectures like B200
• Complex multi-instance setups when you need multiple models
• Resource management during cold starts and wake-up
• ROCm/AMD compatibility issues with missing dependencies

vLLM also requires additional tooling for production. In January 2025, the vLLM team released "production-stack" specifically because vanilla vLLM alone isn't production-ready. ByteDance released AIBrix for the same reason. The operational overhead is real.

The Real Issue: Dev/Prod Mismatch

Here's the pattern we see over and over:

1. Developer gets Ollama working on their Mac
2. They try to scale it for their team
3. Multi-GPU doesn't work as expected
4. They switch to vLLM but hit operational complexity
5. They end up paying for cloud APIs because self-hosting is too hard

Another user on Reddit captured this perfectly:

What We're Doing Differently

This is why we built LLMKube. We wanted something that:

• Actually uses multiple GPUs. Our layer-based sharding distributes work across GPUs without relying on NCCL. We tested this on real hardware with dual RTX 5060 Ti GPUs.
• Handles concurrency. Kubernetes does what it does best: scheduling, scaling, and resource isolation. Each InferenceService gets its own resources.
• Works on consumer hardware. You don't need A100s. We run 14B models on $400 GPUs.
• Abstracts the complexity. You define what you want in a CRD. LLMKube figures out the tensor splits, container args, and resource requests.

A Different Architecture

The key difference is that LLMKube uses llama.cpp's native tensor splitting instead of NCCL-based tensor parallelism. This means:

Layer sharding won't give you the absolute lowest latency for a single request. But it's simpler, more portable, and actually works on consumer hardware without NCCL headaches.

When to Use What

Here's our honest take:

• Ollama: Perfect for local development, prototyping, and single-user scenarios. It's the fastest path from zero to running LLMs, and there's a reason it's so popular.
• vLLM: Great if you have the ops expertise and need the absolute highest throughput on enterprise hardware.
• LLMKube: When you need to go from laptop to production without the operational complexity. When you're running on consumer GPUs. When you need Kubernetes-native deployment with proper resource isolation.

Try It Yourself

If you've hit Ollama's scaling limits and want to try something different:

# Install the CLI (macOS/Linux)
curl -sSL https://raw.githubusercontent.com/defilantech/LLMKube/main/install.sh | bash

# Install the operator
helm repo add llmkube https://defilantech.github.io/LLMKube
helm install llmkube llmkube/llmkube --namespace llmkube-system --create-namespace

# Deploy a model from the catalog
llmkube deploy llama-3.1-8b --gpu

The getting started guide walks through the complete setup. If you have multi-GPU hardware, check out the multi-GPU deployment guide.