Stop Renting GPUs: Serving Quantized 30B+ LLMs on CPU-Only Kubernetes Clusters with Ollama

In the current AI landscape, there is a pervasive myth that effective Large Language Model (LLM) deployment requires a hoard of H100s or A100s. For CTOs and IT decision-makers, this belief translates into exorbitant cloud bills and daunting hardware waitlists. The prevailing narrative suggests that if you aren't renting high-end GPUs, you aren't doing AI. We are here to challenge that narrative.

While GPUs are undeniably the kings of training, the landscape for inference—the act of actually using the model—is shifting rapidly. Thanks to advances in quantization techniques and the efficiency of runtimes like Ollama, it is now entirely feasible to serve massive models (30B, 70B, or even larger) on standard, commodity CPU hardware within a Kubernetes environment.

For Nohatek clients and modern enterprises, this represents a massive opportunity to democratize AI access internally without bankrupting the IT budget. In this guide, we will explore how to architecture a CPU-only inference layer using Kubernetes and Ollama, allowing you to run powerful models like Llama 3 or Command R on the infrastructure you likely already own.

The Economics of Inference: Why CPU?

The math behind GPU inference is often difficult to justify for internal enterprise applications. Renting a single high-end GPU instance can cost thousands of dollars a month. Conversely, high-memory CPU nodes are abundant, cheaper, and easier to procure. But how do we fit a 70 billion parameter model onto a CPU?

The answer lies in Quantization. Standard models usually store weights in 16-bit floating-point precision (FP16). However, research has shown that reducing this precision to 4-bit integers (INT4) results in negligible accuracy loss for most reasoning tasks, while drastically reducing the memory footprint.

The RAM Rule of Thumb: A 4-bit quantized model requires roughly 0.7 GB of RAM per billion parameters.

This means a 70B parameter model, which would normally require 140GB+ of VRAM (requiring multiple A100s), can fit comfortably into 48GB to 64GB of system RAM—a specification easily met by standard server blades. By leveraging the GGUF file format and modern CPU instruction sets (like AVX-512 available on Intel Xeons), we can achieve inference speeds that are perfectly acceptable for chatbots, RAG (Retrieval-Augmented Generation) pipelines, and background analysis tasks.

The Tech Stack: Ollama meets Kubernetes

To operationalize this, we need a robust serving layer. Enter Ollama. Ollama has rapidly become the standard for local and CPU-based inference because it abstracts away the complexities of llama.cpp into a clean, RESTful API. It manages the model weights, handles the tokenization, and optimizes the thread usage for the specific CPU architecture it detects.

However, running Ollama on a laptop is different from running it in production. This is where Kubernetes comes in. By containerizing Ollama, we can deploy inference nodes as standard K8s deployments. This offers several distinct advantages:

• Autoscaling: You can scale the number of replicas based on CPU load or custom metrics.
• Resource Quotas: K8s allows you to strictly define RAM limits to prevent OOM (Out of Memory) kills, ensuring the model stays resident in memory.
• Service Discovery: Your internal applications can query a stable ClusterIP service (e.g., http://llm-service:11434) without knowing which node is processing the request.

For an enterprise environment, we recommend creating a dedicated node pool for your AI workloads. These nodes should be memory-optimized rather than compute-optimized. This ensures that your 'noisy neighbor' web apps don't steal the memory bandwidth required for the LLM to generate tokens efficiently.

Implementation Strategy: From Docker to Deployment

Let's look at how to practically implement this. The first step is creating a custom Docker image. While you can use the official Ollama image, for production, you often want to bake the model into the image or use an initContainer to fetch it, so the pod starts up ready to serve.

Here is a conceptual example of a Kubernetes Deployment manifest for a CPU-based inference node:

apiVersion: apps/v1
kind: Deployment
metadata:
  name: ollama-cpu-inference
spec:
  replicas: 2
  selector:
    matchLabels:
      app: ollama-llm
  template:
    metadata:
      labels:
        app: ollama-llm
    spec:
      containers:
      - name: ollama
        image: ollama/ollama:latest
        resources:
          requests:
            memory: "64Gi"
            cpu: "8"
          limits:
            memory: "72Gi"
            cpu: "16"
        env:
        - name: OLLAMA_KEEP_ALIVE
          value: "24h"

Note the resources section. We are requesting substantial memory. If you are serving a 70B model quantized to q4_k_m, it will consume roughly 42GB of RAM. We request 64GB to leave ample headroom for the context window (the conversation history). If the system runs out of RAM and swaps to disk, performance will plummet to unusable levels.

Performance Tuning Tip: CPU inference is heavily bound by memory bandwidth, not just clock speed. Ensure your Kubernetes nodes are populated with multi-channel DDR5 or fast DDR4 RAM. Additionally, setting the OMP_NUM_THREADS environment variable to match the number of physical cores allocated to the container can significantly improve token generation speed.

Real-World Expectations: Latency vs. Cost

It is crucial to manage expectations. A CPU-only cluster will not match the tokens-per-second (TPS) of an H100 GPU. However, for many business use cases, it doesn't need to.

• Human Reading Speed: The average human reads at about 5-8 tokens per second. If your CPU cluster generates text at 10 tokens per second, the user experience is perceived as "instant" streaming.
• Batch Processing: For tasks like summarizing emails, analyzing logs, or classifying support tickets, latency is rarely a bottleneck. These jobs can run asynchronously on CPU nodes at a fraction of the cost of GPU instances.

By moving these workloads to CPU, you free up your scarce GPU resources for the tasks that actually demand them—like fine-tuning models or high-frequency trading algorithms. This hybrid approach is what we at Nohatek call "Tiered AI Infrastructure."

Furthermore, this architecture allows for total data sovereignty. You are not sending data to OpenAI or Anthropic; the model runs entirely within your VPC (Virtual Private Cloud) or on-premise datacenter, satisfying the strictest compliance requirements for healthcare and finance sectors.

The era of AI exclusivity is ending. You no longer need a seven-figure hardware budget to leverage the power of 30B+ parameter models. By combining 4-bit quantization, the efficiency of Ollama, and the orchestration power of Kubernetes, you can turn standard CPU servers into capable AI inference engines.

This approach reduces costs, simplifies supply chain logistics, and keeps your data secure. At Nohatek, we specialize in helping enterprises architect these cost-effective, high-performance AI solutions. Whether you are looking to build a private cloud AI cluster or optimize your existing Kubernetes infrastructure, our team is ready to help you stop renting and start owning your AI strategy.

Ready to optimize your AI infrastructure? Contact Nohatek today for a consultation.