Llama 3.3 vs. ChatGPT Pro: Key Considerations

1. Intro

Here at Valdi GPUs, we pride ourselves on being the Switzerland of AI - meaning we work with all the projects, all the platforms, all the builders, all the tech. The AI landscape continues to evolve at blistering speed, with both commercial and open-source solutions competing for developer attention. The choice between the two is a fundamental and powerful one for organizations implementing AI solutions, with implications for cost, time, performance, privacy, and more, hanging in the balance. How do you choose? The new release of Meta’s Llama 3.3 70B and OpenAI’s ChatGPT Pro plan provides a good opportunity to compare the cost and performance tradeoffs between leading AI solutions. And, since Llama 3.3 is self-hosted and the Pro plan is not, the traditional considerations around cloud also apply and are discussed.

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2. Installation & Setup

Llama 3.3 70B on Valdi GPUs

The foundation of any open-source AI deployment begins with proper hardware configuration. Modern AI models typically require at least 40GB of VRAM for basic operation, with recommended configurations including 70GB or more for optimal performance. Parallel processing capabilities become crucial for inference speed, with NVIDIA GPUs supporting CUDA being the standard choice for many deployments.

Prerequisites for Llama 3.3 70B specifically are:

• A GPU capable of running the target model based on memory requirements; for our example we’re using an NVIDIA A40 48 GB from Valdi’s on-demand inventory
• Docker
• At least 70GB of available disk space

To get started, we’ll log into Valdi and spin-up an A40 quickly to do our deployment.

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Note: When you launch a GPU - if the node requires specific port mapping, be sure to create an external port mapping/forward for 8080 - the public port will be where you access the web UI.  Some of the providers on Valdi do not require port mapping and will provide access to all host ports configured on the node OS.

Quick Start with Llama 3.3 70B

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(Note: for this example, we are running on a Valdi VM with Ubuntu 22.04)

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Step 1: Install ollama

curl -fsSL https://ollama.com/install.sh | sh

Step 2: Pull down Llama 3.3 for ollama

ollama fetch llama3.3

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Step 3: Install and run open-webui

(Note: most Valdi VM instances come with docker pre-installed)

docker run -d --network=host --gpus=all -v ollama:/root/.ollama -v open-webui:/app/backend/data -e OLLAMA_BASE_URL=http://127.0.0.1:11434 --name open-webui --restart always ghcr.io/open-webui/open-webui:ollama

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The installation process with open-webui provides an easy and functional combo for model deployment.  The model weights will be downloaded from the repositories, though downloading times may vary significantly based on internet connection speed. Configuration files can be adjusted for your specific hardware setup, with particular attention to memory allocation and thread management.  Once the docker container is running, you can access the web interface on port 8080 on the public IP (or the forwarding port you configured when the VM was first deployed). You’ll be asked to create the default admin account, then you’re in.

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Common troubleshooting often centers around CUDA compatibility issues and memory management. Maintaining detailed logs during installation helps identify and resolve these challenges quickly.  With open-webui you can adjust parameters of the environment like number of threads (num_thread), batch size and more.

ChatGPT Pro

As a cloud solution, ChatGPT Pro does not require any installation or setup.

3. Features

OpenAI recently released their ChatGPT Pro offering which includes higher usage limits and access to a variety of their models, in particular O1 Pro mode - which is supported by more compute behind the scenes.

The Llama class of Large Language Models is one of the best examples of improving capabilities while not being lazy.  It’s clear the Llama team at Meta has targets to either keep the overhead required to run their models the same or improve it over subsequent releases.
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4. Cost Analysis

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Llama 3.3. 70 70B on Valdi GPUs

Hardware & Maintenance:

Initial deployment costs center around GPU selection and provisioning. With many quality GPUs - such as those from us at Valdi - it’s becoming very easy to get your own GPUs and even build out a cluster for the ultimate in flexibility and performance.  Monthly costs typically range from $200-$1500 monthly for a performant GPU and can vary by region and provider.  

Maintenance costs primarily involve time investment for updates and optimization rather than direct financial outlays.

Users:

Many users, even possibly your entire team, can use the deployment up to the capacity you’ve selected.

The table below is a great example of how accuracy of leading models relates to their approximate pricing.  While there are a lot of variables to hosting technology, controlling your own AI not only provides superior data privacy, but has a significant cost savings as you scale usage.  We’ve seen similar in the cloud SaaS space over the past decade and it looks like generative AI is following a similar power-law with general LLM deployments.

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ChatGPT Pro

Subscription

Instead of hardware and maintenance costs, commercial services often have tiered pricing models based on usage volume. OpenAI’s ChatGPT Pro, for example, was just released at $200 USD/month per user.  

Users: 1 for $200.

Tokens & Features

Consider not just the base subscription cost but also per-token charges and any additional fees for features like longer context windows or priority access. While the monthly subscription may seem higher initially, it can go even higher still when the company decides to implement a higher priced tier or new metered charges.  While higher packages from providers have higher limits, most providers implement usage limits and constraints. (e.g. max number of requests, token and context window limits)

The chart below highlights the trend towards price and performance optimization of new model releases.

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5. Performance Comparison

Response speeds vary significantly between self-hosted and cloud solutions. Local deployments may offer lower latency for individual requests but can struggle with concurrent loads. Output quality depends heavily on the specific model and use case, with both solutions offering competitive results for most applications. Context window capabilities affect the model's ability to handle longer conversations or documents, with recent advances expanding these capabilities across both platforms. Using datacenter quality GPUs like the Nvidia H100 and H200 can generate performance exceeding commercial SaaS solutions.  

The chart below shows the trend for models towards increased speed through the networks even while improving accuracy.

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We chose Llama 3.3 because the Llama team at Meta has shown a focus on improving the price vs. performance ratio of foundation models.  As the model-card below shows, 3.3 is improving context, in addition to improving accuracy.  

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6. Key Considerations

Properties of Llama 3.3. 70B and Open Source LLMs

Self-hosted solutions provide complete control over data flow and model behavior. Organizations can modify and fine-tune models for specific use cases, ensuring high performance for their particular needs. Privacy-conscious operations benefit from keeping all data within their infrastructure, meeting stringent compliance requirements more easily.  Data security, sovereignty, and privacy is highly regarded as a key aspect of open-source models.

ChatGPT and Commercial Solutions

Cloud-based commercial solutions eliminate infrastructure management concerns. Regular updates ensure access to the latest improvements without manual intervention. The ability to scale usage up or down based on demand provides flexibility for varying workloads.  A big issue with proprietary cloud AI solutions is that your data is controlled by a 3rd-party:  every artifact, query and piece of information you submit is able to be used by the commercial SaaS provider as they see fit.  Even with published terms-of-services, those terms are always subject to change.  

7. Implementation Tips

Successful deployment requires careful attention to resource monitoring and optimization. Implement proper security measures, including API key management for commercial services or network isolation for self-hosted solutions. Consider implementing caching mechanisms to improve response times and reduce resource usage. Document your deployment process thoroughly to facilitate future updates and troubleshooting.

Additionally:

• Use a firewall to lockdown ports on your node.
• Remove unnecessary User and System accounts
• Activate encryption-at-rest on your OS drive (note you don’t necessarily want to do this for VRAM / TEE features as it has a performance hit.)
• Use an Inference Engine like vllm.  Engines are optimized to load models and orchestrate queries.  It also makes it easier to scale usage later on.

7. Conclusion: Which option is better?

The choice between Llama 3.3 40GB and ChatGPT Pro (and open-source and commercial AI solutions in general) depends heavily on the cost-performance requirements of your specific use case, technical capabilities, and budget constraints.

Organizations with strong technical teams that can handle install, maintenance, optimization of self-hosting may benefit most from open-source deployments, while businesses seeking rapid deployment and minimal customization may prefer commercial solutions instead.

If security and privacy are your highest priority, controlling your GPUs is still your only option and the tools make it easier to do every day.

If not, consider starting with a hybrid approach, using both solutions to understand their practical implications for your specific needs.

Unlock the full potential of Llama 3.3 with access to high-performance GPUs designed for large-scale AI workloads. Scale up or down on demand, pay by the second, and deploy your models effortlessly—no long-term commitments, just peak performance when you need it most. Shop our GPU inventory.