In the last few years, the demand for GPU resources has surged in the evolving landscape on par with the development of new AI powered tech. Traditionally, teams have relied on local workstations or cloud services to meet these needs, however, HP’s introduction of HP Z Boost offers a compelling alternative: remote access to available GPUs within your organization’s workstation.
The GPU Dilemma: Local or Remote?
In the established scenario where you would like to start using this software and you’re Some time ago, if you were either training a large language model, rendering 3D scenes, or fine-tuning a deep learning model for any kind of research or industry project, there was a single question that was constantly raised:
“Should I invest in a beast GPU, or get some sort of remote power/cloud service?”
With the arrival of tools like Z by HP Boost, the choice isn’t as black-and-white anymore and you can combine both, and not relying entirely on cloud if you have direct local access to a GPU somehow. I will break down when it makes sense to go remote with Boost – and when sticking to a local GPU is still king.
What Is HP Z Boost?
In simple terms, Boost turns idle GPUs in a workstation into a shared pool of remote compute power. Anyone in your team can spin up those GPUs on-demand, from anywhere, without physically sitting at the machine. This sort of resembles having a cloud service, but on-premise – secure, fast, and managed by you or your team, which can useful, especially if you don’t want those cloud credits to ramp up within time.
In the following sections, I will give a couple of reasons on why I would think in which case (either remote with Boost or local) makes sense to go use your resources for AI training or other heavy-duty tasks.
When using Boost for remote access makes more sense
- Sporadic GPU Needs
Not everyone needs a big GPU 24/7. For occasional heavy workloads – like training a new model, doing experiments (especially if you have time to do so) or rendering final assets – tapping into a remote GPU saves thousands in upfront costs. If this is the case where your team has had the opportunity to invest in one or multiple GPUs, this can be very useful.
2. Shared Team Resource
For small and mid-sized teams, buying a high-end GPU for every developer or designer rarely adds up financially. It usually means some machines sit idle while others run at capacity. Sharing a central pool of GPUs helps spread that power around as needed, cuts down on wasted hardware, and delays expensive upgrade cycles.
3. Constant traveling
If you’re often working away from your main desk hauling a powerful laptop or an external GPU isn’t practical most cases. With Boost, you can run demanding tasks remotely on your office workstation’s GPU. Your local machine handles the interface; the heavy compute stays behind, saving your battery and your project.
4️. Protected data
Unlike cloud solutions, your compute and data stay on-premises. Boost encrypts traffic, so your models and proprietary data don’t float around on public servers or any cloud provider. This is especially useful if you are working with sensitive data and models.
Best-Case Remote GPU Scenarios
Occasional AI training & inference
Constant need to share resources simultaneously
Constant travelers in need of a GPU
Companies or teams needing secure on-prem compute, not cloud related
When Local still wins
- Low-Latency Requirements:
If you’re training very large models or having a huge workload that constantly read and write huge amounts of data, network latency can quickly become a bottleneck. Local GPUs keep computation and data transfer on the same bus, which is hard to beat for speed and consistency.
2. Always-On Heavy Loads:
When your models or projects are in constant work, either being real-time inference, or heavy rendering, dedicated local hardware usually makes more financial sense, especially when your team is smaller and you have direct access to the workstation and there’s no need for simultaneous access. In this scenario, the upfront investment pays off faster than paying for shared or remote resources over time.
The Hybrid Sweet Spot
A lot of teams could thrive with a hybrid approach:
• Local GPUs handle everyday heavy lifting.
• Remote GPUs cover spikes, occassional workloads, travel, or new experiments that have to run
• Need for low latency for certain projects, while others with not so tight deadlines and lighter workloads can be done simultaneously sharing parts of a GPU or other GPUs
🧠 Final Thoughts
Z by HP Boost isn’t meant to replace your local GPU entirely — it’s there to fill in the gaps when your local setup isn’t enough or isn’t practical. Think of it as an extra safety net: it helps creative and technical teams avoid overcommitting to expensive hardware that might sit unused for much of the month, while still having extra power on tap when deadlines or big experiments demand it.