RunPod vs. Hyperstack: Which Cloud GPU Platform Is Better for Fine-Tuning AI Models?

Fine-tuning pre-trained AI models – whether large language models (LLMs) or vision models – requires a robust cloud GPU platform. Your choice of platform directly impacts training speed, cost efficiency, and the ease of managing data and model checkpoints. In this comparison, we examine RunPod vs. Hyperstack from the perspective of machine learning engineers focused on fine-tuning. We’ll highlight how each platform handles GPU variety, speed, data management, containers, cost, persistence, and reliability, and why RunPod’s solution is often the more advantageous for fine-tuning workloads.

Fine-tuning allows developers to leverage existing pre-trained models and adapt them to specific tasks, saving considerable time and resources compared to training from scratch . However, LLMs typically require significant GPU memory (VRAM) to store model parameters and activations during fine-tuning – for large models you may need GPUs with very high VRAM capacity . The right platform can provide the necessary GPU horsepower (e.g. A100 or H100 GPUs with 80GB memory) along with features like fast provisioning, persistent storage for checkpoints, and flexible pricing. Let’s explore how RunPod and Hyperstack stack up.

Platform Overview: RunPod vs. Hyperstack

RunPod and Hyperstack are cloud GPU providers with different strengths. RunPod launched in 2022 as a specialized AI cloud, while Hyperstack launched in 2023 under NexGen Cloud as a European GPU-as-a-Service platform . Below is a quick overview of their key differences:

RunPod and Hyperstack both aim to make powerful GPUs accessible for AI development, but they take different approaches to fine-tuning workflows. Next, we dive deeper into each platform and then compare how they fare on critical aspects for fine-tuning AI models.

RunPod is an AI-dedicated cloud platform that has quickly gained popularity since its 2022 launch. It emphasizes flexibility and cost efficiency for AI workloads. With operations in over 30 regions, RunPod lets you spin up GPUs close to your data and users, reducing latency for distributed training and data loading . This global reach and multi-region redundancy also mean you can rely on RunPod for consistent availability.

Key features that make RunPod well-suited for fine-tuning tasks include:

• Wide GPU Selection: RunPod offers 32 unique GPU models across its regions, from cutting-edge NVIDIA H100/A100 to consumer GPUs like RTX 4090, as well as AMD MI series . This variety allows ML engineers to choose an optimal GPU for their model’s memory and performance needs. For example, you might use an 80GB A100 for a large LLM, or a cheaper RTX 3090 for a smaller vision model. All GPUs are available on-demand with no lengthy reservation process.

• Isolated, Containerized Pods: Each RunPod Pod runs in an isolated Docker container with direct access to a dedicated GPU (no hypervisor overhead). This ensures consistent performance for training runs without interference . You can bring your own Docker image or use RunPod’s ready-to-go environments, giving you full control over libraries and frameworks – a crucial factor when fine-tuning requires specific toolsets (e.g. PyTorch, Hugging Face Transformers, CUDA versions).

• FlashBoot Fast Startup: RunPod’s unique FlashBoot technology enables certain workloads to cold-start in seconds. In practice, this means less waiting and more iterating. If you’re fine-tuning a model and need to frequently start/stop instances (for example, adjusting hyperparameters or running short experiments), FlashBoot minimizes idle time.

• Flexible Billing & Fractional GPUs: For budget-conscious researchers, RunPod’s billing is extremely granular. Pods are billed per-minute (with no minimum duration), and serverless endpoints per-second. You only pay for the compute time you actually use, which is ideal for fine-tuning jobs that might run for a few hours or require interactive start-stop. Uniquely, RunPod even supports fractional GPU usage on select hardware – you can rent a fraction of a high-end GPU if your task doesn’t need the entire GPU, further improving cost efficiency. This model is perfect for smaller fine-tuning tasks or prototyping, where a portion of an A100’s power might suffice instead of paying for the whole card.

• Persistent Storage for Checkpoints: Fine-tuning typically involves saving model checkpoints and intermediate results. RunPod’s platform includes Network Volumes (in Secure Cloud) that provide persistent storage independent of any single pod. The data on these volumes persists even after the pod is terminated, and you can reattach the volume to new pods as needed . This makes it easy to pause a fine-tuning job and resume later, or to use one pod for training and another for evaluation with shared data. The volumes are backed by high-performance network file systems and can even be mounted to multiple pods simultaneously – useful if you want to, say, train on one GPU while periodically testing the model on another GPU without copying files around.

• Instant Clusters & Multi-Node Scaling: If you need to fine-tune very large models (for example, distributed training of a 70B parameter LLM), RunPod offers Instant Clusters for quickly provisioning multi-GPU, multi-node setups. This is a self-service way to get a cluster of GPUs working together (with NCCL support for distributed training). You can scale up to dozens of GPUs across nodes with a few clicks or API calls, then tear the cluster down when done – no lengthy cloud orchestration needed.

• AI-Specific Tools and Support: RunPod provides conveniences tailored to ML workflows, such as an LLM model directory (for one-click deployment of popular models), a DreamBooth fine-tuning API for vision model customization, and built-in monitoring of GPU utilization. Users also benefit from 24/7 support with AI expertise. The platform’s focus on AI means common fine-tuning issues (like dealing with large datasets, or installing NVIDIA drivers and frameworks) are well-documented and supported. Overall, RunPod’s developer experience is often praised for being user-friendly and efficient for data scientists and ML engineers.

Hyperstack is a newer entrant (launched in late 2023) that markets itself as a high-performance GPU cloud, particularly for the European market. It’s backed by NexGen Cloud and emphasizes scalable infrastructure and affordability for AI. Hyperstack provides access to top-tier NVIDIA GPUs with an orientation toward heavy workloads like training large models and running HPC applications.

Some notable features of Hyperstack include:

• High-End NVIDIA Hardware with NVLink: Hyperstack primarily offers NVIDIA’s flagship GPUs – currently A100 80GB and H100 80GB in various configurations, plus the NVIDIA L40 and A6000 for slightly lower-end needs. They support NVLink connectivity for multi-GPU setups (for example, linking multiple A100s or H100s in one VM to act as a larger combined GPU memory space). This is beneficial for fine-tuning very large models that might need more than 80GB of VRAM or that benefit from fast GPU-GPU communication. If you need to fine-tune a model across two or four GPUs in one machine (data parallel or model parallel training), Hyperstack’s NVLink-enabled instances can handle that with high throughput.

• Instant VM Deployment & Hibernation: Hyperstack allows one-click deployment of GPU virtual machines via its web interface. The provisioning times are relatively fast (though typically on the order of a couple of minutes for a VM to be ready, which is normal for full VM instances). A convenient feature for long training jobs is VM Hibernation – you can pause a VM when not actively training to save costs, then resume later. This is somewhat analogous to RunPod’s approach of shutting down pods and using persistent storage, but Hyperstack’s hibernation keeps the VM state in memory for quick resumption (useful if you want to pause overnight without losing your session state).

• Flexible Pricing with Reservations: Hyperstack’s pricing model is pay-as-you-go with minute-level billing, and they heavily promote reserved pricing discounts. By committing to longer-term use or reserving capacity, users can get significantly lower hourly rates (Hyperstack cites up to 75% cost savings compared to on-demand rates or legacy cloud prices) . This can make Hyperstack very cost-effective for steady, long-duration fine-tuning projects where you don’t mind committing to using a GPU for weeks or months. For example, an on-demand H100 SXM might be around $2.40/hour, but reserved it could drop to ~$1.90/hour or less. However, this approach benefits static, predictable workloads – it’s less flexible than RunPod’s zero-commitment, per-second billing if your usage is sporadic or experimental.

• NVMe Block Storage for Data: Each Hyperstack VM can attach high-performance NVMe SSD storage volumes. This is important for fine-tuning because large datasets or model checkpoint files can be read/written faster with local NVMe storage. You can choose the size of the volume (with additional cost per GB). The data on these volumes can persist independently of the VM if you configure it (for example, you might keep a volume with your dataset and reattach it to new VMs). That said, Hyperstack does not have a native multi-instance shared filesystem feature comparable to RunPod’s network volumes; managing persistence and sharing of data requires a bit more manual handling (like saving snapshots or reattaching volumes to new VMs).

• Kubernetes and Large-Scale Clusters: For advanced users, Hyperstack offers managed Kubernetes clusters and an “AI Supercloud” concept for scaling to very large GPU counts (they tout the ability to deploy 8 to 16,384 GPUs across a supercluster). In practice, leveraging this requires familiarity with DevOps tools – you might use their Terraform provider or Kubernetes API to orchestrate many GPU nodes. This indicates Hyperstack is capable of supporting massive distributed training jobs (e.g., fine-tuning giant models or running hyperparameter sweeps on many GPUs), but it’s more of an enterprise feature. The average ML engineer fine-tuning a model might not need this level of scale, and achieving it isn’t as simple as RunPod’s one-click instant cluster (instead, it’s a more involved setup).

• EU Data Sovereignty and Green Infrastructure: A distinguishing aspect of Hyperstack is its European focus. The platform is based in Europe and positions itself as an alternative to US cloud providers, which can appeal to organizations with EU data residency or compliance requirements. All Hyperstack data centers run on 100% renewable energy, making it a “green” cloud option for AI workloads. While this doesn’t directly affect the fine-tuning performance, it’s worth noting for companies that prioritize sustainability or avoiding U.S. cloud jurisdiction. Hyperstack is part of NVIDIA’s Inception program and has been rapidly expanding its GPU fleet in 2024–2025.

In summary, Hyperstack provides excellent raw hardware and cost opportunities (especially for long-term usage of high-end GPUs), but as a newer platform it may have fewer convenience features for developers and a smaller support ecosystem compared to RunPod.

Comparative Analysis: Fine-Tuning on RunPod vs. Hyperstack

Choosing between RunPod and Hyperstack for fine-tuning comes down to the technical features that matter most for your projects. Let’s compare the platforms on key criteria relevant to fine-tuning AI models:

For fine-tuning, you need the right GPU with sufficient memory and compute power, and you want it available quickly when you’re ready to train. RunPod has a clear edge in GPU variety and immediate availability. It offers 32 different GPU models across its global regions, including not only the latest NVIDIA H100 and A100, but also mid-tier and consumer GPUs (like RTX 6000 series) for smaller jobs . This means you can always find a GPU that fits your task’s requirements and budget. Hyperstack, in contrast, focuses on a narrower range of top-end GPUs (primarily 80GB Ampere and Hopper cards). While Hyperstack’s GPUs are high-performance, the limited selection could be a bottleneck – for instance, if all H100s are occupied, there may not be an alternative GPU type to fall back on, whereas RunPod might have other comparable options available in the same or another region.

RunPod’s deployment speed is also optimized for developers. Launching a RunPod instance (Pod) is very fast – thanks to containerization and features like FlashBoot, many workloads start up in seconds. Hyperstack’s full VMs tend to take a couple of minutes to boot. This difference becomes significant if you iterate often. Consider that fine-tuning often involves many cycles of start/stop (to tweak code or hyperparameters); with RunPod, you spend almost no time waiting for instances to be ready, whereas with Hyperstack you’d be waiting on VM boot each time. Additionally, RunPod doesn’t require any reservations – GPUs are available on-demand without a queue. Hyperstack also offers on-demand access, but if you want the best price you might have reserved a specific GPU type, which slightly reduces flexibility (you’d stick with that GPU for cost reasons).

In terms of raw performance, both platforms deliver uncompromised GPU power since you get dedicated access to the GPU. RunPod’s pods run on bare-metal attached GPUs, and Hyperstack’s VMs similarly have direct GPU passthrough – so pure training performance (e.g., images/sec or tokens/sec processed) will largely depend on the GPU model itself. One difference is in multi-GPU configurations: Hyperstack’s NVLink-connected multi-GPU VMs can offer higher intra-node bandwidth for distributed training on one machine. RunPod’s approach to multi-GPU is to connect multiple pods (possibly across multiple machines) in a cluster via networking. For most fine-tuning cases (which often use 1–4 GPUs), RunPod’s single-GPU per pod model works great and you can still cluster if needed. Only for extremely large parallel jobs might Hyperstack’s single-machine multi-GPU with NVLink have an edge in communication speed.

Finally, network throughput can affect data pipeline performance when fine-tuning on large datasets. Hyperstack touts up to 350 Gbps networking on certain instances, which is exceptionally high. RunPod on the other hand emphasizes low-latency regional deployments and minimal network overhead – essentially, placing your data and compute in proximity to negate the need for extreme network throughput. In practice, both platforms will allow high-speed data loading (RunPod offers free high-bandwidth ingress from your storage). Unless you have a very unusual data streaming requirement, networking will not be a limiting factor on either platform for fine-tuning tasks.

Bottom line: RunPod provides greater flexibility and faster startup for fine-tuning tasks, with a wider range of GPUs ready to go. Hyperstack can deliver strong performance too, especially if you specifically need multi-GPU NVLink setups, but it’s less flexible in hardware choice and quick cycling of jobs.

Cost is often a deciding factor for long-running fine-tuning jobs. Both RunPod and Hyperstack are far more cost-effective than traditional clouds (like AWS) for GPU rentals. Hyperstack’s strategy is to offer low hourly rates, especially through reserved contracts, whereas RunPod offers low per-second rates on a purely on-demand basis.

RunPod’s pricing for popular GPUs is extremely competitive and straightforward. For example, an 80GB A100 on RunPod Secure Cloud is about $1.19/hour on-demand , and an 80GB H100 is around $2.79/hour on-demand (pricing as of this writing). These rates are flat and you pay only for actual usage time (billed down to the minute, or second for serverless jobs). If your fine-tuning job finishes early, you save money by not paying for unused time. Also, RunPod does not charge for data transfer (no ingress/egress fees) , which can otherwise add up when moving large datasets or model files.

Hyperstack’s on-demand rates for comparable GPUs have been in a similar ballpark or slightly lower in some cases – for instance, ~$1.40/hour for an A100 and ~$1.95/hour for an H100 (on-demand) according to recent Hyperstack info. Where Hyperstack tries to undercut is with reserved pricing: if you commit to a longer term, the hourly price drops (e.g. H100 reserved at ~$1.37/hr, A100 at ~$0.95/hr) . They advertise up to 75% savings versus standard cloud providers by using these reservations . This can benefit a scenario where you know you’ll be fine-tuning or training for, say, a solid month continuously – you’d lock in a reservation and indeed get a great rate.

However, for many ML engineers doing fine-tuning, workloads are bursty or project-based rather than continuous. You might spin up GPUs for a week, then not need them for a while, or run a few hours a day. In such cases, RunPod’s no-commitment pricing is actually more cost-efficient and far simpler. You aren’t locked into any plan and you don’t have to predict usage in advance. Additionally, RunPod’s option to use fractional GPUs (pay even less for smaller jobs) and to scale down to zero when not in use (especially with serverless endpoints, where billing stops the moment the endpoint is idle) can yield substantial savings.

It’s also worth noting that RunPod’s Community Cloud (spot marketplace) can provide even cheaper rates for those who are very cost-sensitive. Community Cloud nodes, provided by third-party hosts, often include consumer-grade GPUs at discount prices. For example, you might find an RTX A6000 or RTX 3090 at a lower price per hour than any A100. Hyperstack does not have an equivalent of a community/spot market – all its offerings are from its own data centers with fixed pricing.

In summary, RunPod offers greater cost flexibility: you get low prices without commitments and fine-grained billing to avoid overspending. Hyperstack can be cost-effective for large steady workloads if you utilize reserved pricing, but that comes with the trade-off of less flexibility (and you might pay for idle time if your usage isn’t constant). For most fine-tuning use-cases, where experimentation and intermittent usage are common, RunPod’s pay-as-you-go model will likely result in lower overall costs and less hassle in managing contracts.

When fine-tuning models, you may sometimes need to scale up resources – for example, using multiple GPUs for a distributed training run, or running several experiments in parallel. RunPod and Hyperstack both support scaling, but the ease of doing so differs.

RunPod is designed to be developer-friendly in scaling scenarios. Need more GPUs? You can deploy an Instant Cluster with a few clicks or via API, joining multiple pods together. This is essentially horizontal scaling – you add more GPU instances as needed. Since RunPod has many regions and a large pool of GPUs (including fractional options), you can usually find capacity even on short notice. The platform’s API allows programmatic scaling, which is great for automated hyperparameter search or scaling up a training job when you detect it needs more compute.

Moreover, RunPod’s multi-node scaling does not require you to manage the underlying infrastructure intricacies – networking between pods is handled for you with low-latency links. For example, if you want to fine-tune a transformer model on 4 GPUs, you can launch a cluster of 4 pods and use PyTorch Lightning or Hugging Face Accelerate to distribute across them. The experience isn’t much different than using 4 GPUs on one machine, aside from a minor initial setup of communication backend.

Hyperstack can also scale to many GPUs, but its approach is oriented more toward infrastructure control. To use a large number of GPUs, you might start a multi-GPU VM (for up to 8 GPUs in one VM with NVLink) or set up a Kubernetes cluster to handle multiple VM instances. This gives you flexibility if you know how to manage it, but it’s not as instant or simple as RunPod’s approach. Hyperstack’s advertised ability to reach thousands of GPUs is likely through careful orchestration (potentially involving their team’s support for such big deployments). In contrast, RunPod’s users have spontaneously spun up hundreds of GPU pods for distributed workloads because the platform self-serve scaling is very quick.

Another aspect of flexibility is in the range of use cases supported. RunPod is not only for training/fine-tuning – it also supports serving models (serverless inference endpoints), interactive development (notebooks/SSH on pods), and more. This means you can fine-tune a model on RunPod and then deploy the same model on an endpoint for real-time inference all within the same platform. Hyperstack is more narrowly focused on providing raw GPUs for you to do what you want; deploying an inference endpoint would be up to you to set up on a VM or move to another service. For a streamlined workflow (train -> deploy), RunPod’s integrated features add flexibility.

Additionally, fractional GPUs on RunPod (mentioned earlier) provide a kind of scaling down. You have the flexibility to use only part of a GPU if full power isn’t needed, which Hyperstack doesn’t offer. This granularity is useful for testing or smaller fine-tuning tasks (e.g., fine-tuning a small 100M parameter model) where even an A100 might be overkill. Instead of paying for the whole card, you pay proportionally on RunPod.

In short, both platforms can handle scaling up to serious workloads, but RunPod makes scaling simpler and more granular, fitting the dynamic needs of fine-tuning projects. Hyperstack is capable of massive scale, but requires more planning and possibly external tools to harness that scale.

Fine-tuning is a data-intensive process – you need to load datasets, save model checkpoints, and possibly resume training if interrupted. Efficient data handling and reliable storage are thus key considerations.

On RunPod, data management is very straightforward and developer-friendly. Every pod comes with an attached ephemeral storage for scratch data, and more importantly, you can mount persistent volumes to pods. RunPod’s Secure Cloud volumes act like a network drive that stays available even after a pod is terminated . This means you can train a model, save checkpoints to the attached volume, shut down the pod (incurring no further compute cost), and later re-launch a new pod and pick up right where you left off by mounting the same volume. It’s a built-in checkpointing solution. RunPod also ensures these volumes are stored redundantly behind the scenes to protect against hardware failure – adding a layer of reliability for your valuable model weights.

For example, suppose you are fine-tuning a vision model and periodically saving weights (e.g., model_epoch_10.pt, model_epoch_20.pt). Using a RunPod volume, those files persist after your training pod is stopped. If you later start a different GPU instance (maybe to evaluate the model or continue training), you simply attach the same volume and all your files are immediately accessible. This streamlines the workflow for iterative fine-tuning and experimentation.

Hyperstack, being VM-based, treats storage in a more traditional way. You have NVMe block storage volumes that you can attach/detach from VMs. If you terminate a VM without saving its disk, you’d lose data, so you need to consciously use a separate volume for anything you want to keep. You can indeed persist data by detaching a volume before deleting a VM, or by creating a snapshot of your volume. It’s effective, but requires more manual steps compared to RunPod’s always-on network volume approach. Also, Hyperstack doesn’t currently support sharing one volume across multiple running VMs simultaneously (whereas RunPod volumes can be attached read-write to multiple pods at once, facilitating scenarios like training on one pod and validation on another sharing the dataset).

When it comes to moving data in and out, RunPod has an advantage of free data transfer – you can download your training data or upload your fine-tuned model without incurring egress fees . Hyperstack’s documentation doesn’t highlight data transfer costs, which suggests they may include it in the service (likely not charging extra for reasonable usage), but it’s not explicitly promoted as free. If your fine-tuning involves terabytes of data, it’s worth verifying with Hyperstack to avoid surprises. With RunPod, it’s clear that you won’t be billed for data egress, making it easier to integrate with external data sources or backups.

In terms of raw data I/O performance, both platforms give options for high-speed storage (RunPod’s network volumes are backed by fast storage and Hyperstack’s local NVMe is very fast). Unless you are doing something extremely disk-intensive, both will handle typical ML dataset throughput well (e.g., streaming images or text at hundreds of MB/s). For most users, the difference will be in convenience and reliability of persistence. RunPod essentially provides a plug-and-play solution for persistence and checkpointing, whereas Hyperstack requires you to plan your storage usage (attach volumes, manage them outside the VM lifecycle).

Checkpointing reliability is also about not losing your work if something goes wrong. RunPod’s stable infrastructure across many regions means you can also choose to periodically sync your checkpoints to another region or cloud for safety (since data egress is free). Hyperstack’s European focus might mean less geographic redundancy if that matters to you (though they do have some presence in North America now). In practice, both platforms will safely keep your data as long as you use the persistent storage options correctly, but RunPod makes it easier to “set it and forget it” for keeping your fine-tuning outputs safe.

Both RunPod and Hyperstack target technical users, but RunPod’s platform is inherently more developer-experience oriented, given its AI-specific features and community.

Environment setup: RunPod’s use of Docker containers means that setting up your training environment is seamless. You can select from pre-configured images (with popular frameworks like TensorFlow, PyTorch, etc.) or supply your own Docker image if your project has unique dependencies. This containerized approach ensures consistency – if it works locally in your Docker, it will work on RunPod. Hyperstack, on the other hand, gives you a raw VM (typically with an OS image like Ubuntu). You are responsible for installing CUDA, drivers, libraries, and so on, unless you prepare a custom VM image. This is akin to the difference between a PaaS and IaaS: RunPod feels more plug-and-play, whereas Hyperstack might require that initial setup effort. For fine-tuning tasks, which often rely on specific library versions (for example, a certain version of Transformers or Diffusers), the container approach can save time and avoid environment issues.

Ease of use: RunPod’s interface (both web UI and CLI/API) is designed with ML workflows in mind. It’s straightforward to monitor your GPU utilization, set up SSH or Jupyter access to a pod, and manage multiple pods. The learning curve is gentle – many users report that they find RunPod one of the easiest GPU cloud platforms to work with. In fact, an independent review noted that “Runpod is my favorite and the easiest platform to use for data scientists” , highlighting its user-friendly design. Hyperstack’s UI is improving but being newer, it might not yet have all the polish. Power users might end up interacting with Hyperstack more through infrastructure-as-code (Terraform scripts, etc.) especially for advanced setups, which is powerful but not as simple as clicking a button on a web dashboard.

Support and community: RunPod offers free 24/7 support and has an active community (including a Discord server and forums) where fellow users and RunPod engineers can help with issues. This is valuable when fine-tuning – if you encounter an issue (say, how to set up distributed training, or a question about optimal instance type), you’re likely to find answers quickly. Hyperstack, being smaller, has a support team you can contact (and since it’s more enterprise-focused, likely good account support for paying customers), but the community aspect is not yet as large. Their documentation exists but might not cover as many “AI cookbook” scenarios since the user base is smaller.

Specialized features: RunPod, as mentioned, has features like the DreamBooth endpoint, an LLM hosting directory, and examples in their docs specifically for fine-tuning certain models. This shows a developer-centric approach – the platform is anticipating and catering to common use cases in AI development. Hyperstack is more about raw capability and less about built-in AI workflows. Over time Hyperstack might add more managed services, but currently, RunPod feels more tailored to the ML engineer’s journey from start (getting a GPU and setting up code) to finish (saving the model and deploying it).

Reliability & trust: Because RunPod has been serving AI researchers and startups for a few years now, it has built a solid reputation. It handles millions of GPU requests per day on its infrastructure , indicating a mature and robust service. Hyperstack, launched more recently, is still proving itself at that scale. This doesn’t mean Hyperstack is unreliable, but when choosing a platform for critical work, many developers prefer the one with a longer track record. RunPod’s compliance certifications (SOC 2, etc.) also provide confidence if you’re working in regulated industries or with sensitive data.

Overall, from a developer’s point of view, RunPod offers a smoother and more guided experience for fine-tuning tasks, whereas Hyperstack might appeal to those who prefer a more hands-on infrastructure feel or need its specific hardware benefits. Most ML engineers will appreciate RunPod’s ready-to-go environments and supportive ecosystem when fine-tuning models.

Running long fine-tuning jobs (which can sometimes last for days) demands a platform that is reliable and secure. Any unexpected interruption or instability can waste time and money.

RunPod operates in multiple top-tier data centers (Tier 3/Tier 4), providing a high level of reliability. If one region has an issue, you often have the option to spin up in another region, given the 30+ region spread. The platform’s uptime track record has been strong, and status pages are available for transparency. With regards to security, RunPod’s Secure Cloud environment ensures container-level isolation and dedicated hardware for sensitive workloads. They have achieved SOC 2 Type I certification and partners compliant with standards like HIPAA and ISO 27001 , which means enterprise-grade best practices are in place for handling data securely. This is reassuring if you are fine-tuning models on proprietary data. Additionally, RunPod’s support team is available around the clock and focused on AI use cases, so any technical issues that could affect a long training job (such as a pod issue or a networking glitch) can be addressed promptly.

Hyperstack, while emphasizing sustainability and data sovereignty, does not yet list similar compliance certifications. Being based in Europe, they inherently satisfy data locality requirements for EU data (which can be a form of compliance in itself for GDPR, etc.). They also partner with renewable-energy-powered data centers, which speaks to operational excellence but not directly to technical reliability. As a young service, Hyperstack’s reliability is expected to improve as it matures; however, at present, RunPod’s reliability and support depth have an edge simply due to more time in operation and a larger user base ironing out issues.

One particular feature on Hyperstack’s side for reliability of long jobs is the VM hibernation – if you pause your job, you won’t be affected by someone else taking your spot, since the capacity is reserved for you. On RunPod, if you stop a pod, you release that capacity (unless you immediately start a new one). However, RunPod’s massive capacity and fractional usage model mitigate this, as there’s almost always another GPU available when you need it. It’s also worth noting RunPod’s Community Cloud adds an extra layer of redundancy – if needed, you can even run the same job simultaneously on two different providers (one on Secure, one on Community) for a critical job and keep the one that completes first, an uncommon scenario but possible thanks to RunPod’s flexibility.

In terms of support, RunPod’s team is known to actively help users optimize their workloads for the platform (for example, guidance on multi-GPU training, environment setup, etc.). Hyperstack likely offers support too, but given their positioning, their support might be more ticket-based and oriented to infrastructure issues rather than ML questions.

Security for both platforms involves proper handling of your data and isolation of your workload. RunPod’s containers ensure that even in the Community Cloud (with third-party hosts), your environment is secure and isolated from others. Hyperstack’s approach uses full VM isolation, which is also a strong isolation mechanism. Both should adequately protect your model and data from other customers. RunPod’s additional compliance certifications might be crucial if your project is for a company with strict infosec requirements.

Summary of reliability: RunPod provides a reliable, well-supported environment proven by an extensive user community – an important factor for peace of mind during long fine-tuning runs. Hyperstack is making strides, but as of now, it remains slightly unproven at the same scale and lacks some of the third-party validations of security and reliability that RunPod offers.

Conclusion

For ML engineers and developers looking to fine-tune AI models, RunPod emerges as a more versatile and fine-tuning-friendly platform in this comparison. Hyperstack offers impressive hardware and can be cost-effective for certain scenarios, but RunPod’s combination of greater GPU variety, faster startup, flexible billing, easy data persistence, and user-centric design gives it a notable advantage for most use cases.

Hyperstack is a strong contender if your priority is sustained raw performance on high-end GPUs (especially in Europe) with potentially lower costs through reservations. However, it comes with a bit more operational overhead and less flexibility in how you use those resources. In contrast, RunPod caters to fast-moving AI projects – the kind where you might spin up resources on a whim, fine-tune a model for a few hours, save results, and spin everything down – without feeling like you’ve wasted time or money. It’s built ground-up for AI workloads, which shows in features like per-second billing, network volumes for checkpoints, and quick-swap GPU instances.

In practical terms, if you need to fine-tune a large language model or a computer vision model with minimal hassle, RunPod will let you get started in minutes, using the exact environment you want, and ensure that you can iterate quickly. The costs will scale precisely with your needs, and you won’t be locked in. As one industry publication noted, RunPod combines ease-of-use with competitive pricing, making advanced GPU computing accessible to individual developers and teams alike .

Hyperstack is a promising platform and might be worth watching as it grows, especially if you operate in Europe and value the local presence and green energy aspect. But for now, RunPod provides a more complete package for fine-tuning workflows – from the moment you provision a GPU to the moment you deploy or save your fine-tuned model.

Internal Links (RunPod Resources):

If you’re ready to accelerate your fine-tuning tasks, you can sign up for RunPod and launch a GPU in seconds. Be sure to check out the RunPod pricing page to see the cost for different GPU types, and explore the platform’s features (like pods, serverless, and volumes) through the RunPod docs and tutorials. With RunPod’s platform, fine-tuning your next AI model can be faster, easier, and more cost-effective.

FAQ

Q: Which platform is better for fine-tuning large language models (LLMs) like GPT-3 or Llama?

A: For most scenarios, RunPod is better suited for fine-tuning LLMs. It offers GPUs with high VRAM (A100 80GB, H100 80GB) on-demand and supports multi-GPU scaling if needed. Crucially, RunPod’s persistent storage and flexible billing let you handle the long training times of LLMs without worrying about losing progress or paying for idle time. Hyperstack can also fine-tune LLMs (it has the same high-end GPUs and NVLink for multi-GPU), but you may need to reserve capacity to get the best price, and you’ll have to manage your own environment on the VM. RunPod provides a smoother experience out-of-the-box for large model fine-tuning, plus it has a variety of GPU options in case you want to start with a smaller model on a cheaper GPU and then scale up to larger ones.

Q: How do RunPod and Hyperstack compare in pricing for a one-month fine-tuning project?

A: If you plan to use a GPU continuously for a month, Hyperstack’s reserved pricing could offer a lower hourly rate (since you can lock in a discount for long-term use). For example, an H100 on Hyperstack reserved might cost significantly less per hour than RunPod’s on-demand rate. However, the catch is you pay for the entire reserved period regardless of actual usage. In contrast, RunPod on-demand pricing might be slightly higher per hour, but you only pay for the hours (or seconds) you actually run the GPU. If your project has any downtime or you don’t end up using the GPU 24/7, RunPod could end up cheaper overall. Additionally, RunPod doesn’t charge for data transfers, which might save money if you need to download pretrained model weights or export results. In summary: Hyperstack can be cheaper for 24/7 utilization with reservations, while RunPod is usually more cost-effective for on-demand and irregular usage common in fine-tuning workflows.

Q: Do both RunPod and Hyperstack support custom software environments (libraries, frameworks, etc.) for training?

A: Yes, both platforms allow you to run custom software, but the approach differs. RunPod supports custom Docker containers – you can use any environment you want by either selecting a pre-built image or providing your own. This makes it easy to include specific libraries (e.g., a certain Transformers version or OpenCV, etc.) in your training environment. Hyperstack gives you full root access to your VM, so you can install anything you need on the VM (it’s essentially like configuring your own server). In practice, setting up a deep learning environment (with Python libraries, CUDA, cuDNN, frameworks) is faster on RunPod because many images are ready to go. On Hyperstack, you might start from a base Ubuntu image and manually install everything, or create a custom image beforehand. Both platforms ultimately let you run the tools you need for fine-tuning, but RunPod gets you there with less effort thanks to containerization.

Q: Is Hyperstack a good choice at all for fine-tuning, and when might I consider it over RunPod?

A: Hyperstack can be a good choice in certain situations. If you are in Europe and require your data to stay in Europe for compliance, Hyperstack’s EU-based infrastructure is a plus. If you know you need a high-end GPU (or multiple) continuously for a long period, the reserved pricing on Hyperstack could save you money. Also, if you specifically want to leverage NVLink with multiple H100s in one machine for maximum training throughput, Hyperstack provides that setup. You might consider Hyperstack for very large, production-grade training jobs with relatively predictable resource needs. On the other hand, for most fine-tuning use cases – which tend to be shorter-term, experimental, or require flexibility – RunPod is often the more convenient and agile choice. RunPod doesn’t require any long-term commitment, has a broader feature set tailored to AI dev workflows, and has a proven track record with the fine-tuning community. In short, Hyperstack is a strong specialized platform, but RunPod is usually better for iterative development and fine-tuning agility.