Private LLM hosting vs Google Cloud TPU
We rent DGX Sparks, so weigh that against everything below, and note upfront that this comparison is not like-for-like in the way a GPU-to-GPU page is. A TPU (Tensor Processing Unit) is a chip Google designed itself, a genuinely different architecture from an NVIDIA GPU, available only through Google Cloud. A DGX Spark is an NVIDIA GPU machine running the standard CUDA software stack. Neither one is simply a faster or slower version of the other; they're different chip families with different software ecosystems, and the honest way to compare them is on the practical tradeoff between broad NVIDIA/CUDA tooling compatibility on hardware you can SSH into directly, versus Google's managed TPU platform.
Side by side
| Google Cloud TPU | Dedicated DGX Spark (GPUwerk) | |
|---|---|---|
| What it is | Google's own tensor processing chip, available exclusively as a managed Google Cloud resource, not hardware you can buy or rent elsewhere. | A single-purpose NVIDIA GPU host: one dedicated DGX Spark, rented by the hour, running standard CUDA. |
| Chip architecture | Google's custom tensor processing architecture, distinct from NVIDIA's GPU architecture and not CUDA-compatible. | NVIDIA DGX Spark: Grace Blackwell, 128GB unified memory, the same NVIDIA GPU architecture running CUDA. |
| Software ecosystem | Historically strongest with JAX and TensorFlow; PyTorch support exists through PyTorch/XLA but is a secondary path compared to TPU-native frameworks. | The mainstream NVIDIA/CUDA stack most open-source LLM tooling targets first: PyTorch, vLLM, llama.cpp, and the rest, no adaptation layer needed. |
| How you access it | A managed Google Cloud resource provisioned through Google's tooling; not a machine you SSH into as root the way a bare GPU instance works. | Root SSH access to one physical machine GPUwerk operates directly in Prague. |
| Region | Whichever Google Cloud regions carry TPU capacity, which varies by TPU generation; check Google Cloud's current documentation for EU availability by generation. | EU-Central (Prague), exclusively. One location, no region selection to get wrong. |
| Who can see your data | Governed by Google Cloud's data processing terms; review those directly for the specifics of TPU workload handling. | Nobody at GPUwerk. Our DPA states GPUwerk "hosts the machine but does not access, read, copy, index, or analyse the content of the controller's instance." |
| Pricing model | Per chip-hour, varying by TPU generation and commitment terms, published on Google Cloud's own pricing page and subject to change; check it directly for a current number. | Per hour, billed per minute: $0.79/hour on-demand, $0.59/hour for a customer-requested stop that holds your reservation. One rate regardless of workload, per pricing. |
| Contracts and DPA | Google Cloud's standard terms and data processing terms; enterprise agreements typical at volume. | A standard GDPR Article 28 DPA published free at /legal/dpa, no negotiation required. Sub-processor list at /legal/sub-processors states none are engaged for instance workloads. |
The worked cost example
An always-on internal inference endpoint for a month on a Spark: 730 hours × $0.79/hour = $576.70, before tax, fixed regardless of which open-weight model you run or how many requests it serves. Per pricing.
Google Cloud's TPU pricing is per chip-hour and varies by TPU generation and commitment terms, and Google Cloud's own pricing page is the place to get a number you can trust for the generation you'd actually use, since a straight dollar comparison across two different chip architectures depends heavily on how well your specific model and workload map to TPU hardware in the first place. That mapping is itself the harder question: a model and inference stack built and tuned around CUDA doesn't automatically run as efficiently on a TPU without engineering work, so any price comparison should account for that adaptation cost, not just the headline per-hour rate.
Migration path
This is the part where the two diverge most. A model server built on vLLM and standard PyTorch runs on a Spark with no changes, since a Spark is a standard NVIDIA CUDA machine. Moving that same stack to a TPU generally means adapting it to JAX or TensorFlow, or to PyTorch/XLA if you want to keep PyTorch, plus reworking anything that assumed CUDA-specific behavior. Put LiteLLM in front of either endpoint once it's serving, since LiteLLM only cares about the OpenAI-compatible HTTP interface, not what's underneath it. What doesn't move without engineering effort: any inference or training code written directly against CUDA kernels or CUDA-specific libraries.
When Google Cloud TPU is the right choice
- Your workload is already built on JAX or TensorFlow, or you're training at a scale where TPU pods make sense.
- You want Google managing the infrastructure layer as part of a broader Google Cloud footprint.
- Matching Google's own published TPU benchmarks for specific model families matters more than broad tooling compatibility.
When a dedicated Spark is the right choice
- Your model server and tooling are already built on the CUDA and PyTorch ecosystem, which is where most open-source LLM tooling starts.
- You want direct root access to a physical machine rather than a managed cloud resource.
- EU data residency and a standard GDPR DPA are requirements, not nice-to-haves.
FAQ
Is a TPU the same thing as a GPU?
No. A TPU (Tensor Processing Unit) is a chip Google designed itself, purpose-built for tensor operations, and it is architecturally different from an NVIDIA GPU. TPUs are available only through Google Cloud, not as hardware you can buy or rent from anyone else. A DGX Spark is an NVIDIA GPU machine running standard CUDA software, so this comparison is between two different chip families and ecosystems, not two versions of the same thing.
Can I run the same software on a TPU and a DGX Spark?
Not without changes. TPUs are historically strongest with JAX and TensorFlow, Google's own frameworks, though PyTorch support exists via PyTorch/XLA. A DGX Spark runs standard NVIDIA CUDA software: PyTorch, vLLM, llama.cpp, and the rest of the mainstream open-source inference stack, without any TPU-specific adaptation layer. If your stack is already built on CUDA and PyTorch, that's a smaller lift on a Spark than adapting it to run well on TPU.
Is a DGX Spark cheaper than a Google Cloud TPU?
It depends on the TPU generation and configuration, since Google Cloud prices TPUs per chip-hour on its own pricing page and that varies by generation and commitment terms. A dedicated Spark costs $0.79/hour flat, one number regardless of workload. Check Google Cloud's current TPU pricing for the generation you'd actually need before comparing, keeping in mind you're comparing different chip architectures, not just different price tags.
Why choose a DGX Spark over a Google Cloud TPU?
Mainly ecosystem compatibility and direct machine access. A Spark runs the mainstream NVIDIA CUDA and PyTorch stack that most open-weight LLM tooling targets first, and you get root SSH access to one physical machine GPUwerk operates in the EU. A TPU is a managed Google Cloud resource, strong for JAX and TensorFlow workloads and Google's own training pipelines, but it isn't hardware you SSH into directly the way a Spark is, and it isn't the primary target for most open-source inference tooling. If broad compatibility with existing GPU-based tooling and a dedicated machine you control matter more than Google's managed TPU platform, the Spark fits better.
GPUwerk's own figures on this page ($0.79/hour, $0.59/hour, $576.70/month) come from our published pricing. Google Cloud's TPU pricing was not fetched or reproduced here, since per-chip-hour rates vary by generation and change over time; check Google Cloud's pricing page directly for a specific figure.