AMD EPYC 9455P48 cores · 128 GB DDR5 ECC · 2 × 1.92 TB NVMe
48 Zen 5 cores with full-width AVX-512 and 12 DDR5 channels: the value choice for CPU inference and CI.
AI & CPU compute
Serious compute, no GPU queue
Not every AI or compute job needs a GPU. Quantised language models, embeddings, CI pipelines, compilation and rendering run well on many modern cores with wide vector units — at a fraction of the price, with no waiting list.
- Up to 64cores per socket
- AVX-512full width on Zen 5
- AMXon Xeon 6
- $255from, per month
What this workload needsWorkload profile
- Wide vector unitsZen 5 executes AVX-512 at full 512-bit width, and Xeon 6 adds AMX matrix engines for BF16 and INT8 inference.
- Memory bandwidthToken generation is bound by memory bandwidth. EPYC Turin feeds 12 DDR5 channels per socket.
- Many parallel jobsCI runners, compile farms and render nodes scale almost linearly with cores. 48 or 64 of them change a build queue.
- Predictable costA flat monthly price instead of per-minute billing: run the queue flat out, day and night.
Recommended
The servers we would deploy for it
Chosen by the engineers who size these workloads every week. Every one of them is in stock.
Intel Xeon 6761P64 cores · 256 GB DDR5 ECC · 2 × 1.92 TB NVMe
64 Granite Rapids cores with AMX, the best CPU path for BF16/INT8 inference frameworks that support it.
AMD EPYC 9555P64 cores · 256 GB DDR5 ECC · 2 × 3.84 TB NVMe
64 Zen 5 cores and 256 GB for the biggest render and build queues.
Setting it up
Advice from our engineers
The first hour on a new server decides how the next year goes.
Where to host it
Compute is location-agnostic: pick the cheapest stock near your data. Frankfurt and Amsterdam carry the largest EPYC and Xeon inventory.
Compare latency from your users- Use quantised models (Q4–Q8) with llama.cpp or ONNX Runtime; match threads to physical cores, not threads.
- On Xeon 6, enable AMX in frameworks that support it (PyTorch with oneDNN, OpenVINO).
- Give CI runners their own tmpfs for build directories to spare the NVMe.
- Be honest about scale: training large models still belongs on GPUs — we will tell you when that is the case.
FAQ
Questions about AI & CPU compute
Still have a question?
Ask the engineers who run the servers — median first reply under 15 minutes, 24/7.
Contact usDo you offer GPU servers?
Not at the moment — our range is CPU-focused. For many inference and batch workloads, 48 to 64 modern cores are the better-value option.
How big a model can I run on CPU?
With 256 GB of memory, quantised models of several tens of billions of parameters fit comfortably; generation speed then depends on memory bandwidth.
Can I run CI for many repositories?
Yes. Runners in containers or VMs, each with CPU limits, keep dozens of pipelines busy on one server.
Other workloads
All workloads