Not every AI workload needs a GPU. CPU inference for smaller language models, embedding generation, batch classification, and NLP preprocessing runs efficiently on high-core, high-memory EPYC servers — often more cost-effectively than renting GPU cloud instances. Our AMD EPYC 9000-series bare-metal line gives you up to 256 CPU cores and 1 TB of DDR5 ECC memory at a flat monthly rate.
Memory bandwidth is the key constraint for LLM inference on CPU. AMD EPYC 9000-series processors have some of the highest memory bandwidth available in server silicon — up to 460 GB/s on dual-socket configurations. This directly translates to faster token generation throughput when running quantised models via llama.cpp, Ollama, or vLLM in CPU mode.
IPMI out-of-band access means you can reboot, reinstall the OS, and manage your server remotely without opening a ticket. Same-day provisioning with the OS of your choice means your ML environment is running within hours. No shared tenancy, no noisy neighbours — every FLOP on the system belongs to your workload.
Why Voxa for AI & Machine Learning Inference
Up to 1 TB DDR5 ECC
Large model weights fit in memory. DDR5 at 4800 MHz provides the memory bandwidth that CPU inference throughput depends on.
256 cores on EPYC 9754
Parallelise inference, preprocessing, and serving across hundreds of threads. EPYC 9000-series scores exceptionally in multi-threaded ML benchmarks.
NVMe scratch storage
10–24 NVMe slots for fast model loading, dataset staging, and checkpoint storage. Models load from NVMe in seconds, not minutes.
IPMI remote management
Full out-of-band access for OS reinstall, remote console, and power control. Your infrastructure, fully managed by you.