What TITAN is built for
13 load profiles for which this series is the right platform — each with the reason why.
Oversized for single services. Only makes sense from several dozen concurrent workloads.
- HPC
- MPI jobs across multiple nodes with up to 25 Gbit/s connectivity. Scaling is determined by the NUMA binding of the processes — whether a process runs on the socket whose memory it uses — not the pure core count.
- Simulation
- CFD, FEM and Monte Carlo runs that rely on the aggregated throughput of all twelve memory channels and must run stably for days.
- Build farms
- Hundreds of parallel compiler jobs on one node. Turin cores with high boost also shorten the serial linker runs at the end.
- Virtualisation
- Proxmox VE or KVM with SR-IOV and PCIe passthrough on your own bare metal — without hypervisor overhead and without external guests on the same cores.
- Hypervisor clusters
- From three nodes with vRack fabric. Live migration and heartbeat run via the private network, not via the public uplink.
- Rendering
- Blender, V-Ray or Nuke nodes. CPU passes run without being tied to GPU driver versions and can be kept open indefinitely.
- Licence-bound Intel workloads
- Software whose certification or licence explicitly requires an Intel platform — on a socket with 86 cores.
- AI inference on CPU (AMX)
- Intel Advanced Matrix Extensions accelerate INT8 and BF16 matrix operations. Smaller models thus run economically without a GPU.
- Databases with large memory
- 256 GB registered ECC ex works: the buffer of large databases fits into memory instead of reloading from the drive.
- Container hyperscale
- Hundreds of threads per node support four-digit pod numbers. These are full Zen 5 cores of the Turin generation with up to 4.5 GHz boost, not a density variant with lowered clock speeds.
- CI fleet
- One node replaces an entire runner fleet. Containers start from local NVMe, making the queue in the morning significantly shorter.
- Private Cloud
- OpenStack or Proxmox cluster where one chassis replaces what would otherwise occupy half a rack — including the associated cabling.
- Big data clusters
- HDFS, Iceberg or Trino nodes with up to eight NVMe drives. Intermediate results remain in memory instead of travelling across the network.
