1,000+ GPUs
one cluster scales from a single node, no re-contracting
Multi-provider
GPU supply reserved to guarantee your timeline
InfiniBand
multi-node fabric tuned for near-linear MoE scaling
24/7 SRE
fault detection, node expansion, incident response
Trusted by teams training LLMs and foundation models
Built around the large language model training run
Most of a foundation-model program is infrastructure, not modeling, and a stalled cluster is the most expensive thing a lab owns.
Secure the capacity, then guarantee it
A run needs hundreds of the right GPUs on a specific week, not whenever a single vendor frees them up. Sourcing across multiple providers means the capacity you plan for is the capacity you get, reserved ahead to guarantee it.
When the science moves, the compute plan moves with it. You commit to the run you're doing, not to a single-vendor contract you have to grow out of.
Multi-provider
sourcing: capacity without single-vendor lock-in
200–1,000+ GPUs
provisioned for a run, not a fixed quota
Reserve-to-guarantee
capacity lined up to your training timeline
The full cluster stack, built and run for you
Topology, scheduling, storage, and the software stack down to NCCL: the undifferentiated heavy lifting is ours, not your researchers'. You get a cluster that's ready to train, not a pile of VMs to assemble.
It's the difference between a GPU cloud that hands you machines and walks away, and a partner that sits between the infrastructure supplier and your team and owns the platform underneath the run.
Slurm or K8s
scheduling stood up and tuned for you
PB-scale
shared storage wired into the cluster
Full stack
topology, drivers, NCCL: built and operated
Scale it, tune it, keep it up
Throughput tuning (FSDP and HSDP, multi-dimensional parallelism, NVLink and InfiniBand tuned end to end) pushes a run toward near-linear scaling with zero hardware changes. The same cluster grows from a single node to 1,000+ GPUs without re-contracting.
Then SRE keeps it moving: automated fault detection, zero-downtime node expansion, and 24/7 incident response, so a weeks-long run doesn't lose days to a bad node.
Near-linear
scaling with FSDP/HSDP and multi-dimensional parallelism
Zero-downtime
node expansion while the run keeps training
24/7
SRE: fault detection and incident response
The lab that ships a frontier model isn't the one with the most GPUs. It's the one whose cluster never sits idle waiting on someone to fix it.
A workload the cluster can't hide from
Training a foundation model at scale stresses every layer between the GPU and your code, not just the accelerator.
MoE changes the bottleneck
Mixture-of-Experts routes every token to a handful of experts spread across nodes, so all-to-all traffic explodes. A bandwidth-constrained inter-node fabric caps scaling long before the GPUs are the limit.
RL post-training, not just pretraining
Modern foundation models are finished with reinforcement learning: generation, reward scoring, and policy updates in one loop. It's a spikier load than a steady pretraining pass, and it shares the same cluster.
Where the cluster gets in the way
The blockers large-model teams describe are rarely about raw GPU speed.
Securing capacity on your timeline
A run needs hundreds of the right GPUs on the week the science is ready, not a quarter later. Single-vendor allocations rarely line up with the roadmap, and every week of waiting is lost progress.
Multi-node scaling that leaks throughput
Getting from one node to a hundred is where efficiency quietly disappears. A bandwidth-constrained inter-node fabric, or untuned NCCL, turns a 2× GPU count into far less than 2× throughput, and MoE feels it first.
Managing the undifferentiated cluster stack
Slurm or Kubernetes scheduling, PB-scale shared storage, NCCL and topology tuning: none of it trains your model, but all of it has to be right. It's weeks of platform work that pulls researchers off the science.
Keeping a large cluster healthy
At scale, node failures are routine, not exceptional. Without automated detection and clean expansion, every failed node and every capacity bump becomes downtime the whole run pays for.
How VESSL Cloud fits
A full-stack compute partner between the infrastructure supplier and your team, not another bare VM.
Multi-provider GPU capacity
Source hundreds to thousands of GPUs across providers so the capacity you plan for lands on your timeline, reserved ahead to guarantee it, with no single-vendor lock-in.
Full-stack cluster build
Topology design, Slurm or Kubernetes scheduling, PB-scale shared storage, and the full software stack, stood up as a cluster that's ready to train, not a box of parts.
Training-throughput optimization
FSDP and HSDP, multi-dimensional parallelism, and NVLink + InfiniBand tuning push large and MoE runs toward near-linear scaling: more GPUs, proportionally more throughput, with zero hardware changes.
Ongoing SRE for the run
Automated fault detection, zero-downtime node expansion, and 24/7 incident response keep a weeks-long run healthy, so a single bad node doesn't cost days.
Scale without re-contracting
The same cluster grows from a single node to 1,000+ GPUs as the program does: no new contract, no migration, no restart of your stack.
SOC 2 Type II
Runs on infrastructure held to SOC 2 Type II, with dedicated clusters and workspace isolation for sensitive foundation-model work.
NLP & foundation models on VESSL Cloud: FAQ
How large a training cluster can VESSL Cloud run?
A single cluster scales from one node to 1,000+ GPUs and grows as your program does, without re-contracting. Capacity is sourced across multiple providers and reserved ahead so hundreds of the right GPUs line up with your training timeline.
Can you handle Mixture-of-Experts and other large-model architectures?
Yes. MoE, dense transformers, and 100B–300B+ parameter runs all lean on the inter-node fabric, so clusters are wired with InfiniBand and tuned end to end (NCCL, topology, and multi-dimensional parallelism) for near-linear scaling as you add nodes.
Do we have to manage Slurm, storage, and NCCL ourselves?
No. VESSL Cloud builds and operates the full stack (Slurm or Kubernetes scheduling, PB-scale shared storage, drivers, and NCCL tuning) so your researchers get a cluster that's ready to train instead of a platform project.
What happens when a node fails during a multi-week run?
Ongoing SRE covers it: automated fault detection, zero-downtime node expansion, and 24/7 incident response keep the run moving, so a failed node or a capacity bump doesn't turn into days of downtime.
Where AI models
get their GPUs.
- Start in minutes
- Scale to multi-node clusters
- Capacity reserved to your timeline
- Dedicated support