Full-Stack AI Compute Infrastructure
From raw GPU provisioning to production inference serving — the Tensormesh platform covers every layer of the ML infrastructure stack, purpose-built for teams running at scale.
GPU Cluster Orchestration
Provision 1 to 1,000 GPUs in Minutes
Tensormesh manages a heterogeneous fleet of H100 SXM5, A100 80GB, and V100 nodes across multiple availability zones. Our scheduler places your job on the optimal hardware for your workload — automatically.
- H100 SXM5 and PCIe variants, A100 80GB, V100 32GB nodes
- NVLink & NVSwitch fabric for intra-node GPU communication
- InfiniBand HDR (200 Gb/s) for inter-node communication
- NUMA-aware process pinning for minimal CPU bottlenecks
- Auto-restart on node failure with checkpoint recovery
- Spot-equivalent pricing with preemption protection options
Distributed Training
Train Any Model at Any Scale
Our distributed training layer supports the full spectrum of parallelism strategies required to train modern large language models and vision transformers — without manually tuning communication primitives.
- Data parallelism (DDP) with gradient bucketing and overlap
- Tensor parallelism (Megatron-style) for layer-level sharding
- Pipeline parallelism with schedule-aware micro-batching
- Expert parallelism for Mixture-of-Experts architectures
- Automatic mixed precision (FP16/BF16) with loss scaling
- Native support for PyTorch FSDP, JAX pmap/pjit, DeepSpeed
Inference Optimization
Production LLM Serving at Low Latency
Deploying a trained model to production involves a different set of challenges than training. Tensormesh's inference engine is optimized for throughput, latency, and cost efficiency across diverse LLM architectures.
- Continuous batching for dynamic request queuing
- PagedAttention KV cache management (vLLM-compatible)
- INT8 and INT4 weight-only quantization with minimal accuracy loss
- Speculative decoding for sub-100ms first-token latency
- Multi-LoRA serving: serve hundreds of adapters per GPU
- OpenAI-compatible REST API for drop-in replacement
Everything in One Platform
Network Fabric
400 Gb/s Ethernet backbone with RoCE v2 for RDMA-accelerated all-reduce operations. Latency-optimized topology for large-scale distributed jobs.
Elastic Scaling
Scale jobs up mid-run with elastic training APIs. Add or remove nodes without stopping the job — ideal for dynamic research experiments.
Unified API
One REST + Python SDK for job submission, checkpoint management, resource monitoring, and serving. CI/CD-ready with GitHub Actions integration.
Security & Compliance
SOC 2 Type II certified. Dedicated VPC deployment, customer-managed encryption keys, audit logging, and role-based access control for enterprise teams.
Multi-Cloud & Hybrid
Deploy on Tensormesh-managed infrastructure, your own cloud VPC, or a hybrid mix. On-premise bare-metal GPU server integration available for regulated industries.
Model Registry
Store, version, and deploy model checkpoints with a built-in registry. Tag experiments, compare training runs, and promote checkpoints to serving with a single API call.
Start Your First GPU Job in Under 10 Minutes
Our onboarding team will walk you through setting up your first cluster, submitting a training job, and connecting your existing ML tooling to the Tensormesh API.
Schedule Demo