Why NVIDIA H100 SXM Matters for Modern AI Workloads
Search Neysa
Real-time inference tuned to your workload.
Deployed on dedicated infrastructure, sized to your latency targets, and traffic; billed at predictable monthly spend.
Built for Production
Dedicated GPUs, custom kernels, hybrid pricing, and engine selection per workload. Everything you’d build yourself if you had the team to build it.
Full Control
Single-tenant endpoints with dedicated SLA.
Scalable
Reserved baseline plus on-demand scale-up.
Cost Efficient
Hybrid pricing against a committed spend pool.
Fast
Custom CUDA kernels, vLLM or SGLang engine selection.
Access Leading Open-Source and Open-Weight Models
Run any open-source model with a HuggingFace checkpoint or bring your own fine-tuned weights, custom containers, or Helm charts.
- Deploy within seconds.
- OpenAI-compatible endpoints
- Hybrid pricing with scale-to-zero
Proven Results
Consistent, high-performance inference – more tokens per second, lower latency, and optimized throughput even under heavy workloads.
Gemma 4 | 27b
Output throughput: 224 tokens per second | Time to first token: 132 ms
Endpoint configuration:
- Context length: 256k
- GPU used: 1x H100
- Parallel queries: concurrency of 10 requests
- Quantization: fp8
Qwen3.6 | 30B-A3B-Instruct
Output throughput: 412 tokens per second | Time to first token: 96 ms
Endpoint configuration:
- Context length: 256k
- GPU used: 1X H100
- Parallel queries: concurrency of 10 requests
- Quantization: fp8
GLM-5.1
Output throughput: 184 tokens per second | Time to first token: 318 ms
Endpoint configuration:
- Context length: 200k
- GPU used: 4X H100
- Parallel queries: concurrency of 10 requests at a time
- Quantization: fp8
Built for Full Control and Customization
Get dedicated single-tenant inference endpoints running on vLLM, deployed on reserved monthly GPUs for guaranteed availability and security and ability to customize every aspect of your endpoint
- Per-workload kernel and engine selection (vLLM, SGLang, custom CUDA)
- Forward-deployed engineering for benchmarking, fine-tuning, and RLHF
- Deploy in your VPC, on-prem, air-gapped edge, or multi-cloud
- Workspace-based access management for AI/ML teams
Customize with the Power of Top NVIDIA and AMD GPUs
NVIDIA configurations from L4 through Blackwell B300, plus AMD Instinct for teams that want a non-NVIDIA path. As new silicon ships, endpoints move across generations without re-architecting – and one cluster handles LLM, speech, and vision workloads side by side.
- Comprehensive GPU configuration options
- Enterprise-grade reliability and uptime
- Optimized for AI and inference workloads
Security and Privacy-First Design
Security and compliance are built into every layer of Velocis – from physical infrastructure to model deployment – and audited against ISO 27001:2022, SOC 2, CSA STAR Level 2, ISO 27017, and ISO 27018. Neysa is also a CSA Trusted Cloud Provider.
Cloud & Infrastructure Security
Strict compliance and security controls ensure your data remains protected. Includes RBAC, audit logs, policy enforcement, encryption, and zero-trust access.
Model Security
Your AI models are secured by default, enabling safe deployment of AI/ML projects across cloud and on-premises environments.
soc
ISO 27001:2022
ISO 27017:2015
ISO 27018:2019
Explore real-time inference for your workload
Send us your test set, your latency targets, and your monthly spend. We’ll come back with a configuration that hit your SLAs.