tenantplaneBook a demo Llama 3.1 8B Instruct
Answers and drafting inside an existing application.
Acceptance: Time to first token, generation speed, concurrency, and application task quality.
Model or workload reference ↗Your cloud.
Or your own hardware.
Choose the workload and where it runs. We handle the serving layer, test it, and hand it over.
Build your deployment briefWhat should run.
What should run.
Where it belongs.
Choose a starting point. No account or confidential details needed.
Your starting point
Private GPU service in your cloud
A serving container on approved GPU compute, a private network endpoint, and your existing application.
- Example model
- Llama 3.1 8B Instruct
- Implementation
- $12,500–$20,000
- Readiness
- Optional when scope and prerequisites are already established
- Check the existing GPU, runtime, access, and network configuration.
- Pin the model/runtime, record dependencies, and define access controls.
- Deploy serving and connect one existing application’s synchronous call path.
- Test the agreed workload and hand over versioned deployment files and operating instructions.
Infrastructure and provider charges are separate. Maintenance is optional; no 24/7 coverage is included.
Representative model, not a fixed hardware recommendation. Your selected model and license are reviewed during scoping.
Start with the workloadReal models.
Real models.
Useful application paths.
Examples to scope around—not a prescribed model or hardware configuration.
Qwen3-Coder-30B-A3B-Instruct
Code generation and repository-aware application calls.
Acceptance: Representative coding tasks, context limits, response latency, and tool permissions.
Model or workload reference ↗Llama 3.1 8B Instruct
Classify text and return structured fields to your workflow.
Acceptance: Field correctness, schema validity, document length, throughput, and rejected inputs.
Model or workload reference ↗BAAI/bge-m3
Generate private vectors for your existing search pipeline.
Acceptance: Embedding contract, input limits, retrieval quality, throughput, and access boundaries.
Model or workload reference ↗A private endpoint in your account.
A serving container on GPU compute, connected to your application over the approved network path. We scope identity, credentials, storage, logging, and external dependencies with your infrastructure owner.
Need capacity? We can compare suitable compute options. You approve the provider and pay it directly.
Your GPUs. Your facility.
Run on a Linux GPU server at your premises or in a colocation facility. Model memory, drivers, storage, networking, and remote access are checked before installation. Hardware procurement and facility work are priced separately.
A deployment without public downloads.
Restricted or disconnected environments need approved artifact transfer, a digest inventory, local dependencies, isolation checks, and an offline update procedure. These controls belong to the custom implementation scope. If you add model release security, the scope also covers transferring and verifying signed evidence in your registry.
Explore model release security →
Match serving to demand.
Start with a single GPU server when it meets the workload. Add replicas for capacity, or evaluate tensor parallelism when a model needs multiple GPUs. Multi-node operation, high availability, and durable job queues are separately scoped.
Measure latency and throughput on the intended request mix before choosing a topology. See inference optimization.
A handover your operator can use.
Your team receives versioned deployment files, test records, and recovery instructions. Name the owner for monitoring, patching, and incidents; optional maintenance covers an agreed allowance of work.
Model rights follow the original license. There is no required Tenantplane control plane or management subscription.
See it on your clusterLet your teams share GPUs
Book a demo Let your teams share GPUs
without the help desk.
Book a 30-minute walkthrough of your GPUs, teams and workloads.