Private inference that survives production.
Installing an inference server is easy. Engineering the serving layer around real concurrency, latency, memory, topology, reliability, security, and operations is the work TensorWard Runtime is built for.
ON-PREM · PRIVATE CLOUD · HYBRID · AIR-GAPPED — WHERE YOU CONTROL THE DATA PATH
DISCUSS PRIVATE INFERENCE →GPU architecture
Single GPU, multi-GPU, and multi-node topology selected against model fit, memory, interconnect, and workload behavior.
Serving runtime
vLLM, llama.cpp, SGLang, TensorRT-LLM, or another path chosen because it fits—not because it is fashionable.
Memory + context
Weights, KV cache, context length, concurrency, batching, and headroom treated as one capacity problem.
Performance
TTFT, prompt processing, decode, concurrency, speculative decoding, routing, and saturation measured on representative load.
Production controls
Authentication, network boundaries, deployment automation, health behavior, rollback, observability, and failure handling.
Capacity + operations
Utilization, growth, upgrade strategy, runbooks, and ownership defined before the platform becomes somebody else's emergency.
One tok/s number is not a capacity model.
Time to first token
What users experience before useful generation begins.
Prompt + generation rate
Reported separately and in the context of batch/concurrency.
VRAM + memory envelope
Weights, caches, framework overhead, and safety margin.
Concurrency + saturation
How the system behaves as real users arrive.
Workload evaluation
Performance changes remain attached to the quality result.
Reliability + observability
Errors, recovery, utilization, alerting, and operating ownership.
A platform your team can operate.
Typical production work includes the inference topology, serving configuration, deployment automation, access design, performance envelope, observability, capacity model, documentation, and runbooks.
TensorWard Runtime is an engineering service, not a mandatory licensed runtime. Technology selection is workload-driven and vendor-neutral, and the goal is an environment the client controls.
TensorWard does not resell GPUs or cloud capacity; procurement guidance is independent.