CAP / 01
Custom Inference Architecture
Sub-millisecond serving paths, dynamic batching, speculative decoding, and kernel-level tuning for exact workload profiles.
Enterprise AI systems engineering
Engineered for execution.
We architect, deploy, and optimize high-throughput AI infrastructure and low-latency inference pipelines—custom-engineered for mission-critical enterprise workloads.
GPU UTIL
87%
P99 LATENCY
11.2ms
THROUGHPUT
18,240 tok/s
GPU FABRIC / CP-NORTH-01
18 NODES / RDMA ACTIVE
18 NODES / RDMA ACTIVE
Fabric synchronized
Capabilities / 04
Systems, not subscriptions.
ComputePlane enters where standard platforms stop: at the physical, network, runtime, and model-serving layers that determine production AI performance.
CAP / 02
Dedicated GPU Cluster Deployment
Bare-metal orchestration and sovereign on-premise or hybrid fabrics engineered around locality, bandwidth, and failure domains.
CAP / 03
Compute & Cost Optimization
Maximize useful tokens per watt and per dollar through memory-aware scheduling, quantization, and topology optimization.
CAP / 04
24/7 Managed Compute Operations
Deep telemetry, cluster health, incident response, and capacity forecasting operated by infrastructure engineers.
Architecture / Assessment
Model the operating envelope.
Translate workload intent into a concrete compute topology. Adjust the profile to generate a directional architecture for review.
Assessment / liveModel topology
01 / Workload profile
02 / Daily token volume
48BTOKENS / DAY
03 / Deployment target
Provisional architecture
48 GPU
dedicated fabric
Accelerators
48 × H100/H200
Serving pools
4 isolated
Runtime
vLLM + TensorRT-LLM
Envelope
< 12ms p99
01Topology-aware workload placement
02Redundant high-speed data plane
03Bare-Metal security boundary
0424/7 telemetry integration
Directional system model — validated during audit
Evidence / Benchmarks
Useful throughput over theoretical peak.
We optimize the entire serving path, exposing the constraints hidden by headline accelerator specifications.
420Base
680Tuned
1120CP
Throughput / tok·s⁻¹
Engineered serving stack
Baseline420
vLLM tuned680
ComputePlane1120
Representative lab envelope. Actual performance depends on model, precision, context, and hardware topology.
Deployment window / open