SYSTEMS // APOLEMIA RUNTIME v2.1

High-Throughput
Inference Engine.

The computational engine beneath Knolink Cloud. High-throughput distributed nodes, specialized weight sparsity, and edge execution layers built to run intelligence in production.

KV Cache Reuse
94.2%
activation retention

Eliminating compute redundancy across multi-turn reasoning loops.

Throughput Efficiency
2.4x
tokens / watt improvement

Custom kernel compilation minimizing dynamic power draw.

Time to First Token
< 14ms
TTFT latency

Fast distributed lookahead scheduling across cluster meshes.

01 — EXECUTION LAYERS

Subsystem Architecture

DISTRIBUTED TOPOLOGY & ENCLAVES

[01]LAYER_01

High-Throughput Inference Nodes

Cluster configurations engineered for production model activations. Apolemia partitions tensor operations dynamically across interconnected GPU/NPU fabrics without transport bottlenecks.

SPECIFICATION ATTRIBUTES
Multi-GPU tensor parallelism (TP=2/4/8)
Decoupled prefill and decode execution queues
Automated FlashAttention-3 kernel optimizations
[02]LAYER_02

Edge Compute & Micro-Clusters

Optimized runtime configurations for localized setups. Apolemia compiles compressed INT8/INT4 quantization profiles for edge appliances and private workstation hosts.

SPECIFICATION ATTRIBUTES
Native Apple Silicon & discrete NPU driver bindings
Zero-downtime offline fallback when network degrades
Local vector index pairing and embedded cache sync
[03]LAYER_03

Security & Memory Enclaves

Enterprise-grade model isolation. Execute sensitive weights inside hardware-encrypted memory enclaves (Intel SGX / AMD SEV) with zero telemetry extraction.

SPECIFICATION ATTRIBUTES
Hardware-level enclave encryption
Cryptographic weight signature verification
Full VPC network containment
DECLARATIVE CLUSTER SPECIFICATION

Deployment Profiles

High-throughput Node Orchestration

Partitions weight matrices across interconnected GPU clusters. Custom CUDA kernels optimize FlashAttention-3 execution lines to bypass transport overhead.

// apolemia-cluster.yaml
apiVersion: apolemia.knolink.io/v2
kind: InferenceCluster
metadata:
  name: kalman-sparse-14b-high-throughput
spec:
  tensorParallelism: TP_4
  kvCacheMaxGb: 64
  scheduler: apolemia-dynamic-lookahead
  sparsityThreshold: 0.12
PRODUCTION DEPLOYMENT

Deploy Apolemia on your infrastructure

Configure Apolemia inference runtimes across your cloud instances, private hardware clusters, or edge appliances.