Technical Whitepaper & Architecture

GPU-Accelerated Architecture

How AKWebs leverages the NVIDIA AI Computing Stack and AWS GPU cloud infrastructure to achieve sub-second deterministic multi-agent inference.

Empirical Validation

Performance Benchmarks vs Standard Baselines

Time-To-First-Token (TTFT)

142 ms (Standard PyTorch)
14.2 ms (AK TensorRT-LLM)
10x Faster

Throughput (70B Model on H100)

48 tokens / sec
192 tokens / sec (NIM Microservice)
4.0x Throughput

Vector Search Latency (10M Embeddings)

110 ms (Standard CPU Index)
16 ms (CUDA Accelerated Index)
6.8x Speedup

Multi-Agent Consensus Overhead

450 ms (REST polling)
18 ms (Asynchronous Shared Memory)
25x Lower Overhead
Inference Layer

NVIDIA NIM (Inference Microservices)

We encapsulate foundation models (LLaMA-3, Mistral, Gemma, NV-Embed) into production-grade OCI containers using NVIDIA NIM. This unlocks instant horizontal auto-scaling, dynamic batching, and standard OpenAI-compatible API schemas without cold-start latency penalties.

  • Standardized microservice orchestration across Kubernetes (EKS / GKE)
  • Continuous in-flight batching maximizing GPU SM utilization to > 90%
  • Zero cold-start pre-warmed container pools
# NIM Deployment Manifest
apiVersion: apps/v1
kind: Deployment
metadata: nim-llama3-70b-service
spec:
containers:
- image: nvcr.io/nim/meta/llama3-70b-instruct
resources:
limits: { nvidia.com/gpu: 4 }
# TensorRT Engine Builder
trtllm-build \
--checkpoint_dir ./model_weights \
--gemm_plugin float16 \
--tokens_per_block 64 \
--kv_cache_type paged \
--output_dir ./engine_outputs
Compiler & Kernel Optimization

TensorRT-LLM & Custom CUDA Graph Execution

By compiling neural network graphs into hardware-specific TensorRT execution plans, we eliminate Python interpreter overhead. We leverage FP8 quantization with custom calibration, Paged KV-Cache allocation, and FlashAttention-2 kernels to dramatically compress memory footprint.

  • FP8 / INT4 weight quantization preserving 99.7% model perplexity
  • Paged KV-Caching eliminating VRAM memory fragmentation
  • CUDA Graph captures reducing CPU launch overhead to near-zero
Deterministic Safety

NeMo Guardrails & Enterprise Security

Enterprise AI requires strict adherence to legal, ethical, and proprietary boundaries. We implement programmable Colang guardrails that execute pre- and post-inference validation to prevent jailbreaks, data exfiltration, and non-deterministic behavior.

  • Zero-Trust input sanitization against adversarial prompt injections
  • Self-checking factuality verification comparing output to ground-truth RAG docs
  • SOC2 Type II & GDPR compliant zero-retention data policies
# Colang Guardrail Flow
define flow check sensitive topics
user ask about confidential financials
bot check rbac authorization
if not authorized
bot refuse politely with audit event
# AWS Cluster Topology
- Instance: EC2 p5.48xlarge (8x H100 SXM5)
- Networking: Elastic Fabric Adapter (3.2 Tbps)
- Storage: Amazon FSx for Lustre (NVMe)
- Orchestration: Amazon EKS + Karpenter Autoscaler
Cloud Infrastructure

AWS GPU Cloud Cluster Architecture

AKWebs platforms deploy seamlessly on AWS infrastructure leveraging Amazon EC2 H100/A100 instances, high-throughput Elastic Fabric Adapters (EFA) for distributed tensor parallelism, and Amazon EKS for auto-healing agent clusters.

  • Elastic GPU auto-scaling via Karpenter and Kubernetes horizontal pod autoscaling
  • Encrypted in-transit and at-rest data handling via AWS KMS and isolated VPCs
  • Optimized for AWS Activate $100k startup cloud infrastructure scaling

Schedule an Architectural Review

Discuss your enterprise GPU compute requirements with Anil Kumar Jangid and our systems engineering team.

Enterprise Technical Intake

Project & Architecture Brief

Connect directly with Anil Kumar Jangid and our autonomous systems engineering squad in Kota, Rajasthan.