AI Infrastructure Efficiency

HBMGuard

Reduce GPU Energy.
Then Control the Context.

Infrastructure-aware optimization for private AI — from GPU, HBM and power to agents, context and tokens.

Request an HBMGuard Pilot Explore ContextGuard

20.47% mean GPU-energy reduction · Replicated A100 KV-cache qualification · <1% throughput impact · workload-specific results

GPU / HBM / Power↑HBMGuard↑Context / Tokens / Loops↑ContextGuard

Built for private, on-prem and security-sensitive AI environments.

hbmguard_agent · node-a100-01 · live

[2026-06-18 08:41:03] HBMGuard v0.9 — DCGM Profiling API connected

[08:41:05] SM_CLOCK: 1410 MHz DRAM_ACTIVE: 94% PWR: 398W ⚠ Memory Wall detected

[08:41:05] ECO_CTRL: applying workload-aware power policy

[08:41:14] HBM telemetry: threshold crossed → controlled response

[08:42:31] ✓ Node healthy. Workload restored. Engineers paged: 0. █

Primary capability · HBMGuard

HBMGuard — GPU Infrastructure Efficiency

AI infrastructure efficiency begins below the model layer. HBMGuard observes and optimizes the physical behavior of GPU workloads using hardware telemetry, workload measurements and controlled GPU power policies.

HBMGuard focuses on

GPU energy consumption

GPU utilization

HBM activity

Memory-bound behavior

Performance per watt

GPU time per workload

Thermal behavior

Workload SLA impact

Primary KPIEnergy / Successful Workload ↓Secondary KPIPerformance per Watt ↑

Validated result · workload-specific

Evidence-backed GPU efficiency

20.47%mean GPU-energy reductionReplicated A100 KV-cache qualification

<1%throughput impactmeasured within the qualification workload

Results are workload-specific and must be independently qualified for each customer environment. No universal savings are implied.

Secondary capability · ContextGuard

Control the Context. Protect the Task.

ContextGuard is a deterministic enforcement gateway between AI agents and an OpenAI-compatible model endpoint. It provides exact model-token accounting, atomic per-task budgets, safe context compaction, repeated-request loop protection, hard input-budget enforcement with no silent truncation, and tamper-evident HMAC audit verification.

Agent / context efficiency and safety

ContextGuard keeps AI agents within explicit token, loop, and audit boundaries before requests reach the model. It reduces unnecessary context while preserving required information and gives operators a verifiable decision trail.

The objective is not simply fewer tokens.
Resources per Successful Task.

ContextGuard enforces

Exact input tokens

Atomic task budgets

Context growth

Loop detection

Tool calls

Retries

Audit verification

Controlled A100 demonstration — workload-specific● evidence panel

MODELQwen2.5-7B-Instruct

HARDWARENVIDIA A100-SXM4-40GB

ORIGINAL INPUT1,781 tokens

FORWARDED INPUT216 tokens

INPUT SAVED1,565 tokens

CONTROLLED-DEMO REDUCTION87.87%

MODEL FAILURES0

AUDITHMAC verified

TOKENIZERexact via vLLM /tokenize

Demonstration result from one controlled workload; it is not a universal savings guarantee. Customer environments require independent qualification.

ContextGuard flow, enforced

COUNT

Exact model tokenizer

BUDGET

Reserve task budget atomically

COMPACT

Remove only permitted old context

ENFORCE

Block loops and hard-limit violations, then record a verifiable audit event

Full-stack architecture

APPLICATION / AGENTSAI applications and agent workflows

↓

CONTEXTGUARD / TOKENS / LOOPS / AUDITDeterministic gateway · budgets · compaction · HMAC audit

↓

INFERENCE / MODEL / KV CACHEOpenAI-compatible model endpoint

↓

HBMGUARD / GPU / HBM / POWERPhysical workload efficiency and telemetry

Deployment model

Built for Private AI Infrastructure

BZICHIMEM is designed for environments where sensitive application data should remain inside the customer's controlled environment.

Metadata-only assessment mode

Prompts, documents, RAG content and model responses can remain inside the customer's environment.

Operational telemetry

Token counts · context size · agent-step counts · tool-call counts · retry counts · latency · cache statistics · GPU utilization · HBM telemetry · power consumption.

Designed to integrate with

NVIDIA GPUsvLLMNVIDIA NIMTensorRT-LLMTritonRayKubernetesSlurm

Start with GPU Efficiency.
Expand to Agent Efficiency.

HBMGuard GPU Efficiency Pilot

BASELINE

GPU / HBM / power telemetry

Workload profiling

Controlled optimization

A/B validation

OUTPUT

Energy / workloadGPU time / workloadPerformance-per-wattSLA impact

ContextGuard Agent Efficiency Assessment

BASELINE

Exact token accounting

Atomic budget enforcement

Context compaction

Loop and audit verification