Anavec — Heterogeneous Rack-as-a-System AI Infrastructure

CHAPTER 01 Problem Statements

An AI workload is a pipeline — a workflow graph of stages: ingest → stage → compute → post → egress, across CPU, GPU, memory, and network. Inference, fine-tune, MoE serving, agentic chains: every one of them runs this way, with branches and loops where the workflow demands it. Not as a single GPU job. However,

CUDA kernels NCCL collectives Triton inference graphs Dynamo prefill–decode Kubernetes pod placement

Each layer handles its own slice. None of them handle the pipeline - across your physical and virtual racks, your tenants, your GPU generations, your data paths.

Today Your platform team — manually. Static partitions, fixed CPU:GPU ratios, redeploys when SLOs break.

With Anavec The runtime — per stage, per tenant, per tick. Workload-aware placement across rack and data paths.

The Anavec View

One rack. One pipeline. One governed system.

01 · Runtime

The center of authority

A pipeline runs across physical boundaries. The rack has no center of authority.

AnaROS provides one — rack-level visibility, traceability, and workload control. The pipeline becomes the unit of work; the rack becomes the unit of accountability. No more black boxes. No more piecemeal observability.

02 · Rack Platform

The heterogeneous substrate

A pipeline demands stage-level collaboration. Piecemeal hardware cannot assure the delivery.

AnaRack is engineered for it — heterogeneous by design. CPU controls, GPU serves, standard Ethernet between them. Multiple GPU generations and accelerator classes run on the same rack with rack-wide workload placement — so enterprises mix what they have with what they're adding, instead of refreshing the way hyperscalers do.

No single system has authority over the full pipeline across the rack — without end-to-end workload control, p99 stability collapses before data ever reaches the GPU.

CHAPTER 02 What we believe

If the unit of work is AI pipeline, then the unit of infrastructure has to be the rack.

The next decade of enterprise AI will be won by infrastructure that scales CPU, GPU, memory, and storage independently — and is governed end-to-end as a single pipeline, not box-by-box.

We are not ready to share the full architecture in public. The atlas below tells the story under NDA. If your team is wrestling with capital efficiency, lifecycle mismatch, or p99 stability in production AI workloads, we'd like to compare notes.

Independent scalingof CPU, GPU, memory, storage
End-to-end SLOsacross the full pipeline path
Heterogeneous acceleratorsMoE experts and agentic stages on the right GPU class
Multi-vendoraccelerator and storage options
2–3× longeruseful lifecycle per rack
Pipeline X-Rayas the operator's primary surface
Trust as a platformevery decision auditable

CHAPTER 03 Why ANAVEC

But today's rack is not operationalized as one.

For enterprise and neocloud teams, AI is data and model serving — ingestion, transport, staging, inference, post-processing, return, across network, CPU, GPU, memory, and storage — end-to-end as one workload. The unit of work is the pipeline, not only the GPU itself. Today's racks ship as a puzzle of bundled hardware; they don't deliver rack-level visibility, traceability, or workload control — and meanwhile, AI work spreads quietly across on-prem racks, GPUaaS, and external LLM APIs that CIO and CISO cannot even see. The four gaps below all stem from this — and each is addressed by a different page in the atlas above.

PROBLEM · 01

Capital inefficiency — paying twice, by stage and by lifecycle.

An AI workload isn't one shape. MoE experts, agentic routing, RAG retrieval, pre- and post-processing — every stage needs a different accelerator class. Meanwhile, CPU, GPU, memory, and storage refresh on different lifecycles. Single-class racks and bundled SKUs force enterprises to overbuy by stage and by year — then under-utilize the result. AnaRack fixes both — workload-aware placement per stage, drawer-level refresh per lifecycle.

PROBLEM · 02

Architectural rigidity — multi-vendor integration paid in months.

Today's AI rack is many vendors at once — CPUs, GPUs, accelerators, NICs, BMCs, fabric switches, storage, firmware. The traditional path is rigid: qualify every combination, ship one tested release, freeze it. Every new component, every drawer swap, every patch reopens the test matrix. AnaRack takes a different approach — a software-defined integration layer above the device OS, so new hardware slots into a contract the rack already speaks.

PROBLEM · 03

No rack-level pipeline authority — lack of cohesive experience and assurance.

An AI workload spans ingest, staging, compute, post-processing, persistence — across CPU, GPU, fabric, and storage. Every layer has its own dashboard; none own the pipeline as a whole. No rack-level visibility, traceability, or workload control across the full path — p99 collapses long before data reaches the GPU, and the symptom you see is rarely the root cause. AnaROS fixes this — one runtime, end-to-end, root-cause aware.

PROBLEM · 04

AI sprawl — invisible to IT, security, compliance and governance.

Employees spin up AI workflows, fine-tune models, and call LLM providers across on-prem racks, GPUaaS, and external APIs. CIO and CISO cannot see who is running what — whether existing security tooling still applies, what data leaves the perimeter, or whether the cost split between local, GPUaaS, and provider LLMs is rational. AnaROS fixes this — one operating contract across the continuum.

01 · One pipeline, not point jobs

An AI workload is a continuous pipeline, not a set of isolated steps. Visibility starts when you treat it that way — p99 collapses long before data reaches the GPU when you don't. Pipeline becomes the unit of work; the rack becomes the unit of accountability.

02 · One runtime, not a rebuild

AnaROS attaches to your existing stack — same K8s, same CUDA, same models, same teams. No application rewrites. No new platform layer for your team to learn. Visibility, traceability, and workload control arrive without a redesign.

03 · One rack, many generations

Multiple GPU generations on the same rack. Workload-aware placement across mixed accelerator classes — heterogeneous by design, built for enterprise budgets that can't refresh fleets the way hyperscalers do. Mix what you have with what you're adding.

Deep visibility, traceability, and workload control across every boundary.

From L1 silicon to L4 workflow. From physical rack to logical fabric. One operating contract, end-to-end.

RACK · RESOURCE MAP

heterogeneous · MoE-aware · multi-generation GPUs · data and execution pipelines through one rack

Anavec rack — data pipeline flow and execution pipeline flow, 400 Gbps, latency < 1ms

PIPELINE · STACK VIEW

workflow → pipeline → fabric → physical · the same rack, observed

L4 · APPLICATION WORKFLOW

Agentic chains, MoE routing, RAG — as a controlled workflow graph.

Workload intent expressed as steps, branches, loops, and SLOs — agentic chains rarely run linearly. The surface CIOs and product owners reason about.

L3 · PIPELINE X-RAY

Stage-level health, POFC (Pipeline over Fabric correlation), SLO scoring.

Ingest → stage → compute → egress observed end-to-end. Verdicts, evidence, audit — every decision shows its work.

L2 · LOGICAL FABRIC

Compute · flow · storage as a rack-wide resource map.

Heterogeneous GPUs and accelerators surfaced as one logical map — below the application layer. Your pipelines see standard CUDA, standard interfaces. AnaROS routes each stage to the right silicon.

L1 · PHYSICAL RACK

Capability drawers, governed.

The rack on the left — heterogeneous accelerators, network fabric, NVMe — surfaced through POFC, ready for placement.

AnaROS visibility · traceability · workload control · AI-native three-tier compute · debate · learn · Memory Management staging fabric

FIGURE · ONE RACK · ONE PIPELINE · ONE GOVERNED SYSTEM

Same hardware. Two views. Governed end-to-end by AnaROS.

CHAPTER 06 The AI workload continuum

The pipeline is the new unit of work. Govern it wherever it lands.

Hybrid and multi-cloud are old news for traditional IT — apps and services have moved across clouds for over a decade. What's new for AI is that the unit of work is no longer a service call. It's a pipeline — a workflow graph with many stages, many venues, and one outcome. A single agentic chain might ingest on-prem, retrieve context in one cloud, run sensitive inference on sovereign GPUs, call an external LLM provider, and persist back to another cloud — all in one request. Each leg has its own dashboard, its own audit trail, its own placement decision. No existing toolchain governs the pipeline as a unit of work — until now.

Anavec workload continuum — on-prem racks bridged to hybrid and multi-cloud (AWS, Azure, GCP) and external LLM providers, with bidirectional data and control flows governed end-to-end by AnaROS.

FIGURE · ONE PIPELINE · MANY VENUES · ONE OPERATING CONTRACT

The unit of accountability is the pipeline — wherever it runs.

01 · VISIBILITY

The pipeline, observed end-to-end.

Every stage, every venue — on-prem, hyperscaler cloud, neocloud, external LLM API. POFC traces the whole pipeline, not just the rack and not just the request. Where did each stage live? What did it cost? How long did it wait?

02 · TRACEABILITY

Every stage. Every venue. Provenance recorded.

Every token, every embedding, every API call — provenance attached. Which model ran where, with what data, under what tenant. Audit-ready across cloud, vendor, and sovereignty boundaries — not just within one stack.

03 · SECURITY & COMPLIANCE

Sovereignty as pipeline policy.

Data residency enforced at the pipeline-stage level, not the cluster level. Sensitive stages stay on-prem; commodity stages burst to cloud — and the line between them is governance, not glue code. CISO sees the boundary, signs off.

04 · CONTROL

Per-stage placement, workload-aware.

Per-stage decisions: cost, latency, data residency, model quality. Cap an LLM provider at $X/day. Pin sensitive RAG to sovereign GPUs. Burst to GPUaaS when on-prem saturates. Routing is workload-aware, not vendor-locked.

Who we want to talk to

Design partners, not pilot tourists.

We have capacity for a small number of design partners through 2026. We are most useful to teams that match one of these profiles.

CIO · CTO You own the AI investment thesis. You're tired of approving 2-year refresh cycles to get one GPU generation. You want capital efficiency expressed at the rack, not the SKU.

CISO You own the risk surface. You need governance, isolation, and visibility into AI workloads that today look like black boxes. Compliance starts with knowing where the data path actually went.

Chief Architect You own the reference design. You're picking your AI rack standard now and the choice will outlast three accelerator generations. You want optionality and clean layering.

Infrastructure lead You run the day-2 reality. You operate under SLOs and budget — and the operational chaos of one-off AI pods is no longer acceptable. You need pipeline-aware tooling.

Platform · SRE You own pipeline reliability. You know p99 fails in the data path, not in the model. You want end-to-end pipeline observability with stage-level routing and chargeback.

Procurement · Supply You own multi-vendor leverage. You don't want a single-vendor rack that locks CPU, GPU, memory, and storage refresh into one PO line. You want supply-chain options preserved.

Become a design partner

Pilot a rack profile, not a slide deck.

Bring us a real workflow. We'll instrument it, profile it across the four layers of the stack, and propose a rack profile that scales independently along your real bottleneck.

We respond personally to every inquiry within two business days. We don't sell yet — we listen, and we shortlist.

Every atlas page is shared under mutual NDA. Most pilots land in 6–8 weeks.

hello@anavec.ai

Deep visibility, traceability, and control for AI workloads across your racks — on-prem, hybrid, and multi-cloud.