Make AI work on hardware you can actually own and operate.

We help teams determine whether AI inference, bounded training experiments, and agentic workloads can run privately on CPU systems, then fix the Linux, kernel, numerical, or embedded constraints that stop them from working well.

CPU AI and high-performance Linux engineering

Run useful AI privately on hardware your organization can own.

Who we help

Most teams should begin with PyTorch, llama.cpp, vLLM, SGLang, NeMo, or another established runtime. Work with us when installing the framework does not answer the engineering question: whether a model fits private CPU hardware, why a workload is wrong or unexpectedly slow, how to support an embedded target, or which architecture produces acceptable latency, privacy, power, and total cost. We investigate the boundary beneath the framework rather than rebuilding mature software without a reason.

Model And Hardware Qualification

Common starting point 01 / 1-2 weeks

For a team deciding whether one model, runtime, and hardware combination meets production requirements. You provide the model, representative workload, expected users, latency and accuracy targets, privacy constraints, current or proposed hardware, and budget. We deliver a dated memory and model-fit budget, measured baseline where access permits, concurrency and deployment analysis, hardware and TCO options, risks, and a go, no-go, or test-next decision. Completion means every recommendation is tied to a stated assumption or measurement and the next investment decision is clear.

First-Divergence Investigation

Common starting point 02 / 1-3 weeks

For a model that works in one runtime or hardware path but produces corrupted, unstable, or measurably different output in another. You provide both runnable paths, fixed inputs, expected precision, and the failing example. We trace preprocessing, layout, RoPE, attention, quantization, reductions, cache state, projections, and generated tokens to locate the first meaningful disagreement. Completion means the discrepancy is corrected or reduced to a named, measured cause with a deterministic reproducer and correctness gate.

Kernel And Backend Bring-Up

Common starting point 03 / 2-4 weeks

For a missing or broken model architecture, precision, CPU instruction path, operator, or embedded backend. You provide source, the authoritative reference, target hardware, representative inputs, and acceptance requirements. We implement or repair the bounded path, establish numerical tests, profile the result, and document supported limits. Completion means the target route runs reproducibly, passes its agreed correctness gate, and has a measured performance baseline without claiming support beyond the tested scope.

Inference, bounded training experiments, and agentic workloads on Linux CPU nodes.

Technical capability / CPU systems and deployment

Model and memory fit, concurrency, Linux configuration, thread placement, model storage, startup, recovery, monitoring, privacy, and total cost. Multi-node performance remains a research roadmap item until measured.

Make the software use the compute, memory, and network you paid for.

Technical capability / kernel, compiler and numerical engineering

C kernels, generated code, AVX2, VNNI, AVX-512, AMX BF16, packing, tiling, cache reuse, threading, NUMA, and Linux networking. Work is grounded in correctness gates, before-and-after measurements, VTune, Advisor, perf, rooflines, and hardware counters.

Move transformer and numerical intelligence onto embedded Linux.

Technical capability / embedded AI and robotics

Cross-compilation, runtime integration, model sizing, state-estimation mathematics, numerical validation, profiling, and repeatable device tests where memory, power, latency, sensors, and toolchains matter. CKE has run on TI TDA4VM; broader embedded support remains evidence-gated.

A decision, working artifact, and evidence your team can inspect.

What you receive

Depending on scope, the result may include a feasibility and TCO model, reproducible benchmark, profiler report, numerical correctness gate, source patch, optimized kernel, configured Linux node, evaluation environment, embedded prototype, or prioritized implementation plan. Assumptions, hardware, software revisions, test inputs, acceptance criteria, and known limits are recorded explicitly.

The methods are visible before you contact us.

Public evidence

C-Kernel-Engine exposes model circuits, numerical contracts, generated C, kernel work, profiler artifacts, and Linux deployment experiments. DroneMath connects equations to state-estimation and robotics code. ShivasNotes documents the mathematics, failures, benchmarks, and engineering decisions. Internal infrastructure developed over fourteen years keeps experiments and evidence organized rather than scattered across unexplained folders.

Optimize the system people can afford to own and operate.

Cost is an engineering constraint

Peak throughput is not the same as useful value. We evaluate the workload against response time, concurrent users, memory capacity, utilization, privacy, power, cooling, operations, replacement, and cloud or API alternatives. Hardware recommendations and total-cost models are dated, assumption-driven, and tied to the actual workload rather than a generic CPU-versus-GPU claim.

Fourteen years of building the tools behind the work.

Experience across the complete path

Antsand has evolved since 2014 into the internal system used to organize data, experiments, technical content, and publication. C-Kernel-Engine turns transformer mathematics into generated C and CPU kernels. DroneMath and Antshiv Robotics connect physics and estimation equations to robotics software. Linux CPU nodes, Intel profiling, Xeon research, and TI TDA4VM deployment keep the work connected to real machines. The complete engineering record is published through ShivasNotes, GitHub, and YouTube.

First principles before frameworks. Evidence before claims.

What makes our work different

We begin with the real behavior the system must produce. Mathematics defines correctness. Physics defines limits on compute, memory movement, power, heat, sensing, and communication. Source code shows how the work is actually performed. Profilers and experiments reveal which constraint matters now. Only then do we choose or change frameworks, kernels, hardware, and deployment architecture. This depth lets us move from model equations to Linux, CPU instructions, networks, embedded devices, and total cost without treating any one tool as the answer.

The person responsible for the work is visible.

Principal engineer

Engagements are led by Anthony Shivakumar. Antsand provides the internal systems used to organize project data, evidence, review, and publication, while C-Kernel-Engine, ShivasNotes, DroneMath, and the relevant repositories provide inspectable engineering artifacts. Specialist collaborators may be introduced when a scope requires them, but responsibility, assumptions, review points, and acceptance criteria remain explicit.

Qwen3-VL attention: finding the execution contract beneath the formula.

Current investigation / numerical correctness

A current CKE investigation traces model divergence through named tensor boundaries and compares attention dtype, storage, tiling, and reduction semantics against an authoritative runtime. The final case will include hardware, model, context, threads, both commits, benchmark command, before-and-after result, date, and known limitations. Material improvement will not be described as full parity unless the end-to-end gate closes.

CPU prefill: equivalent settings and inspectable measurements.

Benchmark in preparation / performance

This case will compare CKE with an established runtime using the same model, quantization, context, thread count, CPU, power policy, and measurement window. It will include commit identifiers, warm-up policy, raw outputs, profiler evidence, date, and limitations. This remains intentionally unclaimed until the controlled benchmark artifact is ready.

Work that can begin with bounded inputs and acceptance criteria.

Capability maturity / available now

Model and hardware feasibility; CPU kernel profiling; first-divergence and numerical analysis; Linux CPU investigation; and embedded feasibility. Availability means the investigation method and deliverables exist, not that every model, processor, or device is already supported.

Evidence lanes currently being hardened.

Capability maturity / being built

A public benchmark dashboard, broader model qualification, a Whisper and audio lane, and native Xeon BF16 and AMX evidence. These are active engineering targets, not services represented as complete today.

Directions that require measured closure before promotion.

Capability maturity / research roadmap

Distributed CPU execution, multi-node scaling, and broader embedded deployment remain research roadmap items. They become demonstrated capabilities only after reproducible hardware, communication, synchronization, correctness, and performance evidence is published.

Use an established product or runtime when it already solves the problem.

Not a fit

We are not the right fit for generic chatbot integration without a systems constraint, frontier-scale GPU pretraining, unbounded research without measurable acceptance criteria, or performance claims that cannot be reproduced. We will not replace a mature framework when it already satisfies the workload. The useful engagement begins when model output is wrong, a target is unsupported, performance is unexpectedly poor, or correctness, privacy, hardware behavior, and total cost cannot be validated through the default abstraction.

Tell us what is failing, slow, expensive, or impossible today.

Send the model or system, available hardware, required outcome, what has already been tried, and any real deadline. We will explain what we would investigate first, whether we can help, and what a useful initial engagement would produce. If the problem is outside our useful scope, we will say so directly.

ShivasNotes

Engineering notes, A.I. workflow, drones, systems programming, and the messy process of building in public.

Explore

Connect