MEMOPT is the open-source universal memory fabric for AI infrastructure, built from first principles by Sophisticates — a deep tech venture company working across AI, Quantum Computing, Robotics, and Physics. MEMOPT is Sophisticates' flagship product, open-sourced under Apache-2.0 so the broader AI infrastructure community can build on, audit, and extend the hardest part of GPU serving: memory.

⚠️ ALPHA — TEST ON YOUR OWN GPU BEFORE PRODUCTION

GPU validation has NOT been performed on this release. The 1016-passing test baseline is Mac / no-CUDA only. The 2 @gpu tests

• 18 cuda-named tests are SKIPPED / DESELECTED on the release host.

If you are deploying to real GPUs you MUST:

1. Run the full regression on your target GPU (A100 / H100 / L40S / ROCm) with the steps in PRODUCTION_READINESS.md.
2. Soak-test under representative traffic for ≥ 24 hours before declaring the deployment "production-ready."
3. Set MEMOPT_SIGNING_KEY to a high-entropy secret (NOT the default).

Do not assume "tests pass" means "works on my hardware." See the four blockers in Status — production readiness below.

A GPU memory profiling, and serving platform that turns GPU clusters into a unified memory fabric. Python control plane, C++17 data plane, optional CUDA kernels.

1016 tests pass | 7 C++ test suites | 0 failures (Mac / no CUDA)

Status — production readiness

This is an alpha release (v1.3.0a1). The library is OSS-licensed and the test baseline is green on Mac, but the following must complete before any "production-ready" claim:

1. GPU rig validation — the 2 @gpu tests and 18 cuda-named tests are SKIPPED / DESELECTED on Mac. They must run green on an A100 / H100 host before tagging a non-alpha release.
2. CI must pass on its first push — .github/workflows/ci.yml defines a Linux + Mac × Python 3.10/3.11/3.12 matrix; nothing has run there yet.
3. Live-workload soak — recommend ≥ 24h soak under representative traffic before declaring "production-ready."
4. Phase B MEMOPT_USE_ORCHESTRATOR=1 — Layer 2 (orchestrator) ships in observation-only mode per docs/orchestrator_v1_design.md DECISION 7. Eviction-driving Layer 2 ships in v1.4.0, not here.

Track these in the v1.3.0 entry of CHANGELOG.md.

What It Does

memopt v1.3.0 ships two infrastructure layers plus eight product pillars.

Infrastructure layers

Pillars

Pillars 4, 6, 7 wire to Layer 1/2 through memopt/integrations/ (attach_ledger_to_substrate, FinOpsPoller, assemble_production_receipt).

Quick Start

# Install from PyPI:
pip install memopt-engine

# After installation, the Python import path is `memopt`
# (distribution-name vs import-name — same convention as
# `pip install scikit-learn` then `import sklearn`):
python -c "import memopt; print(memopt.__version__)"

For development from source:

git clone https://github.com/basnetlachu/memopt.git
cd memopt
pip install -e ".[dev,daemon,api]"

Profile a Model

memopt profile --model gpt2 --batch-size 8

Serve with All Pillars Active

memopt-serve --model meta-llama/Llama-2-7b --port 8001

The serving engine automatically:

• Deduplicates KV cache across requests (Pillar 2)
• Synthesizes fused kernels on HBM stalls (Pillar 3)
• Measures energy per token via NVML (Pillar 4)
• Monitors hardware drift (Pillar 6)

OpenAI-Compatible API

curl http://localhost:8001/v1/completions \
  -H "Content-Type: application/json" \
  -d '{"model": "llama2", "prompt": "Hello", "max_tokens": 50}'

Check Savings

curl http://localhost:8001/report/found-capacity

Architecture

Python (control plane)          C++ (data plane)
─────────────────────          ──────────────────
vmm/                           csrc/core/
  page_table.py (shim) ──────── _memopt_core.so
  oracle.py (shim)                (64-shard page table,
  tier_manager.py                  striped-lock oracle,
  prefetch_engine.py               16-shard block directory)
  federation.py

serving/                       csrc/hooks/
  kernel_hooks.py (shim) ────── _memopt_hooks.so
  auto_optimizer.py               (FNV-1a keys, atomic
  server.py                        counters, lock-free dispatch)
  paged_attention.py (shim) ── _memopt_paged.so
                                  (block pool, CUDA gather)

cluster/                       csrc/cuda_backend/
  gkd_store.py ────────────────  _memopt_cuda.so
  transport.py (shim) ────────── memopt-transport (sidecar)
  prefix_index.py (shim) ─────  _memopt_simd.so
  block_directory.py (shim)      (AVX-512 prefix match)

Every C++ module has a Python fallback. The system runs correctly without any C++ extensions built.

Repository Structure

memopt/
├── memopt/              Python package (control plane)
│   ├── vmm/             Infinite Context VMM (Pillar 1)
│   ├── cluster/         GKD + GUM + transport (Pillars 2, 5)
│   ├── kernels/         Self-synthesizing kernels (Pillar 3)
│   ├── observability/   Energy ledger + certificates (Pillar 4)
│   ├── serving/         OpenAI-compatible HTTP server
│   ├── profiler/        Hardware counter profiling
│   ├── control_plane/   Cluster management (FastAPI)
│   ├── daemon/          Background GPU monitor
│   └── api/             REST API
├── csrc/                C++17 data plane (38 source files)
│   ├── core/            PageTable + Oracle + BlockDirectory
│   ├── hooks/           Kernel dispatch table
│   ├── paged/           Block pool + CUDA gather kernel
│   ├── cuda_backend/    Stream pool + NVMe I/O + GDS
│   ├── transport/       RDMA sidecar daemon
│   └── simd/            AVX-512 prefix matching
├── tests/cpp/           GoogleTest suites (7 files)
├── scripts/             Audit and tooling
│   └── audit_wiring.py  Runtime wiring verification
└── docs/
    ├── architecture.md  Complete technical reference
    └── rdma_deployment.md  InfiniBand deployment guide

Configuration

All behavior is configurable via environment variables. Key ones:

See docs/architecture.md Section 25 for the complete list.

Building C++ Extensions

pip install pybind11 scikit-build-core cmake ninja

# Build all extensions
cd csrc && mkdir build && cd build
cmake .. -DMEMOPT_ENABLE_TESTS=ON
make -j$(nproc)

# Run C++ tests
ctest --output-on-failure

# Optional: CUDA, RDMA, AVX-512
cmake .. -DMEMOPT_ENABLE_RDMA=ON -DMEMOPT_ENABLE_AVX512=ON

Running Tests

# Python tests (no C++ required)
pytest --tb=short -q

# Wiring audit (verifies C++ integration)
python scripts/audit_wiring.py

Requirements

• Python 3.10+
• PyTorch 2.0+
• Optional: CUDA 12.4+ (GPU kernels), pynvml (power measurement), redis-py (cluster mode)

Documentation

• Architecture Reference — complete technical specification
• RDMA Deployment Guide — InfiniBand setup and troubleshooting

Memory substrate

memopt v1 ships Layer 1 of the memory substrate: a tenant-isolated, stream-aware allocator with pluggable backends (CUDA VMM, ROCm/HIP stub, Level Zero stub, CXL/NUMA, CPU). The public API is memopt.alloc / free / context / stats / observe plus MemoryHandle. See docs/substrate_v1_user_guide.md for usage and docs/substrate_v1_design.md for the spec.

Orchestrator (Layer 2)

memopt v1.2 adds Layer 2: a tenant-aware observation/decision layer that sits on top of the substrate. In v1.0 (Phase A) it observes the substrate's event stream and exposes a public Policy protocol; it does NOT drive eviction yet (that ships behind MEMOPT_USE_ORCHESTRATOR=1 in Phase B). The public API is memopt.orchestrator.start / stop / stats / register_policy plus memopt.peek_handle. See docs/orchestrator_v1_user_guide.md for usage and docs/orchestrator_v1_design.md for the spec.

About MEMOPT

MEMOPT (pronounced memm-opt) is a universal memory fabric for AI infrastructure. It solves one of the hardest problems in modern GPU serving: memory — KV-cache OOM under long contexts, redundant KV recomputation across requests, HBM stalls from un-fused kernels, multi-tier paging across HBM/DRAM/NVMe, cross-node block sharing, and auditable energy/cost accountability. memopt unifies all of these behind one API, with two pinned infrastructure layers (substrate + orchestrator) and eight product pillars layered on top.

It's released under Apache-2.0 so any AI infrastructure team can read the source, audit the security guarantees, fork it, contribute back, or run it in production without licensing friction.

About Sophisticates

memopt is built and open-sourced by Sophisticates (pronounced so-phis-ti-cates), a deep tech venture company founded by Lachu Man Basnet. Sophisticates builds companies from first principles across AI, Quantum Computing, Robotics, and Physics. MEMOPT is Sophisticates' flagship product in the AI infrastructure vertical.

• Website: sophisticatesai.com
• Maintainer: Lachu Man Basnet (lachu.basnet@sophisticatesai.com)
• Issues / discussions: https://github.com/basnetlachu/memopt/issues
• Security disclosures: see CONTRIBUTING.md § Security

If your team uses memopt in production, we'd love to hear about it — open a discussion on GitHub or reach out via sophisticatesai.com.

License

Apache License 2.0. See LICENSE for the full text and NOTICE for third-party attributions. A pinned dependency license audit lives at docs/license_audit.md.