Machine learning, charged by a single pole.
We translate the physics of magnetic monopoles into training algorithms that converge faster, generalize further, and stay stable at scale. Production-grade intelligence for teams that ship.
One foundation. Every workload.
The Monopole platform unifies training, evaluation, and deployment behind a single control plane. Define a model once and run it across CPU, GPU, and custom accelerators with deterministic build artifacts and reproducible gradients.
- Unified runtime across cloud, on-prem, and edge
- Reproducible training graphs with cryptographic lineage
- Native support for sparse, dense, and hybrid models
Training that converges where others stall.
Our monopole-inspired optimizer treats parameters as charges in a single-pole field, eliminating the symmetry traps that cripple traditional gradient descent. Models reach their loss floor in fewer steps and stay stable across long horizons.
- Up to 4.1x faster convergence on transformer workloads
- Stable training to 100B+ parameters without warmup tricks
- Drop-in compatibility with PyTorch and JAX
Physics-grade rigor. Engineering velocity.
Our research team publishes openly and ships internally. Every algorithm in the platform traces back to a peer-reviewed result, and every result ships with the reference implementation, the dataset, and the seed.
- 32 peer-reviewed papers since 2023
- Open-source reference kernels for every published method
- Quarterly research-to-production handoff cycles
# pip install monopole
from monopole import Client, Field
client = Client(api_key="mk_live_...")
field = Field(
topology="unified-mesh",
charge=1.0,
horizon=2048,
)
model = client.train(
base="mp-large-v3",
optimizer=field.optimizer(),
dataset="corp/internal-knowledge",
)
for token in model.stream("Summarize Q3 risk."):
print(token, end="")A clean SDK. No surprises.
The Monopole SDK exposes the entire stack through a single, predictable surface. Train, evaluate, deploy, and stream from one client. Versioned endpoints, typed responses, signed payloads.
- Typed clients for Python, TypeScript, Go, and Rust
- Streaming-first inference with backpressure
- Region pinning, residency controls, audit logs
Built for teams that cannot afford ambiguity.
Deterministic builds
Every artifact is hash-verified, signed, and reproducible across regions.
Private deployment
Run the full platform inside your VPC with zero outbound dependencies.
Compliance-ready
SOC 2 Type II, ISO 27001, and HIPAA-aligned controls built in.