HAMERSPACE

Optimization, abstracted.

Automatic optimization for trained models. Reduce size, increase speed, and cut inference costs with compiler-style automation.

Join Early AccessHow it Works
Early Access / Launching Soon
01 / The Problem

WASTED POTENTIAL

Most models ship unoptimized

ML teams train sophisticated models, then deploy them without optimization. The result? Wasted compute, higher infrastructure costs, and slower inference for end users.

Optimization is too complex

Quantization, pruning, graph optimization—each technique requires specialized knowledge and fragmented tooling. Most teams skip it entirely, leaving performance on the table.

02 / The Insight

TREAT IT LIKE A COMPILER

Model optimization should work like a compiler: analyze the input, apply proven transformations, and output an optimized artifact—all automatically.

Hamerspace abstracts backend-specific optimization into an automated workflow. You define your goals and target hardware. We handle the rest—quantization, pruning, graph rewrites, and validation.

Quick Start

Start in
Minutes

Get started with the Hamerspace Python SDK. Load your model, define constraints, and let AUTO mode find the best optimizations.

01

Install

Available on PyPI. Single command installation.

02

Configure

Set your constraints: target size, latency, or accuracy threshold.

03

Optimize

Run AUTO mode. The compiler finds the best optimization path.

optimize.py
# Install
$ pip install hamerspace

# Load your model
from hamerspace import Optimizer

optimizer = Optimizer(
  model="path/to/model.pth",
  mode="AUTO"
)

# Define constraints
optimizer.set_constraints(
  target_size=0.5, # 50% reduction
  max_accuracy_loss=0.02 # 2% threshold
)

# Run optimization
result = optimizer.optimize()

# Export optimized model
result.save("optimized_model.pth")

Full docs available at launch → hamerspace.dev/docs

03 / Process

HOW IT WORKS

01

Analyze Model

Upload your trained model. We analyze architecture, operations, and computational patterns.

02

Select Strategy

Define goals (size, latency, cost) and target hardware. We recommend optimization techniques.

03

Apply Passes

Automated optimization passes: quantization, pruning, graph fusion, and backend-specific rewrites.

04

Benchmark & Validate

Run accuracy and performance tests. Ensure the optimized model meets your quality thresholds.

05

Export Artifacts

Download optimized model, deployment config, and detailed performance report.

04 / Audience

WHO IT'S FOR

ML Engineers

Deploying trained models to production without specialized optimization expertise.

AI Startups

Running inference at scale and looking to reduce cloud costs before growth.

MLOps Teams

Optimizing CPU, ARM, and edge workloads where every millisecond matters.

Technical Founders

Building AI products and need to ship faster, cheaper inference pipelines.

05 / Coming Soon

HAMERSPACE STUDIO

A web-based optimization workspace for ML teams. Upload models, define optimization goals, and export production-ready artifacts—all from your browser.

Features

  • • Visual optimization workflows
  • • Model upload and analysis
  • • Goal-based optimization engine
  • • Real-time benchmarking

Export

  • • Optimized model artifacts
  • • Deployment configurations
  • • Performance reports
  • • Integration guides

Join early access to be first in line when Studio launches.

06 / Early Access

JOIN THE WAITLIST

Be among the first to access Hamerspace. Early users get priority access, influence on roadmap, free credits, and launch pricing.