--- title: SpinForge Warp: Differentiable Annealing | SpinForge url: https://spin-forge.com/synthesis/warp description: Differentiable annealing that lets a learning workflow backpropagate through optimization. --- Synthesis / Layer 1 / A SPINFORGE / A GARMO LABS COMPANY # SpinForge Warp Differentiable annealing that lets a learning workflow backpropagate through optimization. [Explore the family](https://spin-forge.com/synthesis) [Talk about your workload](https://spin-forge.com/contact) Customer outcome [Design and discover](https://spin-forge.com/platform/discovery-design) THE PRODUCT, IN PRACTICE ## Optimize a decision. Learn through it. An optimization result can sit between the parameters a team learns and the outcome it cares about. SpinForge Warp makes the optimization step differentiable, so a compatible learning workflow can propagate feedback through that step rather than treating it only as a separate black box. BUILT FOR Teams connecting optimization to machine learning, differentiable simulation or computational design. CHALLENGES PRODUCT RESPONSE ## The problem you face. What the product changes. YOUR CHALLENGE WITH SPINFORGE WARP 01 ### Optimization breaks the feedback path A separately executed search can leave a learning workflow without a gradient through its decision step. SpinForge Warp provides differentiable annealing on NVIDIA Warp, making that optimization step part of the supported backpropagation workflow. 02 ### Learning and search are tuned separately Parameters selected upstream may not reflect what happens inside the optimization step. The differentiable path lets compatible training use feedback through optimization, tying parameter changes to the downstream modeled objective. 03 ### A gradient needs a defined interpretation An optimization-related gradient is useful only in the model and numerical regime where it is evaluated. Qualify the objective, gradient behavior and numerical conditions together with the customer’s accepted end-to-end task. WHAT YOU GET ## A defined capability. A useful result. 01 ### Differentiable optimization Annealing that participates in a supported backpropagation computation. 02 ### NVIDIA Warp implementation The product’s stated execution foundation, not a new proprietary GPU language. 03 ### A learning integration point A route to connect the optimization step to a compatible training objective. 04 ### A defined evaluation scope Agree numerical checks, task quality and complete execution cost for your workflow. EXAMPLE CUSTOMER WORKFLOW ## Learn a decision-dependent parameter A team wants an upstream parameter choice to reflect the optimized result downstream. Illustrative customer use—not a new benchmark or completed customer deployment. 1. 01 Define the modeled objective and the optimization step. 2. 02 Integrate the supported differentiable operation into the learning workflow. 3. 03 Evaluate gradients, task outcomes and total training/execution cost. FIT & ACCEPTANCE ## Start with the task. Agree the useful outcome. What you bring The differentiable model, optimization objective, supported execution environment and acceptance criteria. What a useful evaluation measures - + Gradient behavior within the qualified model - + Quality of the downstream task - + Total compute and memory cost Delivery License the selected capability, commission an implementation, or agree an operated service. Infrastructure and additional engineering are specified separately. QUESTIONS BEFORE BUYING ## A clear scope. A clear relationship. **What is the important boundary? − **Differentiability is a product capability, not a guarantee of a global optimum or a universal learning speedup. The supplied table does not provide a product-specific benchmark, arbitrary-operation coverage or a universal gradient-correctness claim. **How is it priced? + **No new standalone price is supplied for this product; request a scoped proposal. The platform plans buy qualified workflows and capacity, not a second charge every time a compatible method is selected. Existing bundle inclusions do not expand automatically. **How do we get access? + **An existing product in the SpinForge portfolio. Request the release, available invocation interfaces and setup for your selected workflow; this publication does not invent executable commands or MCP tool names. PUT THE PRODUCT TO WORK ## Tell us what needs to improve. Bring a task, an accepted outcome and the environment in which the product needs to work. [Discuss this workflow ](https://spin-forge.com/contact?product=synthesis%2Fwarp&brief=Product%3A+SpinForge+Warp%0AOur+challenge%3A%0AOur+workload%2Finput%3A%0AExpected+result%3A%0AAcceptance%3A+Gradient+behavior+within+the+qualified+model%3B+Quality+of+the+downstream+task%3B+Total+compute+and+memory+cost%0AEnvironment+and+delivery+preference%3A%0A) PRODUCT RELATIONSHIPS ## Related, not interchangeable. [Synthesis ### Neural Schedule Learned temperature schedules for annealing through a Fourier neural operator.](https://spin-forge.com/synthesis/neural-schedule) [Synthesis ### Gödel Optimizer A self-evolving optimizer that improves its optimization algorithms.](https://spin-forge.com/synthesis/godel-optimizer) [Synthesis ### Recursive Engine The synthesis meta-system through which its eleven products improve one another.](https://spin-forge.com/synthesis/recursive-engine) ONE PLATFORM ## Choose a qualified workflow. Select the capability you need directly or include its compatible role in a qualified platform workflow. Production scope identifies release, environment, capacity and support; a product count is not an entitlement. [Explore monthly plans](https://spin-forge.com/plans) [See the system connections](https://spin-forge.com/system-synergies/product-map) START WITH THIS PRODUCT Use the software directly or ask us to scope the implementation. [Software access](https://spin-forge.com/plans?product=synthesis/warp) [Have us do the work](https://spin-forge.com/contact?product=synthesis/warp&delivery=engineering)