Does this MCP compare ASICs to GPUs?

Yes. This MCP is specifically designed to compare the Total Cost of Ownership (TCO) between developing custom AI silicon (ASICs) and using standard, off-the-shelf GPU clusters.

What kind of data do I need to provide?

You must provide financial inputs, including Non-Recurring Engineering (NRE) costs, unit costs for both custom silicon and GPUs, and performance metrics to run the models.

Can I find out when custom silicon pays for itself?

You can use the get_break_even_analysis capability. It determines the exact deployment volume needed to justify the switch from GPUs to custom silicon.

Is this just for small projects?

No. The MCP handles large-scale economic modeling, allowing you to project costs and viability across multiple hardware generations and high volumes.

Does it account for development delays?

Yes. The evaluate_ttm_opportunity_cost capability quantifies the financial impact of delays, helping you measure the cost of waiting for custom development.

How do I determine if custom silicon is worth the investment?

You can use the get_break_even_analysis capability to find the exact volume where the cost of custom silicon becomes lower than the cost of using GPUs.

Does this capability account for the delay in chip development?

Yes, the evaluate_ttm_opportunity_cost capability quantifies the economic impact of the time-to-market delay caused by the development cycle.

Can I model the impact of future chip versions?

Yes, use model_iteration_impact to estimate how subsequent hardware versions and performance improvements affect long-term economics.

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Connect AI Custom Silicon Economics to ChatGPT and Claude

Set it up once and it works in ChatGPT, Claude, Cursor, and any other client you love. It all runs safely in the background, so you can relax and focus on what really matters. Determine if custom ASICs beat standard GPU clusters.

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Developed, maintained, and hosted by Vinkius.

MCP VERIFIED · PRODUCTION READY · VINKIUS GUARANTEED

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Works with modern AI clients that support MCP, including ChatGPT, Claude, Cursor, and more.

Your AI does the work. We keep it safe and sound.

No other AI tool gives you this. Your workflow runs in the client you already love, your account stays protected, and you decide what the agent can and cannot do. It is the whole reason to make the switch.

Each Connector lives inside our infrastructure, the same place where your workflow runs. The three things below are already part of it, working quietly in the background so you never have to think about them. Nothing to add, nothing to set up, nothing to worry about.

V8 Isolate01

Each Connector runs inside its own sealed environment.

Each Connector runs inside its own sealed V8 runtime — no access to your files, your system, or other servers. A Connector never sees past its own request.

Kill Switch02

One switch stops every agent. Instantly.

One action deactivates every Connector, revokes every token, and ends every session at once. AI Governance keeps the record.

FinOps per User03

Spending limits that pause instead of surprise.

Every user keeps their own connections and credentials, isolated from everyone else. Anything that would cross your spending limit is held for your approval, not billed.

Three controls, built into the layer your AI connects through.

Curated set · 4 capabilities

We ship only the actions that matter.

Capability set01 / 01

01-04

4 capabilities in this set.

Part of 4 available through Silicon Economics Engine.

01
Compare tco at volume
This capability calculates the total cost difference between custom silicon and GPUs when you deploy a specific number of units.
02
Evaluate ttm opportunity cost
Use this to quantify the financial impact of delays. It measures the opportunity cost associated with waiting during the custom silicon development cycle.
03
Get break even analysis
This determines the exact deployment volume required to justify switching from using standard GPUs to developing custom silicon.
04
Model iteration impact
This projects how subsequent hardware versions affect your long-term economics, allowing you to plan across multiple hardware generations.

Real work

What teams actually run

New Product Line Launch

A company needs to decide if building a custom chip saves money compared to using high-end GPUs for a new product line. Run the TCO comparison to get a clear answer.

Budgeting for Next Fiscal Year

A finance team needs to model the cost impact of waiting two years for a custom chip versus buying today's GPU generation. Use the opportunity cost capability.

Scaling Operations

Your organization is growing rapidly and needs to know the break-even point where custom silicon becomes cheaper than GPU clusters. Run the break-even analysis.

Long-Term Strategy Planning

You need to plan for the next five years of hardware upgrades. Use the iteration impact model to see how costs change with each new chip generation.

One connector, every AI

Silicon Economics Engine works with the most popular AI clients.

These are the most popular clients, each with a step-by-step guide: one link, set up once, with governance and visibility built in. And because everything runs on the MCP standard, the same connection also works in any other compatible client — nothing to rebuild.

Building your own app? The connector is yours to use.

You don't need a client to put Silicon Economics Engine to work: the same hosted connection plugs into your own applications and agent code, with the same governance on every request. Build with it, chat with it — one connection for both.

Observed, not estimated

846ms average. Fast in production.

Silicon Economics Engine is checked daily against the live service.

Daily averagePeak 1173ms

Oct 3Today

Fastest day: 646ms
Slowest day: 1173ms
14-day trend: Slowing+21%

Connect your client

One URL. Every client.

Activate the Connector, copy your link, and paste it into the client you already use. 4 capabilities arrive ready to run.

Preview access · not provider authentication

The vk_preview_* token belongs to Vinkius preview infrastructure. It lets Claude discover and display the capabilities of Silicon Economics Engine, so you can see the experience inside your AI.

It does not authenticate your account with Silicon Economics Engine. Actions requiring credentials or live account data may not run until you activate the Connector and authorize the service.

Silicon Economics Engine Connector

You're all set. Choose your MCP client and follow the setup instructions.

Connector linkhttps://edge.vinkius.com/vk_preview_kKxvQ3VAizzni9cf4FjnBTAld45Gtoywbv3Rd5rc/mcp

Claude Desktop

Follow the steps below to connect in seconds.

1In Claude Desktop, open Settings → Connectors.
2Click “Add custom connector” and paste the connector link above as the remote MCP server URL.
3Click Add and start a new chat — Silicon Economics Engine capabilities are ready to use.

Configuration · claude_desktop_config.jsonCopy

{
  "mcpServers": {
    "ai-custom-silicon-economics-mcp": {
      "url": "https://edge.vinkius.com/vk_preview_kKxvQ3VAizzni9cf4FjnBTAld45Gtoywbv3Rd5rc/mcp"
    }
  }
}

Claude
ChatGPT
Cursor
VS Code
Windsurf
Claude Code
JetBrains
Cline

Step-by-step instructions for each client are in the guide. How to connect

Guided setup for Claude? Set up Silicon Economics Engine in Claude and other AIs

See all the AI clients this connector works with ↑

Who it's for

Built for the work Silicon Economics Engine owners hand off.

This MCP is essential for technical leadership and finance teams planning large-scale AI infrastructure. If you're deciding between massive CapEx on custom chips or sticking with established GPU clusters, this capability provides the necessary financial rigor. It turns abstract hardware decisions into concrete, measurable costs.

01
CTO / VP of Engineering
Use this to build the business case for custom silicon development versus buying off-the-shelf hardware.
02
Financial Analyst
Run detailed TCO models to forecast costs across multiple operational scales and time horizons.
03
AI Infrastructure Planner
Determine the optimal timing and volume for hardware upgrades to maximize ROI.

FAQ

Questions Silicon Economics Engine owners ask.

01
Does this MCP compare ASICs to GPUs?
Yes. This MCP is specifically designed to compare the Total Cost of Ownership (TCO) between developing custom AI silicon (ASICs) and using standard, off-the-shelf GPU clusters.
02
What kind of data do I need to provide?
You must provide financial inputs, including Non-Recurring Engineering (NRE) costs, unit costs for both custom silicon and GPUs, and performance metrics to run the models.
03
Can I find out when custom silicon pays for itself?
You can use the get_break_even_analysis capability. It determines the exact deployment volume needed to justify the switch from GPUs to custom silicon.
04
Is this just for small projects?
No. The MCP handles large-scale economic modeling, allowing you to project costs and viability across multiple hardware generations and high volumes.
05
Does it account for development delays?
Yes. The evaluate_ttm_opportunity_cost capability quantifies the financial impact of delays, helping you measure the cost of waiting for custom development.
06
How do I determine if custom silicon is worth the investment?
You can use the get_break_even_analysis capability to find the exact volume where the cost of custom silicon becomes lower than the cost of using GPUs.
07
Does this capability account for the delay in chip development?
Yes, the evaluate_ttm_opportunity_cost capability quantifies the economic impact of the time-to-market delay caused by the development cycle.
08
Can I model the impact of future chip versions?
Yes, use model_iteration_impact to estimate how subsequent hardware versions and performance improvements affect long-term economics.

Explore

More in Finance

Connect Silicon Economics Engine to Claude, Cursor, ChatGPT & more

Your AI does the work. We keep it safe and sound.

Each Connector runs inside its own sealed environment.

One switch stops every agent. Instantly.

Spending limits that pause instead of surprise.

We ship only the actions that matter.

01-04

Compare tco at volume

Evaluate ttm opportunity cost

Get break even analysis

Model iteration impact

What teams actually run

New Product Line Launch

Budgeting for Next Fiscal Year

Scaling Operations

Long-Term Strategy Planning

Building your own app? The connector is yours to use.

Silicon Economics Engine Connector

Claude Desktop

CTO / VP of Engineering

Financial Analyst

AI Infrastructure Planner

Does this MCP compare ASICs to GPUs?

What kind of data do I need to provide?

Can I find out when custom silicon pays for itself?

Is this just for small projects?

Does it account for development delays?

How do I determine if custom silicon is worth the investment?

Does this capability account for the delay in chip development?

Can I model the impact of future chip versions?

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