NeoSignal Tools: 12 Infrastructure Calculators and Advisors in One Place

AI infrastructure decisions are complex. How much GPU memory do you need? Should you use APIs or self-host? What parallelism strategy fits your cluster? These questions require tribal knowledge that most teams don't have. You either hire expensive consultants or learn through expensive trial and error.

NeoSignal Tools hub showing 12 infrastructure calculators organized by category

NeoSignal Tools democratizes that expertise. Twelve specialized tools covering training, inference, cost optimization, and component discovery—each encoding best practices from production AI systems. Memory Calculator estimates GPU requirements using formulas from the HuggingFace Ultrascaling Playbook. Serving Engine Advisor recommends between vLLM, TensorRT-LLM, and SGLang based on your latency and throughput needs. TCO Calculator compares API versus self-hosted costs with real pricing data.

The benefit: make infrastructure decisions with confidence, even without a dedicated MLOps team. Each tool integrates with NeoSignal chat for contextual guidance beyond the calculator outputs.

Detailed Walkthrough

Tools by Category

NeoSignal organizes tools into five categories reflecting the AI infrastructure lifecycle:

Training Tools

Tools for planning model training infrastructure:

Memory Calculator — Estimate GPU memory for model training

• Input: Model selection, batch size, sequence length, precision, parallelism, ZeRO stage
• Output: Memory breakdown (parameters, gradients, optimizer, activations), GPU recommendation
• Based on: HuggingFace Ultrascaling Playbook formulas

Parallelism Advisor — Optimize distributed training

• Input: Model size, cluster configuration, target throughput
• Output: Tensor parallelism, pipeline parallelism, data parallelism recommendations
• Requires: Authentication (saved configurations)

Inference Tools

Tools for optimizing model serving:

Quantization Advisor — Choose the right quantization method

• Input: Model type, deployment target, quality tolerance, serving engine
• Output: Recommended quantization (INT8, INT4, FP8), expected speedup, quality impact
• Compares: GPTQ, AWQ, GGUF, bitsandbytes

Serving Engine Advisor — Find the optimal inference engine

• Input: Model architecture, latency requirements, throughput needs, hardware constraints
• Output: Engine recommendation (vLLM, TensorRT-LLM, SGLang, llama.cpp), configuration snippets
• Requires: Authentication

Cost Tools

Tools for infrastructure cost planning:

TCO Calculator — Compare API vs self-hosted costs

• Input: Request volume, token counts, model size, provider preferences
• Output: Monthly costs per provider, break-even analysis, cost projections
• Compares: Together AI, OpenAI, Anthropic, Groq, self-hosted

Spot Instance Advisor — Maximize savings with spot instances

• Input: Instance type, workload type, interruption tolerance
• Output: Savings estimate, checkpointing strategy, interruption risk assessment
• Requires: Authentication

Registry Tools

Tools for discovering and comparing components:

Benchmarks — AI model evaluation datasets

• Browse: ARC-AGI, FrontierMath, SWE-Bench, MMLU, HumanEval
• View: Difficulty ratings, leaderboards, model performance
• Compare: Up to 4 models across benchmark dimensions

Component Browser — Browse and compare AI components

• Filter: By category (models, accelerators, cloud, frameworks, agents)
• Sort: By score, trend, release date
• Compare: Side-by-side specifications

Knowledge Graph — Explore entity relationships

• Visualize: Connections between components, companies, signals
• Filter: By entity type, relationship type, confidence threshold
• Navigate: Click nodes to explore related entities

Model Compare — Side-by-side model comparison

• Select: Up to 4 models
• Compare: Benchmark scores, metrics, specifications
• Visualize: Radar charts for multi-dimensional comparison

Stack Builder — Build compatible AI stacks

• Select: Components across 5 categories
• Validate: Pairwise compatibility scores
• Save: Stack configurations as artifacts

Media Tools

Tools for learning and exploration:

Media Browser — Product demos and video walkthroughs

• Browse: NeoSignal feature demonstrations
• Watch: Embedded YouTube videos
• Learn: How to use each NeoSignal capability

Tool Access Patterns

Tools fall into two access categories:

Public Tools (no authentication required):

• Memory Calculator
• Quantization Advisor
• TCO Calculator
• Benchmarks
• Component Browser
• Knowledge Graph
• Model Compare
• Media Browser

Authenticated Tools (require NeoSignal account):

• Parallelism Advisor
• Serving Engine Advisor
• Spot Instance Advisor
• Stack Builder

Authenticated tools save configurations to your account, enabling persistent artifacts and historical analysis.

The Tool Design Philosophy

Each NeoSignal tool follows consistent principles:

Expert Knowledge Encoded: Tools implement formulas and heuristics from production AI systems—not generic calculators, but specific methodologies proven at scale.

Input Flexibility: Configure every relevant parameter. Defaults reflect common configurations, but you can customize for your specific situation.

Actionable Output: Results include specific recommendations, not just raw numbers. "Enable activation checkpointing" is more useful than "memory exceeds capacity."

Chat Integration: Every tool connects to NeoSignal AI chat. Ask follow-up questions about results; get contextual guidance beyond what the calculator shows.

Artifact Persistence: Save configurations to revisit later. Share with teammates for infrastructure discussions.

Using Tools Together

Tools complement each other across the infrastructure planning workflow:

Training Planning Sequence:

1. Memory Calculator → Determine GPU requirements
2. TCO Calculator → Compare cloud provider costs
3. Parallelism Advisor → Optimize distributed strategy
4. Spot Instance Advisor → Reduce compute costs

Inference Planning Sequence:

1. Model Compare → Select the right model
2. Quantization Advisor → Choose compression method
3. Serving Engine Advisor → Pick inference framework
4. TCO Calculator → Validate cost assumptions

Component Selection Sequence:

1. Component Browser → Discover options
2. Benchmarks → Compare performance data
3. Knowledge Graph → Understand relationships
4. Stack Builder → Validate compatibility

Real-World Tool Scenarios

Pre-Project Estimation: You're scoping an AI project and need infrastructure estimates. Start with Memory Calculator to size your training cluster. Use TCO Calculator to budget inference costs. Present concrete numbers in the project proposal.

Optimization Sprint: Your inference costs are too high. Open Quantization Advisor to evaluate compression options—INT4 could cut costs 4x with minimal quality loss. Use Serving Engine Advisor to ensure your framework supports the chosen quantization. Validate savings in TCO Calculator.

Vendor Evaluation: Comparing cloud providers for your AI workload. Use Component Browser to see NeoSignal scores for AWS, GCP, Azure, CoreWeave. Check TCO Calculator for pricing differences. Explore Knowledge Graph to see which providers integrate best with your chosen models.

Team Onboarding: New team members need to understand your infrastructure. Share saved Stack Builder artifacts showing your production configuration. Point them to Benchmarks for model selection context. Use Memory Calculator to explain why you chose your current GPU count.

Chat Enhancement

Every tool integrates with NeoSignal AI chat:

• Contextual Questions: With Memory Calculator open, ask "Why is B200 recommended?" and get an answer referencing your specific configuration
• Comparison Requests: "How does this compare to using A100s?" triggers re-analysis
• Explanation Requests: "Explain ZeRO Stage 3" provides educational context
• Follow-up Guidance: "What should I try next?" suggests tool workflows based on current results

The chat maintains tool context across questions, providing coherent conversation about your infrastructure planning.

Access the Tools

All tools are available at neosignal.io/tools:

From Tools to Decisions

NeoSignal Tools embodies a core belief: AI infrastructure decisions shouldn't require expensive consultants or trial-and-error learning. The knowledge exists—in research papers, production playbooks, and expert experience. We encode that knowledge into tools anyone can use.

Each tool tackles a specific decision. Memory Calculator answers "Will it fit?" TCO Calculator answers "Should I build or buy?" Stack Builder answers "Will these work together?" Together, they cover the infrastructure planning lifecycle from training to inference to cost optimization.

Use the tools to validate assumptions, explore options, and make confident decisions. That's the NeoSignal approach to AI infrastructure intelligence—expert knowledge, accessible to everyone building with AI.