AI & Machine Learning Calculators

Plan LLM costs, tool context, agent reliability, GPU memory, KV cache, serving concurrency, training, LoRA and QLoRA, RAG retrieval, embeddings, inference performance, multimodal workloads, and AI business value. Every calculator shows formulas, assumptions, worked examples, limitations, and related tools.

40 free calculators · No sign-up · Browser-based

API Costs & Tokens

AI API Cost CalculatorEstimate AI API costs from input, cached-input, and output tokens, request volume, and provider rates. Compare daily and monthly LLM spend.LLM Cost Comparison CalculatorCompare two LLMs using input, cached-input, output-token, request, and quality assumptions. Find monthly cost and cost per successful task.LLM Token Count CalculatorEstimate LLM tokens from text length, words, characters, language, code content, and expected output. Check context-window usage and API cost inputs.LLM Context Window CalculatorPlan an LLM context window across system prompt, user input, conversation history, RAG context, tool schemas, and reserved output tokens.Prompt Caching Savings CalculatorEstimate prompt-cache savings from reusable prefix tokens, cache hit rate, normal input price, cache-read price, request volume, and cache-write cost.AI Batch API Savings CalculatorCompare standard and batch LLM API pricing for asynchronous jobs. Estimate monthly savings, break-even volume, and delayed-processing value.AI Inference Cost CalculatorCalculate AI inference cost per request, per user, per successful task, and per month using token, GPU, hosting, and utilization assumptions.

Agents & Automation

AI Agent Cost CalculatorEstimate AI agent cost from planning steps, LLM calls, input and output tokens, tool calls, retries, success rate, and human-review cost.AI Productivity ROI CalculatorCalculate AI productivity ROI from users, time saved, adoption, labor cost, software, API, implementation, review, and error-correction costs.MCP Tool Schema Token CalculatorEstimate the token and API-cost overhead of MCP tool names, descriptions, JSON schemas, arguments, and tool results across agent requests.AI Agent Reliability CalculatorEstimate end-to-end AI agent success probability from model steps, tool reliability, retries, human review coverage, and correction rates.

Model Architecture & Compute

Model Parameter CounterEstimate trainable parameters for dense layers, convolutions, embeddings, and transformer blocks. Calculate model size from parameter count and precision.Neural Network FLOPs CalculatorEstimate neural-network FLOPs for dense, convolution, and transformer operations. Compare forward-pass, inference, and approximate training compute.LLM Training Compute CalculatorEstimate dense LLM training FLOPs, accelerator time, GPU days, utilization-adjusted throughput, and cost from parameters and training tokens.

GPU Memory & Performance

Model Parameter Memory CalculatorConvert model parameter count and precision into raw weight memory. Include quantization metadata and multi-GPU sharding assumptions.LLM Quantization CalculatorEstimate LLM storage and VRAM reduction from FP32, FP16, INT8, INT4, or custom-bit quantization, including metadata overhead.LLM GPU VRAM CalculatorEstimate LLM GPU VRAM for weights, KV cache, activations, optimizer states, gradients, batch size, sequence length, and safety overhead.Batch Size vs Sequence Length CalculatorEstimate the maximum LLM batch size for a sequence length and VRAM budget, or compare memory pressure as context and batch size change.LLM Tokens per Second CalculatorCalculate LLM generation throughput, requests per minute, GPU utilization, and capacity from tokens, latency, batch size, and concurrent replicas.AI Inference Latency CalculatorEstimate AI response latency from queue time, prompt tokens, prefill throughput, output tokens, decode speed, network delay, and tool calls.LLM KV Cache Memory CalculatorEstimate LLM KV cache memory per token, request, and concurrent batch using layers, KV heads, head dimension, precision, and tensor parallelism.LLM Serving Concurrency CalculatorEstimate memory-limited and traffic-required LLM concurrency from GPU VRAM, model memory, KV cache, context length, request rate, and latency.Speculative Decoding Speedup CalculatorEstimate LLM latency and speedup from draft tokens, acceptance rate, draft throughput, target verification time, and output length.

Training & Data

AI Fine-Tuning Cost CalculatorEstimate AI fine-tuning cost from dataset tokens, epochs, provider training price, GPU-hours, evaluation runs, and storage overhead.Learning Rate Scaling CalculatorScale a learning rate when changing effective batch size using linear or square-root rules, warmup, gradient accumulation, and world size.Machine Learning Dataset Split CalculatorSplit a machine-learning dataset into train, validation, and test sets with ratios, minimum counts, class-stratification guidance, and k-fold planning.LoRA Adapter Size CalculatorCalculate LoRA trainable parameters, adapter checkpoint size, training-state memory, and trainable percentage from rank and target layers.QLoRA Training VRAM CalculatorEstimate QLoRA fine-tuning VRAM from quantized base weights, LoRA parameters, gradients, optimizer state, activations, runtime overhead, and safety margin.Effective Batch Size CalculatorCalculate global effective batch size, tokens per optimizer step, steps per epoch, and total training steps from micro-batch, accumulation, and data par…Distributed Training Time CalculatorEstimate wall-clock training time, accelerator-hours, utilization, and cloud cost from dataset tokens, epochs, GPU throughput, scaling efficiency, and o…AI Data Labeling Cost CalculatorEstimate annotation labor, quality assurance, rework, tooling, management overhead, calendar time, and cost per accepted labeled example.

Evaluation & Quality

AI Evaluation Sample Size CalculatorEstimate the number of AI evaluation examples needed for a target confidence level, margin of error, expected pass rate, and finite test population.

RAG & Embeddings

RAG Chunk Size CalculatorCalculate RAG chunk count, effective stride, overlap duplication, embedding tokens, retrieval context, and approximate embedding cost.Vector Embedding Storage CalculatorEstimate vector database storage from embedding count, dimensions, precision, metadata, index overhead, replicas, growth, and monthly storage price.Embedding Similarity CalculatorCompare two embedding vectors using cosine similarity, dot product, Euclidean distance, magnitudes, and normalized similarity.RAG Retrieval Metrics CalculatorCalculate Precision@K, Recall@K, hit rate, MRR, average precision, DCG, and NDCG from ranked RAG retrieval relevance judgments.RAG Context Cost CalculatorEstimate RAG input-token, output-token, reranking, query-embedding, and monthly context costs from chunks, candidates, compression, caching, and volume.

Multimodal AI

AI Image Generation Cost CalculatorEstimate AI image-generation spend from images, variants, retries, resolution, quality multipliers, base price, moderation, and storage.Speech-to-Text Cost CalculatorEstimate speech-to-text transcription cost from audio hours, per-minute pricing, channels, diarization, retries, and human quality review.Text-to-Speech Cost CalculatorEstimate text-to-speech cost from characters, words, speaking rate, voice tier, retries, localization, and audio-delivery overhead.

Practical calculators for modern AI engineering

The CalcNovaHub AI calculator suite supports developers, machine-learning engineers, solution architects, students, product managers, and businesses planning hosted and self-managed AI systems. The 40 tools cover API economics, tokens, context, MCP tools, agent workflows, model architecture, GPU memory, serving capacity, fine-tuning, distributed training, evaluation, retrieval, multimodal services, and return on investment.

LLM cost, tools, agents, and context planning

Start with AI API Cost, LLM Cost Comparison, Token Count, Context Window, MCP Tool Schema Token, Agent Cost, and Agent Reliability. These tools separate repeated context, generated tokens, tool payloads, retries, task success, and human review so production workflows are not modeled as a single ideal request.

GPU memory, KV cache, serving, and inference performance

Use the GPU VRAM, Model Memory, Quantization, KV Cache Memory, Serving Concurrency, Tokens per Second, Batch Size, Inference Latency, and Speculative Decoding calculators together. Model weights are only one component: active sequences, context length, KV precision, runtime reserves, queueing, and batching can determine real capacity.

Training, LoRA, QLoRA, data, and compute

The Fine-Tuning Cost, LoRA Adapter Size, QLoRA VRAM, Effective Batch Size, Learning Rate Scaling, Distributed Training Time, Training Compute, Dataset Split, Data Labeling Cost, and Evaluation Sample Size calculators support planning from data preparation through training and validation.

RAG retrieval quality and recurring context cost

Use RAG Chunk Size, Retrieval Metrics, Context Cost, Vector Storage, and Embedding Similarity to balance quality and spend. Evaluate retrieval independently from generation, measure Precision@K and Recall@K, and compare recurring context-token costs with initial embedding and storage costs.