LLM Inference Latency Estimator
The spec sheet says H100 = 15× T4 TFLOPS. But a 7B model is 150× faster on H100. The bottleneck isn't compute —it's memory bandwidth. 12 models × 7 GPUs × 4 quantization levels with real benchmark data.
Model Throughput by GPU →GPU Training Cost Estimator
arXiv says 72 GPU-hours. Your AWS bill says 3× that. Spot/preemptible pricing across 6 providers, on-demand real rates. No guesswork —just the per-hour truth.
Run 6-Provider Cost Matrix →LLM Fine-tuning Cost Estimator
Compare fine-tuning across AWS Bedrock, Google Vertex AI, Azure OpenAI, and self-hosted GPU. Full fine-tuning vs LoRA vs QLoRA. Training cost, GPU hours, inference pricing, and hidden hosting/idle fees (Azure $1,241/mo). 2026 pricing with per-platform trap warnings.
4-Platform FT Cost Model →LLM Params →FP16 VRAM
7B × 2 bytes = 14 GB. Then KV cache. Then Adam optimizer states. Then framework overhead. Your 16 GB GPU just became a bottleneck you didn't see coming.
FP16 VRAM Profiler →LLM Params →INT4 VRAM
Same 70B model. FP16 = 140 GB —you need 4× A100s. INT4 = 35 GB —one A100. You're not debating perplexity. You're debating whether the model loads at all.
INT4 VRAM Profiler →LLM API Pricing Calculator
12 models across 6 providers. Token pricing pages tell one story. Your workload's chat-vs-code-vs-summarization ratio tells another. Prompt caching cuts costs 90%. Batch processing: 50%. Model the real bill before it lands.
Compare Provider Pricing →