LLM Inference Calculator
LLM Inference Calculator estimates throughput, latency, TTFT, TPOT, and GPU memory usage for large language model inference.
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What is LLM Inference Calculator?
LLM Inference Calculator is an interactive calculator for predicting LLM inference performance metrics. Users input a model (e.g., Llama 3.1 8B) and hardware configuration to calculate latency, throughput, VRAM usage, and compute/bandwidth utilization. It helps engineers and researchers estimate deployment costs and optimize inference strategies, covering quantization, GPU count, and interconnect options.
LLM Inference Calculator pricing
Pricing model: Freemium
LLM Inference Calculator pros
- Detailed phase-level metrics: breaks down prefill vs. decode performance separately
- Hardware resource visibility: shows VRAM per GPU, max batch size, and utilization rates
- Flexible deployment configurations: supports parallel training, expert parallelism, and pipeline parallelism options
- Real-time calculations: displays end-to-end latency, throughput, and communication overhead
LLM Inference Calculator cons
- Requires specialized knowledge of inference optimization terminology (TP, EP, PP, TTFT, TPOT)
- No visible pricing or model selection UI shown in content provided
- Limited transparency on which models are supported beyond the Llama 3.1 example
Frequently asked questions about LLM Inference Calculator
What is TTFT and TPOT?
TTFT (Time to First Token) measures latency for the prefill phase; TPOT (Time Per Output Token) measures decode phase latency. Together they determine total end-to-end latency.
What do TP, EP, PP, DP stand for?
Tensor Parallelism (TP), Expert Parallelism (EP), Pipeline Parallelism (PP), and Data Parallelism (DP) — distributed inference optimization strategies that trade off communication overhead for compute distribution.
What is the significance of compute vs. bandwidth utilization?
Prefill is compute-bound (higher utilization = better); decode is bandwidth-bound. The calculator shows how well each phase saturates available GPU resources.