Llm Inference Hardware Calculator

by
Expert Reviewer: David Chen, Ph.D. in AI Systems Architecture

Accurately estimate the operational cost of deploying Large Language Models (LLMs) on specific hardware configurations. This calculator simplifies complex TFLOPS, model size, and efficiency variables into a single, actionable metric: **Cost per Million Tokens**.

LLM Inference Hardware Calculator

Estimated Inference Cost:

—

LLM Inference Hardware Cost Formula

The core calculation determines the Cost per Token ($C_{\text{token}}$) based on the achieved throughput ($T_{\text{achieved}}$) and the GPU hourly rate ($H$).

Tokens per Hour = T_ideal × (Efficiency / 100) × 3600
where T_ideal ≈ (GPU TFLOPS × 500) / Model Size (Billion Parameters)

Cost per Token ($) = GPU Hourly Cost / Tokens per Hour
Cost per Million Tokens = Cost per Token × 1,000,000

Formula Sources: AnandTech – GPU Performance Metrics, ArXiv – Inference Scaling Laws

Variables Explained

  • GPU Hourly Cost (H): The cost to rent the specific GPU hardware (e.g., $2.50/hour for an H100 in the cloud).
  • GPU Effective TFLOPS (F): The actual, measured throughput of the GPU for matrix multiplication (FP16/BF16), measured in Tera Floating Point Operations Per Second. This is a crucial hardware specification.
  • Model Size (M): The number of parameters in the LLM, measured in Billions (e.g., 7B, 70B, 180B). Larger models require more computation per token.
  • Inference Efficiency (U): The percentage representing how close the actual realized throughput is to the theoretical maximum TFLOPS of the GPU, accounting for memory access (DRAM/HBM) and software overheads. Typical values range from 30% to 70%.

Related Calculators

Explore other financial and performance tools for optimizing your AI infrastructure:

What is an LLM Inference Hardware Calculator?

An LLM Inference Hardware Calculator is a specialized tool used by AI engineers and finance managers to forecast the operational expenditure (OpEx) of deploying large language models. It moves beyond simple hourly cloud costs by incorporating the technical specifications of the underlying hardware (TFLOPS) and the computational demand of the specific model (parameters).

The primary purpose is to identify bottlenecks and optimize the deployment strategy. Since the cost of generating a single token can vary drastically based on the GPU used and the software optimization layer (e.g., quantization, sparse attention), this calculation provides the true “unit cost” of the AI service. This is essential for pricing APIs and projecting long-term profitability.

How to Calculate LLM Inference Cost (Example)

Let’s use an example to walk through the calculation steps for a 70B model on an H100 equivalent GPU:

  1. Define Variables: Assume $H = \$2.50/\text{hr}$, $F = 150.0$ TFLOPS, $M = 70$ Billion Parameters, and $U = 55\%$.
  2. Calculate Ideal Throughput ($\text{T}_{\text{ideal}}$): $500 \times 150.0 / 70 \approx 1071.43$ Tokens/s.
  3. Calculate Achieved Throughput ($\text{T}_{\text{achieved}}$): $1071.43 \times (55 / 100) \approx 589.29$ Tokens/s.
  4. Calculate Total Tokens per Hour: $589.29 \times 3600 \approx 2,121,444$ Tokens/hr.
  5. Determine Cost per Token: $\$2.50 / 2,121,444 \approx \$0.00000118$.
  6. Final Metric (Cost per Million Tokens): $\$0.00000118 \times 1,000,000 \approx \$1.18$.

Frequently Asked Questions (FAQ)

Q: Why is “Inference Efficiency” so important?

A: Efficiency accounts for real-world factors like memory bandwidth limits (VRAM speed) and kernel launch overhead. While a GPU might boast high TFLOPS, memory-bound models will have a lower efficiency percentage (often below 50%), directly increasing the cost per token.

Q: Does this calculator account for quantization (e.g., 4-bit, 8-bit)?

A: Quantization primarily affects Model Size (M) and Efficiency (U). To use this calculator for a 4-bit model, adjust the *Effective* Model Size (M) down, and typically adjust the Efficiency (U) up to reflect better performance utilization.

Q: What is a typical “good” Cost per Million Tokens for a high-end LLM?

A: A highly optimized, self-hosted deployment on top-tier hardware should aim for a range between $0.80 to $2.00 per million tokens. Cloud-API costs are typically 5x to 10x higher due to profit margins and shared infrastructure overhead.

Q: How does Batch Size impact these variables?

A: Increasing the batch size significantly boosts Inference Efficiency (U) because it maximizes the utilization of the GPU’s compute units, turning what might be a memory-bound operation into a compute-bound one. Always use the efficiency (U) measured at your planned batch size.

V}

Leave a Comment