LLM Inference Speed Calculator

Our LLM Inference Speed Calculator estimates your model's generation speed by modeling the primary computational bottleneck: memory bandwidth. The core principle is that generating tokens requires constantly moving weights and activations between GPU memory and compute units.

We factor in several crucial variables:

• Memory Bandwidth (GB/s): This is the rate at which data can be moved. It's often the limiting factor, especially for larger models.
• Model Size & Quantization: Smaller quantization (e.g., 8-bit vs. 4-bit) reduces the data size, allowing more tokens to be processed per cycle, but larger models require more bandwidth regardless.
• Context Window: A longer context window increases initial memory consumption and overall throughput requirements for the prompt phase.

By inputting your specific model parameters, we provide a realistic tokens/second estimate across various hardware architectures like the H100 or RTX 4090.

To maximize your calculated inference speed and optimize deployment, consider these practical strategies:

• Model Pruning & Distillation: Instead of running a massive 70B parameter model directly, use smaller, distilled versions. A 13B distilled model might achieve 95% of the performance at half the memory cost.
• Optimal Context Management: If your application allows, implement token limits or summarization techniques to prevent unnecessary context window bloat, which slows down every generation step.
• Hardware Comparison: When comparing GPUs like the RTX 4090 versus the H100, remember that the H100's superior interconnect (NVLink) and bandwidth often provide a more predictable performance uplift for large-scale deployment.

Test with realistic workload profiles—not just single prompts—for the most accurate assessment.