llmspeed: Predicting Local LLM Performance Before Hardware Investment

Understanding Local LLM Performance

llmspeed is a web-based utility that allows users to simulate the performance of various local Large Language Models (LLMs) across a wide range of hardware, from consumer GPUs like the NVIDIA RTX 4090 to high-end Apple Silicon and cloud-based enterprise hardware. By selecting a model, quantization level, and hardware configuration, users can estimate tokens per second (tok/s) and time-to-first-token (TTFT) metrics before committing to expensive hardware purchases.

The Utility of Performance Estimation

For many users, determining the hardware requirements for local inference is a significant challenge. The project aims to solve two primary friction points: the high cost of trial-and-error hardware investment and the inefficiency of downloading multiple model quantizations just to test compatibility and speed. The tool provides a structured way to visualize how different memory capacities and architectures impact performance, offering a clearer picture of whether a specific setup will meet the user's needs for tasks like coding, summarization, or agentic workflows.

Community Feedback and Limitations

While the tool provides a valuable starting point, the community has highlighted several areas where real-world usage diverges from simulated benchmarks. Users have noted that actual performance often depends heavily on specific software implementations, such as the use of speculative decoding, KV cache management, and custom inference engine forks like llama.cpp or Turboquant.

Key insights from the community include:

• Real-world vs. Benchmark: Many users report that benchmarks often fail to account for complex agentic workflows, which involve large context windows (60k–140k tokens) and significant prefill times. As one user noted, "Most of the time that will give you half to a third of decode throughput vs the benchmark."
• Configuration Depth: Advanced users have requested support for more complex setups, including multi-GPU configurations (both homogeneous and heterogeneous), PCIe generation selection, and NVLink status, which significantly impact throughput.
• Optimistic Estimates: Some users observed that the tool's estimates can be overly optimistic compared to their own testing. Discrepancies often arise from the specific software harness used, as well as the difference between peak performance and sustained performance under load.
• Hardware Coverage: While the site covers a broad range of modern hardware, users have pointed out the omission of older, still-capable hardware (e.g., the RX 580) and the need for more granular support for hybrid GPU setups.

Best Practices for Local Inference

For those looking to optimize their local LLM experience, the community suggests looking beyond simple token-per-second metrics. Effective local inference often relies on:

"It becomes much more about cache aware routing and KV cache management (how to offload and restore). Because prefilling 60-140k will simply take too long for interactive work."

Furthermore, users emphasize that mainstream benchmarks often underestimate what is possible with older hardware when using highly optimized inference engines. By utilizing specialized forks and parameter tuning, users can often achieve performance levels that exceed what standard hardware recommendations suggest, proving that software optimization is as critical as raw hardware power.