> For clean Markdown of any page, append .md to the page URL.
> For a complete documentation index, see https://docs.nvidia.com/nixl/user-guide/benchmarking-nixl/kv-bench/llms.txt.
> For AI client integration (Claude Code, Cursor, etc.), connect to the MCP server at https://docs.nvidia.com/nixl/_mcp/server.

# KVBench

> A KV cache benchmarking utility that generates and runs NIXLBench commands to profile LLM inference transfer performance.

KVBench is a benchmarking utility that generates and runs [NIXLBench](/nixl/user-guide/benchmarking-nixl/nixl-bench) commands for profiling KV cache transfer performance across LLM architectures. The `profile` command invokes `nixlbench` as a subprocess, executing the transfer benchmarks that KVBench plans based on model architecture and access patterns.

## Command Categories

### KVBench Commands

- **plan** -- Display the recommended NIXLBench configuration for a given model architecture and access pattern
- **profile** -- Run NIXLBench with the planned configuration and collect performance results
- **kvcache** -- Display KV cache layout information for a model architecture

### CTP Commands

- **ct-perftest** -- Run custom traffic performance tests using asymmetric transfer matrices
- **sequential-ct-perftest** -- Run sequential custom traffic performance tests for ordered matrix evaluation

## Supported Models

KVBench includes model architecture definitions for several LLM families: DeepSeek R1, Llama 3.1, and more. See [Commands and Examples](/nixl/user-guide/benchmarking-nixl/kv-bench/using-kv-bench) for details on defining custom model architectures.

## Next Steps

- **[Building KVBench](/nixl/user-guide/benchmarking-nixl/kv-bench/building-kv-bench)** -- Docker and Python virtual environment installation
- **[Commands and Examples](/nixl/user-guide/benchmarking-nixl/kv-bench/using-kv-bench)** -- Full command reference and usage examples

> A KV cache benchmarking utility that generates and runs NIXLBench commands to profile LLM inference transfer performance.

## Docs

- [Building KVBench](https://docs.nvidia.com/nixl/user-guide/benchmarking-nixl/kv-bench/building-kv-bench.md): Install KVBench using the NIXLBench Docker container or a Python virtual environment.
- [KVBench Commands and Examples](https://docs.nvidia.com/nixl/user-guide/benchmarking-nixl/kv-bench/using-kv-bench.md): KVBench command reference, model configuration guide, and LLM example configurations.