$ loading_
Run, serve, and benchmark local LLMs and image models on your hardware.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "inferbench" yet — see the docs or source repo.
Use InferBench on my RTX 4070 machine to shortlist models suitable for code generation from the catalog, compare quant variants by tokens/sec, VRAM usage, and available context length, and recommend the best option.
A comparison of candidate models, plus the recommended quant for the current GPU with rationale.
Use InferBench to start a local LLM service on this machine and run benchmarks. Report first-token latency, throughput, performance under concurrency, and whether it is suitable for development or production validation.
A runnable local service and a performance report to evaluate deployment feasibility.
Use InferBench to benchmark one llama.cpp text model and one Stable Diffusion image model on the current hardware, then summarize speed, resource usage, and the bottleneck differences between the two workloads.
A summary comparing text and image model performance to help plan local AI workloads.
Delegate low-risk tasks to a cheaper model with main-agent review.
Benchmark and run LLM inference on Arm64 cloud with MCP-compatible results.
Build, debug, and manage software tasks with natural language across LLMs.
Route coding tasks across local and remote LLMs with benchmarking and code search.
Manage local model runtimes with unified discovery, checks, lifecycle control, and inference.
Run Llama models locally for private, offline AI assistance.