WhichLLM: Local LLM Hardware Benchmarking

Signal

• Show HN: Find the best local LLM for your hardware, ranked by benchmarks · GitHub · 2026-05-15 The repository whichllm provides a curated benchmarking dataset and ranking mechanism for local large language models, mapping model performance metrics against specific hardware constraints to assist operators in selecting optimal inference configurations for their available compute.

Context

Local inference workflows require matching model architecture and size to available VRAM, CPU, and memory bandwidth. Tools in this space reduce the friction of trial-and-error deployment by providing empirical performance data relative to hardware tiers. whichllm aggregates benchmark results to facilitate this matching process, addressing the fragmentation of open-weight models where performance characteristics vary significantly across different hardware backends.

Relevance

This signal reinforces the pattern of hardware-aware model selection as a prerequisite for stable local agent operations. As the ecosystem expands with diverse open-weight releases, automated or semi-automated discovery of compatible models becomes critical for reducing setup latency and preventing resource exhaustion. The entry supports the Local Inference as Baseline circuit by providing actionable data for runtime composition decisions.

Current State

The project appears to be a community-maintained repository focused on benchmark aggregation and hardware compatibility ranking. It targets operators seeking to deploy local models without cloud dependencies, offering a reference layer for model selection based on quantitative performance data rather than marketing claims.

Open Questions

• How frequently is the benchmark dataset updated relative to new model releases?
• Does the ranking account for quantization effects and specific inference engine optimizations (e.g., vLLM vs. llama.cpp)?
• Is the tool designed for programmatic integration into agent setup scripts, or is it primarily a reference resource?

Connections

• Maps to Local Inference as Baseline circuit: Provides data layer for hardware-model matching.
• Relates to Adaptive Model Routing & Fallback Infrastructure circuit: Informs routing decisions based on hardware constraints.