llmrun.dev Ranks Local LLMs for Your Hardware
The browser-based checker pairs hardware detection with memory and speed estimates to help you shortlist local AI models before downloading them.
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The browser-based checker pairs hardware detection with memory and speed estimates to help you shortlist local AI models before downloading them.
A browser-based experiment links behavioral signals to interface changes and updated help text, with fixed identifiers and validation rules keeping the model’s…
Seven open-weight models compared by the memory they need, how well they code, and the license terms attached, from 8 GB laptops…
LFM2.5-24B-A2B offers open weights, a 131K context window, and broad local-runtime support, but its speed and benchmark results remain vendor-reported.
Recursive’s open-weight coding model targets offline development, but its context limits, memory requirements, and speed claims need hardware-specific reading.
Hugging Face’s open model combines DNA and conversation, but its deployment options are better established than its biological reliability.
A study of ten coding-agent configurations found that writable session traces could be altered after direct requests, malicious skill instructions, and scoring…
Kimi, GLM, DeepSeek, Qwen, and Ling models compared on coding benchmarks, license terms, API prices, and what it takes to self-host them.
VOLTA-AUTODOXER adds an optional AI analysis stage after stealing data, using configured model fallbacks to turn credentials and personal records into prioritized…
Apple’s faster desktop chips improve prompt processing, while M5 Pro and M5 Ultra supply the memory that larger local models demand.
NVIDIA’s TensorRT Edge-LLM guide provides a local deployment recipe for the 64 GB DGX Spark, with specific software requirements and workload-dependent performance…
Block says Dessa learns entirely from human conversation, listens while speaking, and runs on a high-end MacBook, but access remains limited to…
Block’s speech-only research model promises simultaneous listening and talking, but public access and independent performance evidence remain missing.
The downloadable 1-bit model opens up constrained local-AI experiments, but its smaller weights come with lower benchmark scores and no measured speed…
Lex Toumbourou’s documented run reached 81.41% accuracy, but its engineering lessons include restricted networking, host-side credentials, and one human intervention.
Artificial Analysis’s independent leaderboard measures maintainer-defined mergeability, revealing a steep cost premium for small score gains and important limits to the rankings.
Clef and Clef-flash offer bounded decisions for agent workflows, while hands-on fine-tuning precedes a planned self-serve reinforcement-learning platform.
AQVEN combines repository-based workflows, offline checks and agent-driven experiments, but its alpha status and source-available license set important limits.
The complete speech-to-speech checkpoint runs locally, but LAION’s own evaluation finds gains in predicted quality alongside weaker target-voice similarity.
Microsoft’s Qwen-based scoring model offers inexpensive agent control in Foundry, while its headline speed and accuracy results remain vendor-reported.
The 27B dense model favors practical deployment, while the 2.4-trillion-parameter MoE brings Qwen’s largest foundation model to self-hosted infrastructure.
JetBrains has released weights for self-hosted repository work, but GGUF packages and vLLM speculative-decoding support remain on the roadmap.