FitMyLLM provides independent, daily-updated benchmarks for running large language models locally on a wide range of GPUs and CPUs. The platform is designed to help users identify the best open-source AI model that fits their specific hardware, whether for chat, coding, reasoning, creative tasks, vision, roleplay, agentic applications, or embedding. Users can input their GPU—such as NVIDIA RTX, AMD RX, or Apple Silicon models—and receive instant recommendations for models that match their VRAM capacity, along with speed estimates and ready-to-run commands for deployment.
The tool indexes hundreds of open-source language models, including Llama, Qwen, DeepSeek, Gemma, Phi, and Mistral variants, and benchmarks them across 1,720 GPUs. Each model entry includes detailed performance metrics, such as tokens per second, VRAM requirements at various quantization levels (like Q4_K_M, Q5_K_M, Q6_K, Q8_0, FP16), and results from benchmarks such as MMLU-PRO, HumanEval, MATH, and IFEval. FitMyLLM also catalogs CPUs from Intel, AMD, and Apple Silicon, and provides context length options ranging from 4K to 1M tokens, allowing users to filter models by speed, quality, quantization, and context window.
FitMyLLM caters to a broad range of users, from beginners wanting to try local AI on their computers with minimal setup, to intermediate users seeking to compare models for their specific GPU, to builders planning AI products, and experts evaluating production deployments and total cost of ownership. For beginners, the tool can auto-detect hardware, select an appropriate use case, and generate a single terminal command to launch a local chatbot. Advanced users can filter and compare models side-by-side, plan deployments, and export detailed reports with real TCO calculations.
The service is web-based, requires no signup or account, and is free to use without paywalls or subscriptions. FitMyLLM emphasizes privacy, does not use trackers, and offers a read-only API for live probing and recommendations. It positions itself as an independent benchmarking resource for self-hosted AI, providing quantified, marketing-free data to guide local AI deployment decisions.
FitMyLLM sits in PulseGate's LLM eval & observability category. It enables developers to benchmark and compare AI models, GPUs, and cloud providers for local inference performance. It is built as an open-source project for AI developers and researchers. FitMyLLM is open source under the AGPL-3.0-or-later license. FitMyLLM is available on the web, the command line, and embeddable surfaces.
FitMyLLM first shipped in 2026. PulseGate's similarity index finds few close equivalents — FitMyLLM occupies a relatively distinct niche. Key capabilities include model benchmarking, GPU comparison, and cloud provider comparison.
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