L LocalNodeOps

About LocalNodeOps

LocalNodeOps estimates what local LLM hardware can run. It exists because much of the "which GPU should I buy for local AI" advice online is outdated, unverified, or copied from a vendor spec sheet. This site tries to close that gap: every figure is labeled by where it comes from (a third-party measurement, a published paper, or a calculation), and the estimation math is shown rather than hidden.

Muhammad Haseeb Ur Rehman

Lahore, Punjab, Pakistan

Full Stack + AI Engineer | React.js • Next.js • Node.js • Python | AI-Powered Applications | LLM Integrations | Building Scalable Web Solutions

Built and maintained by Muhammad Haseeb Ur Rehman (@haseebio), a Full-Stack & AI Engineer.

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How this site operates

Experience

Benchmark figures are labeled verified (measured directly on hardware we own or have direct access to), community-reported (submitted by someone else, not independently verified), or estimated (architectural reasoning and published specs, not yet measured on physical hardware) — the three are never blended into one number, and none is presented as more certain than it is.

Expertise

Coverage focuses on the practical side of local inference: quantization trade-offs, VRAM budgeting, and diagnosing failures in CUDA, vLLM, Ollama, and llama.cpp — the tools people actually run in production and at home.

Trustworthiness

Benchmark methodology, hardware specs, and software versions are published alongside every result. Corrections are dated, not silently edited. This site does participate in affiliate programs with select cloud GPU providers and with Amazon Associates — disclosed inline wherever a link appears, not just buried in Terms — and those relationships never influence the calculator’s math or which local hardware profiles a configuration is shown to fit.

Methodology, in practice

  • Every figure is tagged verified (measured directly on hardware we control), community-reported (submitted by someone else, not independently verified), or estimated (derived from published specs and architectural reasoning, not yet measured) — the three are never blended into one number.
  • The VRAM calculator states plainly which parts of its output are exact (synced file sizes) and which are estimated (KV cache math) — see its own footnote for the breakdown.
  • Hardware specs and software versions are published next to results, not buried in a separate methodology page nobody reads.
  • Corrections are dated when made, not quietly rewritten.
  • Affiliate links (RunPod and Amazon Associates) appear in two kinds of places: a labeled suggestion in the calculator (a cloud GPU when a configuration exceeds local hardware, an Amazon link or a rental line when it fits) and inside some posts and docs, with a disclosure. They never change the VRAM math itself, which is computed identically with or without an affiliate relationship.

How the VRAM math works

  • Total VRAM = (Weights + KV Cache) × 1.10 The extra 10% is an estimate of runtime overhead, not a measurement.
  • Q4_K_M quantization = 4.83 bits per weight Used for generic estimates. When you pick a synced model, its real file size is used instead, and real files vary by model.
  • macOS Metal GPU allocation cap = about 75% of system RAM The usual default on Apple Silicon. It varies by macOS version and can be raised.
  • System RAM kept for the operating system = 4 GB Subtracted before counting RAM for CPU offload.

Not included: the calculator does not subtract memory used by your desktop or display from VRAM. If the same GPU drives your screen, leave some VRAM free.

Found something wrong, or want a specific model benchmarked?

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