What's My AILanding page built from search-intent evidence, reference assumptions, and a benchmark-first path.

GPU cohort

What local AI can I run on an RTX 4060 laptop GPU?

People comparing the most common gaming-laptop GPU tier against serious local AI without buying a bigger machine. This page gives the short local-AI answer for the search query, then routes uncertain cases into the benchmark before the recommendation turns into guesswork.

gpu cohort8 GB laptop VRAM class
starter modelgpt-oss-20b
tier guide34B
publish waveWeek 1

benchmark first

Verify this class before you trust the reference answer.

This tier is real for local AI, but the benchmark still matters before you assume it behaves like a desktop 4060 or better. The benchmark is what turns this cohort answer into a machine-specific decision before you spend time downloading models that do not fit.

starter models

Best first models for this cohort

gpt-oss-20b

34B class • 15.5 GB minimum

gpt-oss-20b is the clearest midrange American local-model pick when you want a serious reasoning assistant without jumping straight into a 32B-class package.

Open model page

OLMo 3.1 Instruct 32B

34B class • 19.5 GB minimum

OLMo 3.1 32B is the strongest Apache-licensed American 32B-class option, but it asks for more memory than gpt-oss-20b to reach a clean first run.

Open model page

Granite 4.0 H-Small

34B class • 19.5 GB minimum

Granite 4.0 H-Small is a credible American midrange choice for RAG-heavy work, but it is more specialized than the general-purpose winners above it.

Open model page

why this page ranks

Query evidence and benchmark path

  • Organic traffic signal: High demand for the query cluster aroundrtx 4060 laptop local ai”.
  • Search intent review: GPU cohort searches are high intent when the page explains model size, memory headroom, and when to benchmark anyway.
  • Benchmark completion potential: High. These searches are close enough to a hardware decision that a benchmark CTA consistently belongs above the fold.

runtime paths

Pick the runtime after you confirm the hardware band

The benchmark decides the size band first. The runtime pages then tell you which download path is the cleanest first move inside Ollama, LM Studio, or llama.cpp.

P0Static

Runtime page

Best local models for Ollama

Search intent: ollama best model

Best for the quickest path from benchmark result to a real local run.

Runtime guide + catalog coverage

Open page
P1Static

Runtime page

Best local models for llama.cpp

Search intent: llama.cpp best model

Best for people who care about low-level control, serving flags, and GGUF tuning.

Runtime guide + catalog coverage

Open page

nearby pages

Adjacent queries to open next