Hardware

16GB GPUs for local AI

Unlocks 20-24B models such as gpt-oss 20B, Mistral Small 3.2 and Devstral Small 2, plus 14B models with long context.

Models that run well on this tier

gpt-oss:20b

21B MoE (3.6B active) · MXFP4 · ~15.5 GB

OpenAI open-weight model; native MXFP4, fits 16GB.

mistral-small3.2

24B · Q4_K_M · ~16.5 GB

Fits 16GB (tight) or 24GB comfortably.

devstral-small-2

24B · Q4_K_M · ~16.5 GB

Agentic coding model built for tools like OpenHands/Cline.

gemma4:12b

12B · Q4_K_M · ~9.5 GB

Newest Gemma at 12B; thinking + tools + vision.

phi4-reasoning

14B · Q4_K_M · ~12.5 GB

Thinking variant of Phi-4.

magistral

24B · Q4_K_M · ~15.5 GB

Mistral's reasoning model.

Check your exact setup in the GPU picker → · Read the full guide →

Hardware picks

NVIDIA GeForce RTX 5060 Ti (16GB)

16GB GDDR7 · bandwidth 448 GB/s

The best-value 16GB NVIDIA card. Make sure you pick the 16GB version, not the 8GB one.

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AMD Radeon RX 9070 XT (16GB)

16GB GDDR6 · bandwidth 640 GB/s

Officially supported by Ollama's ROCm build on Linux, with Vulkan as a fallback. Faster memory than the 5060 Ti.

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NVIDIA GeForce RTX 4060 Ti (16GB)

16GB GDDR6 · bandwidth 288 GB/s

Common on the used market. Narrow memory bus makes it slower, but the 16GB still fits the bigger models.

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Our advice: 16GB is the best price-to-capability tier in 2026. gpt-oss 20B was designed to run in about 16GB of memory.

We don't list prices because they change daily. Bandwidth figures are manufacturer specs. Disclosure: "Check price" links may be affiliate links (rel="sponsored"); we may earn a commission at no cost to you.

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Lila Forge Kits

Local-LLM App Starter

A tested starter for building your own app on Ollama: chat, RAG and a GPU-fit checker. Skip the setup hours.

kits.lilaforge.com → VibeEngine

Best local LLM tools, ranked

Ollama, LM Studio, Open WebUI and llama.cpp scored from public evidence, with top picks.

vibeengine.lilaforge.com →