How to Set Up Local AI on 24GB of VRAM

A 24GB GPU's headline model is Ornith 1.0 35B: it needs about 20GB at Q4 quantization and scores 75.6% on SWE-bench Verified, one of the highest results for any open-weight coding model. Gemma 3 27B (about 15GB) and every lighter model fit too, leaving room for longer context windows.

Written by Priya Raghunathan, local-AI hardware reviewer. I test consumer GPUs and Apple Silicon against real llama.cpp and MLX benchmarks before recommending them, because vendor VRAM numbers rarely match what actually loads.

What actually fits in 24GB

Which quantization to pick

GGUF at Q4_K_M through Ollama is the easiest path and works on any 24GB card, NVIDIA or AMD. If your whole model fits in VRAM with no CPU offload, which it does at this tier, the older int4 formats (GPTQ, AWQ, EXL2) served through vLLM or text-generation-webui can run faster than GGUF on NVIDIA cards, since they are built for all-on-GPU inference rather than the run-anywhere flexibility GGUF trades for. See the GGUF vs MLX vs NVFP4 explainer for when each format is worth the extra setup.

Install and run

Real-world speed: what to expect

Hardware Corner's real-hardware GPU LLM benchmarks (llama.cpp's llama-bench on Ubuntu 24.04, updated December 2025, Q4_K_XL quantization, 16K context) measured both 24GB cards on a 30B-class dense model, the closest tested size to Ornith 1.0 35B and Gemma 3 27B:

The RTX 3090 lands within roughly 15-20% of the RTX 4090 across every size tested, which matches the general finding that VRAM capacity, not raw compute, is what decides which models run on a given card. A used 3090 covers the identical model list as a 4090 at a fraction of the price.

Which 24GB card should I buy?

• RTX 3090 (used, ~24GB): two generations old and out of production, but VRAM matters more than raw speed for local inference, so it fits the same model list as newer cards.
• RTX 4090 24GB (~$2,755, discontinued): the fastest single-card option tested above, now scarce and priced accordingly.
• AMD RX 7900 XTX 24GB (~$1,339): same 24GB ceiling, 960 GB/s bandwidth, runs well through llama.cpp's Vulkan/HIP backend without a full ROCm install.

Where to go from here

• Need a 70B-class model? That needs about 48GB, see the Mac M4 Pro 48GB setup guide.
• Coming from 16GB? The 16GB VRAM setup guide covers Gemma 3 27B and gpt-oss-20b.
• Comparing cards? Best Local LLM for the RTX 3090 has a single-card deep dive.