Best LLM for Your VRAM (8, 12, 16, 24 GB)
Author · AI Local Check
Match the model to your VRAM
Graphics memory capacity directly limits the maximum model size that can operate efficiently. Larger models require more memory to store parameters and intermediate computations during inference. When a model exceeds available memory, the system degrades performance by offloading data to slower system memory. Matching model size to graphics memory ensures smooth operation without resource contention.
| Your VRAM | Biggest model (Q4) | All sizes that fit | Top pick |
|---|---|---|---|
| 8 GB | 8B | 1B, 3B, 8B | Meta-Llama-3.1-8B-Instruct |
| 12 GB | 14B | 1B, 3B, 8B, 14B | Qwen2.5-14B-Instruct |
| 16 GB | 14B | 1B, 3B, 8B, 14B | Qwen2.5-14B-Instruct |
| 24 GB | 32B | 1B, 3B, 8B, 14B, 32B | Qwen2.5-32B-Instruct |
See the full list for your exact GPU
Bigger isn't always better
Newer, smaller models can occasionally match or exceed older, larger counterparts despite their size. This occurs when efficiency improvements and architectural refinements in newer designs compensate for reduced parameter counts. Leaving memory headroom during deployment accelerates inference and supports longer context retention, offering practical advantages in resource-constrained environments.
Leave room for context
Model weights are not the only memory consideration when running AI models locally. Longer prompts increase activation storage demands as the model processes more input data simultaneously. This additional load varies with input length, requiring available memory beyond what weights alone consume. Reserving headroom ensures smooth operation under fluctuating computational needs.
Frequently asked questions
What is the best LLM for 8 GB of VRAM?
Generally the largest model that still fits at a balanced 4-bit quantization, as shown in the table above. Smaller, newer models often beat older larger ones.
Should I always pick the biggest model that fits?
Not necessarily. A smaller, more recent model can outperform a larger older one and leaves more memory for longer context and faster responses.
Does more VRAM always mean better results?
It lets you run larger models or longer contexts, but the model's quality and how well it suits your task matter just as much as raw size.