16 GB comfortably runs models up into the mid-teens-billion range at 4-bit with room for context, and lets you run 7-13B models at high quants — a strong, well-balanced tier for local AI.
A 16 GB graphics card — such as an RTX 4060 Ti 16 GB, a 4070 Ti Super, or a 16 GB Mac's shared memory — is a strong, well-balanced tier for local AI. It runs the popular workhorse models comfortably and reaches into more capable territory, which makes it a satisfying place to be. Here's what the 16 GB budget actually gets you.
Using the standard rule — about 0.5 GB per billion parameters at 4-bit for the weights, plus a few gigabytes of headroom for the KV cache, runtime, and OS — 16 GB comfortably holds models up into the mid-teens-billion range at 4-bit, and runs 7-13B models with generous room to spare. That headroom is valuable: on a 7-13B model you can run a higher quant (Q5, Q6, even Q8 for the smaller ones) for better quality, or use a long context, without running out of memory. So 16 GB isn't just about a bigger maximum model — it's about running the useful sizes at their best settings.
The practical picture: 7-13B models become very comfortable, with freedom on quant and context; mid-teens-billion models (around 14B) fit at 4-bit; and you can experiment with slightly larger models using aggressive quantization or a GPU/CPU split. This tier covers the great majority of what individuals want for chat, coding, writing, and summarization, at quality settings that do the models justice. It's a genuine step into "capable local AI" rather than just "local AI that works."
The honest limits: 16 GB stops short of the 24 GB you'd want to comfortably run 32B-class models, and it can't hold large or flagship models. Long contexts still grow the KV cache, so a mid-size model with a very long context can push against 16 GB — the usual levers (smaller quant, shorter context, GPU/CPU split for overflow) apply. If you specifically want 32B-class models, that's the case for stepping up to a 24 GB card.
A note for Mac owners: a 16 GB Apple Silicon Mac uses unified memory, so that 16 GB is shared with macOS and your apps rather than dedicated to the GPU. The effective model budget is therefore a bit tighter than a dedicated 16 GB PC card, since the OS takes its share. It still runs 7-13B models well, but keep the shared-memory reality in mind when a model is near the edge.
The cost angle: a 16 GB card runs these models for effectively $0 in compute beyond electricity, fully private and offline. For personal use of 7-14B models at high quality, that's very cheap once you own the hardware — and 16 GB hits a nice balance of capability and card cost. Bigger ambitions (32B-class and up) point to more VRAM, a rented GPU, or a pay-per-token API with your own key.
Spanvero makes "what fits 16 GB" an objective, computed answer at each model's default quant and a realistic context. See the full ranked list of models that fit at /models/16gb-vram/ and the picks at /best/best-llm-for-16gb-vram/; browse the neighboring tiers at /models/8gb-vram/ and /models/24gb-vram/; and use /calculator/ to check a specific model, quant, and context against 16 GB and its honest local cost. If you're weighing a card upgrade, compare the tiers in the guides at /learn/what-can-i-run-on-12gb-vram/ and /learn/what-can-i-run-on-24gb-vram/.
What LLMs can I run on 12GB of VRAM? · What LLMs can I run on 24GB of VRAM? · VRAM · Can I run Llama 3 on a MacBook? · What quantization should I use? · What GPU should I buy for running local LLMs? · Quantization · KV cache
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