Best local LLM for 16GB

Short answer: Mistral Small 24B Instruct at AWQ INT4 for general use — 13.2 GB of 16GB, leaving 2.8 GB. Also: Devstral Small 1.1 24B (Coding), Magistral Small 1.2 24B (Images).

“Best” here means the largest model in its category that clears the card with headroom left, at the highest-quality quant that still fits. This index measures memory and published perplexity, not task quality — it cannot tell you which model answers better, and does not pretend to.

The picks

For	Model	Quant	Est. VRAM	Headroom	Loss	Why
General	Mistral Small 24B Instruct24B	AWQ INT4	13.2 GB	2.8 GB	3.8%	The largest model of any kind that fits this budget. At AWQ INT4 it gives up 3.8% perplexity against FP16. Only at 4K context — a longer window does not fit.
Coding	Devstral Small 1.1 24B24B	AWQ INT4	13.0 GB	3.0 GB	4.0%	The largest in its category that still leaves headroom. At AWQ INT4 it gives up 4.0% perplexity against FP16. Only at 4K context — a longer window does not fit.
Images	Magistral Small 1.2 24B24B	AWQ INT4	13.0 GB	3.0 GB	4.0%	The largest in its category that still leaves headroom. At AWQ INT4 it gives up 4.0% perplexity against FP16. Only at 4K context — a longer window does not fit.

Sized at 4K context with a 10% activation buffer; 64 of 112 models in the index clear this budget comfortably. Loss is the quantizer’s published perplexity figure against FP16, or a dash where none was published.

Cards this applies to

What fits is decided by capacity alone, so every card here returns the same list. What differs is how fast it reads the weights — each card’s page states its bandwidth and what that costs you.

What does not fit

The nearest miss is Qwen3 32B Instruct. Its smallest build in this index, EXL2 3.5bpw, needs about 16.1 GB — 0.1 GB over 16GB, before any context beyond 4K. Offloading to system RAM will load it at a speed set by your RAM, which is a different machine rather than a smaller quant.

Pushing context

At 4K context 64 models clear this budget. At 16K it is 58, and at 32K it is 52. The weights do not change — the KV cache is what grows.

Check your exact context length

Common questions

What is the best local LLM for 16 GB of VRAM in 2026?

Mistral Small 24B Instruct at AWQ INT4 for general use — 13.2 GB of 16 GB, leaving 2.8 GB. Devstral Small 1.1 24B if you want code, Magistral Small 1.2 24B if you want images. "Best" here means the largest model in its category that clears the card with headroom — this index measures memory and published perplexity, not task quality, so it cannot rank models on how well they answer.

How many models fit 16 GB?

64 of the 112 models in this index clear it comfortably at 4K context — comfortably meaning the estimate uses at most 88% of the memory, so there is margin for the display and the runtime. At 16K that falls to 58, and at 32K to 52: the KV cache is what moves, not the weights.

What will not run on 16 GB?

The nearest miss is Qwen3 32B Instruct: its smallest build here, EXL2 3.5bpw, needs about 16.1 GB — 0.1 GB over, before any context beyond 4K. Offloading part of it to system RAM will load it, at a speed set by your RAM rather than your card; that is a different machine, not a smaller quant.

Do all 16 GB cards run the same models?

They hold the same models — what fits is decided by capacity alone, so all 16 cards at this tier return this same list. What differs is how fast they read the weights: token generation reads the whole weight set once per token, so memory bandwidth sets the ceiling, and two cards with identical memory can be more than 2.5× apart on it. Each card's own page states its bandwidth and what that implies.