What is the best LLM for 16 GB of VRAM?

16 GB lets you run either a 14B model at high quality (Qwen3 14B at Q6, about 12.7 GB) or a 24-27B model at 4-bit (Mistral Small 24B at Q4_1 or Qwen3.6 27B at IQ4_XS, both about 15 GB). For demanding tasks, a 24B at 4-bit usually beats a 14B at 8-bit.

Can a 16 GB GPU run a 32B model?

A dense 32B does not comfortably fit 16 GB even at low bits without offloading to system RAM. A 30B Mixture-of-Experts model like Qwen3-Coder-30B fits only at an aggressive roughly 3-bit quant. For comfortable 30B use, a 24 GB card is the target.

Is a 24B at 4-bit better than a 14B at 8-bit?

For hard reasoning and coding tasks, a larger 24B model at 4-bit generally outperforms a smaller 14B at 8-bit. On 16 GB both fit, so the size-first choice is usually the stronger one unless you need the crispness of a higher-precision smaller model.

Best LLM for 16 GB of VRAM

By Lefi Abdelmonem

Author · AI Local Check

16 GB of VRAM — an RTX 4060 Ti 16GB, 4070 Ti Super, 5070 Ti or a 16 GB laptop GPU — gives you real flexibility. You can run a 14B model at near-full quality, or step up to a 24–27B model at 4-bit. This is where local AI stops feeling constrained. All figures are measured from real GGUF files.

The best models for 16 GB of VRAM

Model	Size	Best quant that fits	Memory
Qwen3 14B	14.8B	Q6_K	≈ 12.7 GB
Gemma 3 12B	11.8B	Q8_0	≈ 13.9 GB
Mistral Small 24B	23.6B	Q4_1	≈ 15.3 GB
Qwen3.6 27B	26.9B	IQ4_XS	≈ 15.1 GB
DeepSeek-Coder-V2-Lite	15.7B MoE	Q6_K_L	≈ 15.2 GB

Memory is weights plus KV cache and system margin at a 4,096-token context.

What to pick

You have two good strategies at 16 GB:

Quality-first: run a 12–14B model at a high quantization. Qwen3 14B at Q6 or Gemma 3 12B at Q8 give you essentially the full model with a comfortable context.
Size-first: step up to a 24–27B model at 4-bit. Mistral Small 24B (Q4_1 ≈ 15 GB) and Qwen3.6 27B (IQ4_XS ≈ 15 GB) bring noticeably more capability, at the cost of running near 4-bit.

For most people the size-first route wins: a 24B at 4-bit generally beats a 14B at 8-bit on hard tasks.

Can 16 GB run a 32B?

A dense 32B (like Qwen2.5-Coder-32B) doesn't quite fit 16 GB even at low bits without offloading. A 30B MoE such as Qwen3-Coder-30B only fits at an aggressive ~3-bit quant. For comfortable 30B+ use, a 24 GB card is the target.

Tips for 16 GB

Prefer a 24B at 4-bit over a 14B at 8-bit for demanding work — the larger model usually reasons better.
IQ4_XS is your friend. This i-quant squeezes a 27B into 16 GB with minimal quality loss versus Q4_K_M.
Watch the context. Long contexts eat into your 4-bit headroom on the 24B+ options.

Size any model to your card with the calculator, or compare tiers: 12 GB · 24 GB · 32 GB.

→ Check what your exact GPU can run