Sweet spot models
With 16GB VRAM you can comfortably run 7–14B models at Q4_K_M with 4K–8K context. Qwen2.5 7B, Llama 3.1 8B, and DeepSeek-R1-Distill 14B are top picks.
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Qwen2.5 7B Q4_K_M → ~5.4 GB weights + ~2 GB KV @ 4K ctx
Llama 3.1 8B Q4_K_M → ~5.7 GB weights + ~2 GB KV @ 4K ctx
R1-Distill 14B EXL2 4.65bpw → ~9.8 GB total @ 4K ctxUse the VRAM calculator
Always verify with our reverse GPU lookup before downloading a 20GB+ GGUF file.
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https://quantized.uk/tools/vram-calc/?mode=reverse&gpu=rtx4060ti16&ctx=4096&sort=qualityRelated guides
IntermediateEdge / Local9 min read
DeepSeek-R1 Distill 14B: EXL2 vs GGUF
Head-to-head on RTX 4090 — when to pick turboderp EXL2 over bartowski GGUF.
BeginnerEdge / Local9 min read
llama.cpp on Windows with CUDA
Build llama.cpp with NVIDIA GPU support on Windows 11 — the path of least resistance for PC gamers.
BeginnerEdge / Local8 min read
8GB GPU Starter Guide: 3060 / 4060 / 3070
The most common local LLM hardware tier — which models, quants, and context lengths actually fit in 8GB VRAM.
Deployment guides are educational. Each model is subject to its own license — read the official Hugging Face model card before downloading or deploying.