llmrun

Best LLMs for 16 GB VRAM

Upper mid-range (RTX 4080, RTX 5070 Ti, Arc A770, Apple M4 16GB) — 13B models, some 30B at Q4

← 12 GB VRAM24 GB VRAM →

16 GB is a comfortable mid-range tier for local AI. Most 7B–13B models run smoothly at good quantization levels, and smaller models can run at near-full precision.

This memory tier strikes a nice balance between price and capability. Popular 7B models like Llama 3 8B, Mistral 7B, and Qwen 2.5 7B all run very well at Q4_K_M quantization with fast inference and reasonable context windows. You can also fit some larger 13B models at Q3–Q4, though you'll want to keep context lengths modest. Small models like Phi 3 Mini (3.8B) practically fly at Q8 or even FP16 quality.

Runs Well

  • 7B models at Q4–Q6 quality with good speed
  • Small models (3B–4B) at Q8 or FP16
  • 9B models (Gemma 2 9B) at Q4_K_M

Challenging

  • 13B–14B models need Q3 or lower
  • 30B+ models do not fit in VRAM
  • Long context (>8K tokens) with larger models

GPUs with ~16.0 GB VRAM

All 23 GPUs

AMD Radeon RX 6800 XT

AMD · RDNA 2

16 GB
512.0 GB/s4,608 SP300W TDP$649

AMD Radeon RX 6800

AMD · RDNA 2

16 GB
512.0 GB/s3,840 SP250W TDP$579

AMD Radeon RX 6900 XT

AMD · RDNA 2

16 GB
512.0 GB/s5,120 SP300W TDP$999

AMD Radeon RX 7800 XT

AMD · RDNA 3

16 GB
624.0 GB/s3,840 SP263W TDP$499

NVIDIA GeForce RTX 4060 Ti 16GB

NVIDIA · Ada Lovelace

16 GB
288.0 GB/s4,352 CUDA165W TDP$499

NVIDIA RTX A4000

NVIDIA · Ampere

16 GB
448.0 GB/s6,144 CUDA140W TDP$1,000

Models That Fit in 16 GB VRAM

Speed estimated for NVIDIA GeForce RTX 5080

92 models · 15 good

LLM models ranked by compatibility and performance
ModelVRAMGrade
GPT OSS 20B21.5B
Q4_K_M·47.0 t/s tok/s·131K ctx·GOOD FIT
13.3 GBA77
Qwen1.5 14B14.2B
Q4_K_M·59.5 t/s tok/s·33K ctx·GOOD FIT
10.5 GBA83
Phi 414.7B
Q4_K_M·65.5 t/s tok/s·16K ctx·GOOD FIT
9.5 GBA76
Phi 4 Reasoning14.7B
Q4_K_M·65.5 t/s tok/s·33K ctx·GOOD FIT
9.5 GBA76
Gemma 3 12B IT12.2B
Q4_K_M·77.6 t/s tok/s·33K ctx·GOOD FIT
8.0 GBA65
Gemma 4 12B IT12.0B
Q4_K_M·75.8 t/s tok/s·262K ctx·GOOD FIT
8.2 GBA66
Llama 2 13B Chat HF13.0B
Q4_K_M·72.6 t/s tok/s·GOOD FIT
8.6 GBA69
Qwen 14B14.2B
Q4_K_M·66.7 t/s tok/s·8K ctx·GOOD FIT
9.3 GBA74
Qwen 14B Chat14.2B
Q4_K_M·66.7 t/s tok/s·8K ctx·GOOD FIT
9.3 GBA74
Mistral Nemo Instruct 240712.2B
Q4_K_M·77.3 t/s tok/s·131K ctx·GOOD FIT
8.1 GBA65
Llama 2 13B HF13.0B
Q4_K_M·72.6 t/s tok/s·GOOD FIT
8.6 GBA69
Baichuan2 13B Chat13B
Q4_K_M·72.7 t/s tok/s·GOOD FIT
8.6 GBA69
Baichuan2 13B Base13B
Q4_K_M·72.7 t/s tok/s·GOOD FIT
8.6 GBA69
Vicuna 13B V1.313B
Q4_K_M·72.7 t/s tok/s·2K ctx·GOOD FIT
8.6 GBA69
Baichuan 13B Base13B
Q4_K_M·72.7 t/s tok/s·GOOD FIT
8.6 GBA69
Llama 3.2 11B Vision Instruct10.7B
Q4_K_M·88.6 t/s tok/s·FAIR FIT
7.0 GBB59

Frequently Asked Questions

What models can I run with 16.0 GB VRAM?

With 16.0 GB VRAM, you can run 1195 LLM models at various quantization levels. Popular models that fit well include GPT OSS 20B, Qwen1.5 14B, Phi 4. 4 models achieve excellent performance at this VRAM level. This is the mid-range sweet spot — enough for most popular open-source models without breaking the bank.

Is 16.0 GB enough for local AI?

16.0 GB is a solid mid-range choice for local AI. 1195 models are compatible, with popular 7B models running smoothly at good quality quantizations. It's a great balance of price and capability — enough for daily use with models like Llama 3 8B, Mistral 7B, and smaller 14B models.

What GPU should I get for 16.0 GB VRAM?

Popular GPUs with ~16.0 GB include AMD Radeon RX 6800 XT, AMD Radeon RX 6800, AMD Radeon RX 6900 XT. The NVIDIA GeForce RTX 5080 leads in memory bandwidth at 960.0 GB/s, which translates directly to faster token generation. When choosing a GPU for AI, memory bandwidth matters as much as VRAM capacity — it determines how fast the model can generate text. A newer GPU with the same VRAM but higher bandwidth will produce tokens significantly faster.

Higher memory bandwidth = faster token generation. All these GPUs have approximately 16 GB VRAM, but speed varies significantly by bandwidth.

Memory bandwidth comparison

960 GB/s
900 GB/s
896 GB/s
736 GB/s
732 GB/s
How to choose the right model size for 16.0 GB?

The key rule: your model must fit in VRAM including KV cache overhead. With 16.0 GB, here's a practical guide: 7B models at Q4–Q5 are the sweet spot — fast and high quality. 14B models fit at Q4_K_M but leave less headroom for context. Avoid 30B+ models — they won't fit at usable quality.

Should I get 16.0 GB or 24.0 GB for AI?

Upgrading from 16.0 GB to 24.0 GB gives you significantly more flexibility. At 16.0 GB you can run 1195 models; moving to 24 GB puts you in enthusiast territory with access to 30B+ models and maximum-quality quantizations on smaller models. If budget allows, the extra VRAM is always worth it for AI workloads — you can't add VRAM later.

Best LLMs for 16 GB VRAM | llmrun