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
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 GPUsAMD Radeon RX 6800 XT
AMD · RDNA 2
AMD Radeon RX 6800
AMD · RDNA 2
AMD Radeon RX 6900 XT
AMD · RDNA 2
AMD Radeon RX 7800 XT
AMD · RDNA 3
NVIDIA GeForce RTX 4060 Ti 16GB
NVIDIA · Ada Lovelace
NVIDIA RTX A4000
NVIDIA · Ampere
Models That Fit in 16 GB VRAM
Speed estimated for NVIDIA GeForce RTX 5080
92 models · 15 good
| Model | Quant | VRAM | Speed | Context | Status | Grade |
|---|---|---|---|---|---|---|
Q4_K_M·122.4 t/s tok/s·33K ctx·FAIR FIT | Q4_K_M | 5.1 GB | 122.4 t/s | 33K | FAIR FIT | B47 |
Q4_K_M·120.5 t/s tok/s·FAIR FIT | Q4_K_M | 5.2 GB | 120.5 t/s | — | FAIR FIT | B47 |
Q4_K_M·123.8 t/s tok/s·262K ctx·FAIR FIT | Q4_K_M | 5.0 GB | 123.8 t/s | 262K | FAIR FIT | B47 |
Q4_K_M·157.6 t/s tok/s·33K ctx·EASY RUN | Q4_K_M | 4.0 GB | 157.6 t/s | 33K | EASY RUN | C40 |
Q4_K_M·215.2 t/s tok/s·41K ctx·EASY RUN | Q4_K_M | 2.9 GB | 215.2 t/s | 41K | EASY RUN | C34 |
Q4_K_M·181.9 t/s tok/s·131K ctx·EASY RUN | Q4_K_M | 3.4 GB | 181.9 t/s | 131K | EASY RUN | C36 |
IQ2_M·42.4 t/s tok/s·131K ctx·POOR FIT | IQ2_M | 14.7 GB | 42.4 t/s | 131K | POOR FIT | C44 |
Q4_K_M·125.1 t/s tok/s·4K ctx·FAIR FIT | Q4_K_M | 5.0 GB | 125.1 t/s | 4K | FAIR FIT | B46 |
Q4_K_M·215.2 t/s tok/s·262K ctx·EASY RUN | Q4_K_M | 2.9 GB | 215.2 t/s | 262K | EASY RUN | C34 |
Q4_K_M·130.5 t/s tok/s·66K ctx·FAIR FIT | Q4_K_M | 4.8 GB | 130.5 t/s | 66K | FAIR FIT | B45 |
Q4_K_M·173.8 t/s tok/s·EASY RUN | Q4_K_M | 3.6 GB | 173.8 t/s | — | EASY RUN | C37 |
Q4_K_M·183.5 t/s tok/s·4K ctx·EASY RUN | Q4_K_M | 3.4 GB | 183.5 t/s | 4K | EASY RUN | C36 |
Q4_K_M·153.3 t/s tok/s·4K ctx·EASY RUN | Q4_K_M | 4.1 GB | 153.3 t/s | 4K | EASY RUN | C40 |
Q4_K_M·135.1 t/s tok/s·4K ctx·EASY RUN | Q4_K_M | 4.6 GB | 135.1 t/s | 4K | EASY RUN | C44 |
Q4_K_M·219.7 t/s tok/s·EASY RUN | Q4_K_M | 2.8 GB | 219.7 t/s | — | EASY RUN | C34 |
Q4_K_M·217.4 t/s tok/s·131K ctx·EASY RUN | Q4_K_M | 2.9 GB | 217.4 t/s | 131K | EASY RUN | C34 |
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/s900 GB/s896 GB/s736 GB/s732 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.