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 13 GPUsNVIDIA RTX A4000
NVIDIA · Ampere
Intel Arc A770 16GB
Intel · Alchemist
AMD Radeon RX 6800
AMD · RDNA 2
AMD Radeon RX 6800 XT
AMD · RDNA 2
AMD Radeon RX 6900 XT
AMD · RDNA 2
AMD Radeon RX 7800 XT
AMD · RDNA 3
Models That Fit in 16 GB VRAM
Speed estimated for NVIDIA GeForce RTX 5080
| Model | Quant | VRAM | Speed | Context | Status | Grade |
|---|---|---|---|---|---|---|
| Q4_K_M | 5.4 GB34% | 115.8 t/s | 131K | FAIR FIT | B49 | |
| Q4_K_M | 4.9 GB31% | 126.8 t/s | 33K | FAIR FIT | B46 | |
| Q8_0 | 4.9 GB31% | 127.1 t/s | 4K | FAIR FIT | B46 | |
| Q4_K_M | 5.0 GB31% | 125.1 t/s | 131K | FAIR FIT | B46 | |
| Q4_K_M | 2.9 GB18% | 215.9 t/s | 41K | EASY RUN | C34 | |
| Q4_K_M | 2.6 GB17% | 236.4 t/s | 2K | EASY RUN | C34 | |
| Q4_K_M | 2.0 GB12% | 315.2 t/s | 131K | EASY RUN | C31 | |
| Q4_K_M | 2.9 GB18% | 218.9 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 most 7B-13B models at good Q4-Q5 quality, and some larger 14B models at lower quantization.
- Is 16.0 GB enough for local AI?
16.0 GB is a solid mid-range choice for local AI. Most popular 7B models run smoothly, and you can experiment with larger models at lower quantizations. A great balance of price and capability.
- What GPU should I get for 16.0 GB VRAM?
There are several GPUs with approximately 16.0 GB VRAM at different price points. Popular choices include NVIDIA RTX A4000, Intel Arc A770 16GB, AMD Radeon RX 6800. Memory bandwidth also matters — higher bandwidth means faster token generation. Check the GPU cards above for specific specs and pricing.
- What quantization works best with 16.0 GB?
For 16.0 GB, Q4_K_M is typically the best starting quantization — it offers a good balance of model quality and VRAM usage. You can also try Q5_K_M or Q6_K for better quality with 7B models. Use Q2_K or Q3_K_M only when you need to squeeze in a model that's otherwise too large.