Best GPU for local LLMs: 16GB
16 GB adds room for gpt-oss-20b at its native precision and longer conversations on 14B models. The model list below comes from the same data as our 16 GB tier page. Specs are copied from NVIDIA's and AMD's own product pages.
NVIDIA
GeForce RTX 5060 Ti 16GB
- Memory: 16 GB GDDR7
- Memory bus: 128-bit
- Power: 180 W total graphics power
The lowest-power NVIDIA card with 16 GB. Make sure the listing says 16 GB: an 8 GB version also exists.
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Specs: NVIDIA product page, checked 2026-10-31.
NVIDIA
GeForce RTX 5070 Ti
- Memory: 16 GB GDDR7
- Memory bus: 256-bit
- Power: 300 W total graphics power
Same 16 GB, but a 256-bit bus (twice as wide as the 5060 Ti), which helps token speed.
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Specs: NVIDIA product page, checked 2026-10-31.
AMD
Radeon RX 9060 XT 16GB
- Memory: 16 GB GDDR6
- Memory bus: 128-bit
- Power: 160 W typical board power
AMD's 16 GB option. Check that your app supports AMD GPUs (llama.cpp and Ollama do) before buying.
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Specs: AMD product page, checked 2026-10-31.
AMD
Radeon RX 9070 XT
- Memory: 16 GB GDDR6
- Memory bus: 256-bit
- Power: 304 W typical board power
AMD's 16 GB card with a 256-bit bus. Same AMD software check applies.
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Specs: AMD product page, checked 2026-10-31.
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What fits in 16 GB
11 of the 30 language models in our calculator fit comfortably in 16 GB with an 8K-token conversation:
| Model | Best precision | Memory |
|---|---|---|
| gpt-oss-20b | 16-bit (FP16/BF16) | 14.0 GB |
| Qwen2.5 14B Instruct | 4-bit (Q4_K_M) | 10.6 GB |
| Qwen3 14B | 4-bit (Q4_K_M) | 10.3 GB |
| DeepSeek-R1-0528-Qwen3-8B | 8-bit (Q8_0) | 9.9 GB |
| Llama 3.1 8B Instruct | 8-bit (Q8_0) | 9.6 GB |
| Nemotron 3.5 Bolt E4B | 8-bit (Q8_0) | 8.2 GB |
| Qwen2.5 7B Instruct | 8-bit (Q8_0) | 8.6 GB |
| Mistral 7B Instruct v0.3 | 8-bit (Q8_0) | 8.8 GB |
| Spark-X2.6 4B | 16-bit (FP16/BF16) | 8.5 GB |
| Llama 3.2 3B Instruct | 16-bit (FP16/BF16) | 7.4 GB |
| LFM3 1.7B | 16-bit (FP16/BF16) | 3.6 GB |
Same data and rule as What can I run with 16 GB?: real Hugging Face download sizes plus FP16 KV cache, under 90% of 16 GB. Data checked 2026-10-30.
How to choose
- VRAM decides what fits. Every card here has 16 GB, so all of them hold the same models.
- A wider memory bus usually means faster tokens. Generating text mostly reads the weights from memory, so at the same VRAM a 256-bit card is usually faster than a 128-bit one. See the measured speeds we've collected.
- Check power and your app. Make sure your power supply can handle the card. NVIDIA cards work with nearly every local AI app. On AMD, check your app's support first.
- Renting may be cheaper. For a few hours a month, compare cloud GPU prices before you buy.