Best GPU for local LLMs: 32GB
32 GB fits 32B to 35B models at 4-bit, plus Mixtral 8x7B, with an 8K-token conversation. The model list below comes from the same data as our 32 GB tier page. Specs are copied from NVIDIA's and AMD's own product pages.
NVIDIA
GeForce RTX 5090
- Memory: 32 GB GDDR7
- Memory bus: 512-bit
- Power: 575 W total graphics power
The only consumer GeForce card with 32 GB. Needs a 1000 W power supply per NVIDIA.
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Specs: NVIDIA product page, checked 2026-10-31.
AMD
Radeon AI PRO R9700
- Memory: 32 GB GDDR6
- Memory bus: 256-bit
- Power: 300 W total board power
A 32 GB AMD workstation card at about half the 5090's power. Check your app's AMD support first.
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Specs: AMD product page, checked 2026-10-31.
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What fits in 32 GB
22 of the 30 language models in our calculator fit comfortably in 32 GB with an 8K-token conversation:
| Model | Best precision | Memory |
|---|---|---|
| Mixtral 8x7B Instruct v0.1 | 4-bit (Q4_K_M) | 27.5 GB |
| Recursive Flint | 4-bit (Q4_K_M) | 25.3 GB |
| Qwen3.6 35B-A3B | 4-bit (Q4_K_M) | 22.5 GB |
| Qwen3.8-35B-A3B (Empero Distill) | 4-bit (Q4_K_M) | 21.9 GB |
| Nanbeige4.3 33B | 4-bit (Q4_K_M) | 22.5 GB |
| Qwen2.5 32B Instruct | 4-bit (Q4_K_M) | 22.0 GB |
| Qwen3 32B | 4-bit (Q4_K_M) | 21.9 GB |
| Gemma 4 31B | 4-bit (Q4_K_M) | 19.6 GB |
| Qwen3.8 27B | 4-bit (Q4_K_M) | 18.0 GB |
| Gemma 4 26B A4B | 8-bit (Q8_0) | 26.9 GB |
| LFM2.5-24B-A2B | 8-bit (Q8_0) | 25.7 GB |
| gpt-oss-20b | 16-bit (FP16/BF16) | 14.0 GB |
| Qwen2.5 14B Instruct | 8-bit (Q8_0) | 17.3 GB |
| Qwen3 14B | 8-bit (Q8_0) | 17.0 GB |
| DeepSeek-R1-0528-Qwen3-8B | 16-bit (FP16/BF16) | 17.6 GB |
| Llama 3.1 8B Instruct | 16-bit (FP16/BF16) | 17.1 GB |
| Nemotron 3.5 Bolt E4B | 16-bit (FP16/BF16) | 15.1 GB |
| Qwen2.5 7B Instruct | 16-bit (FP16/BF16) | 15.7 GB |
| Mistral 7B Instruct v0.3 | 16-bit (FP16/BF16) | 15.6 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 32 GB?: real Hugging Face download sizes plus FP16 KV cache, under 90% of 32 GB. Data checked 2026-10-30.
How to choose
- VRAM decides what fits. Every card here has 32 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.