Best LLMs for 32 GB of VRAM
162 open-weights models run on a GPU with 32 GB of VRAM and 32 GB of system RAM, 140 of them fully on the GPU. The strongest by benchmark score: Qwen 3.8 27B, Qwen3.6 27B, Gemma 4 31B IT.
Each row shows the best quantization that fits, where it runs and a speed estimate at a 16K context, assuming a card with about 1792 GB/s of memory bandwidth. Bigger mixture-of-experts models can keep their experts in system RAM, which is why some run well above 32 GB. Other sizes: 6 GB · 8 GB · 12 GB · 16 GB · 24 GB.
Ranked by benchmarks
| # | Model | Lab | Released | Size | ECI | Best quant | Runs | tok/s |
|---|---|---|---|---|---|---|---|---|
| 1 | Qwen 3.8 27B | Qwen | 2026-08-14 | 28B | 152.6 | Q6_K | fully on GPU | ~45 |
| 2 | Qwen3.6 27B | Qwen | 2026-04-21 | 28B | 146.7 | Q6_K | fully on GPU | ~45 |
| 3 | Gemma 4 31B IT | 2026-04-02 | 31B | 144.3 | Q6_K | fully on GPU | ~41 | |
| 4 | Qwen 3.6 35B-A3B | Qwen | 2026-04-14 | 36B MoE | 144.0 | Q6_K | fully on GPU | ~300 |
| 5 | Qwen3.5-35B-A3B | Qwen | 2026-02-24 | 36B MoE | 144.0 | Q6_K | fully on GPU | ~300 |
| 6 | Qwen3-235B-A22B-Thinking (Jul 2025) | Qwen | 2025-07-25 | 235B MoE | 143.2 | IQ1_M | GPU + experts in RAM | ~20 |
| 7 | Gemma 4 26B A4B | 2026-04-02 | 26B MoE | 141.6 | Q6_K | fully on GPU | ~250 | |
| 8 | gpt-oss-120b | OpenAI | 2025-08-05 | 117B MoE | 140.2 | IQ3_XXS | GPU + experts in RAM | ~50 |
| 9 | Qwen3.5-9B | Qwen | 2026-02-24 | 9.7B | 139.3 | Q6_K | fully on GPU | ~120 |
| 10 | Qwen3-235B-A22B | Qwen | 2025-04-28 | 235B MoE | 138.4 | IQ1_M | GPU + experts in RAM | ~18 |
| 11 | Qwen3-30B-A3B-Thinking (Jul 2025) | Qwen | 2025-07-30 | 30B MoE | 138.3 | Q6_K | fully on GPU | ~300 |
| 12 | Qwen3-235B-A22B-Instruct (Jul 2025) | Qwen | 2025-07-25 | 235B MoE | 138.1 | IQ1_M | GPU + experts in RAM | ~20 |
| 13 | Qwen3-32B | Qwen | 2025-04-29 | 33B | 136.9 | Q5_K_M | fully on GPU | ~44 |
| 14 | QwQ-32B | Qwen | 2025-03-05 | 32B | 136.5 | Q6_K | fully on GPU | ~39 |
| 15 | Qwen3-14B | Qwen | 2025-04-29 | 15B | 136.1 | Q6_K | fully on GPU | ~82 |
| 16 | Qwen3-30B-A3B-Instruct (Jul 2025) | Qwen | 2025-07-29 | 30B MoE | 135.2 | Q6_K | fully on GPU | ~300 |
| 17 | gpt-oss-20b | OpenAI | 2025-08-05 | 21B MoE | 135.0 | Q6_K | fully on GPU | ~240 |
| 18 | Qwen3-30B-A3B | Qwen | 2025-04-29 | 31B MoE | 134.1 | Q6_K | fully on GPU | ~300 |
| 19 | Qwen3-8B | Qwen | 2025-04-28 | 8.2B | 132.1 | Q6_K | fully on GPU | ~140 |
| 20 | Phi-4 | Microsoft | 2024-12-12 | 15B | 129.0 | Q6_K | fully on GPU | ~82 |
| 21 | Gemma 3 27B | 2025-03-12 | 27B | 127.5 | Q6_K | fully on GPU | ~46 | |
| 22 | Qwen2.5-72B | Qwen | 2024-09-19 | 73B | 127.2 | IQ2_M | fully on GPU | ~42 |
| 23 | Llama 3.3 70B | Meta | 2024-12-06 | 70B | 126.1 | IQ2_M | fully on GPU | ~44 |
| 24 | Llama 3.1-70B | Meta | 2024-07-23 | 71B | 124.7 | IQ2_M | fully on GPU | ~43 |
| 25 | Llama 3.2 90B | Meta | 2024-09-24 | 90B | 122.8 | IQ2_XXS | fully on GPU | ~44 |
| 26 | Qwen2-72B | Qwen | 2024-06-07 | 73B | 122.3 | IQ2_M | fully on GPU | ~42 |
| 27 | Llama 3-70B | Meta | 2024-04-18 | 70B | 121.2 | IQ2_M | fully on GPU | ~44 |
| 28 | Qwen2.5-Coder-32B | Qwen | 2024-09-18 | 32B | 118.9 | Q6_K | fully on GPU | ~39 |
| 29 | Gemma 2 27B | 2024-06-24 | 27B | 118.6 | Q6_K | fully on GPU | ~46 | |
| 30 | phi-3-small 7.4B | Microsoft | 2024-04-23 | 7.4B | 116.5 | Q6_K | fully on GPU | ~150 |
| 31 | phi-3-medium 14B | Microsoft | 2024-04-23 | 14B | 114.3 | Q6_K | fully on GPU | ~86 |
| 32 | Mistral NeMo | Mistral | 2024-07-18 | 12B | 113.9 | Q6_K | fully on GPU | ~97 |
| 33 | Mixtral 8x7B | Mistral | 2023-12-11 | 45B | 113.2 | IQ4_XS | fully on GPU | ~43 |
| 34 | Gemma 2 9B | 2024-06-24 | 9.2B | 112.7 | Q6_K | fully on GPU | ~120 | |
| 35 | Llama 3.1-8B | Meta | 2024-07-23 | 8B | 111.1 | Q6_K | fully on GPU | ~140 |
| 36 | phi-3-mini 3.8B | Microsoft | 2024-04-23 | 3.8B | 110.6 | Q6_K | fully on GPU | ~260 |
| 37 | Llama 3-8B | Meta | 2024-04-18 | 8B | 109.4 | Q6_K | fully on GPU | ~140 |
| 38 | Llama 2-70B | Meta | 2023-07-18 | 70B | 105.8 | IQ2_M | fully on GPU | ~44 |
| 39 | Gemma 7B | 2024-02-21 | 7B | 103.3 | Q6_K | fully on GPU | ~160 | |
| 40 | Mistral 7B v0.1 | Mistral | 2023-09-27 | 7B | 102.8 | Q6_K | fully on GPU | ~160 |
| 41 | Qwen-14B | Qwen | 2023-09-24 | 14B | 101.9 | Q6_K | fully on GPU | ~86 |
| 42 | LLaMA-65B | Meta | 2023-02-24 | 65B | 101.7 | IQ3_XXS | fully on GPU | ~41 |
| 43 | Falcon 2 11B | TII | 2024-05-09 | 11B | 99.3 | Q6_K | fully on GPU | ~110 |
| 44 | Llama 2-13B | Meta | 2023-07-18 | 13B | 96.7 | Q6_K | fully on GPU | ~92 |
| 45 | LLaMA-33B | Meta | 2023-02-24 | 33B | 96.6 | Q6_K | fully on GPU | ~38 |
| 46 | StarCoder 2 15B | Hugging Face | 2024-02-20 | 15B | 96.3 | Q6_K | fully on GPU | ~80 |
| 47 | Yi 6B | 01.AI | 2023-11-22 | 6B | 94.4 | Q6_K | fully on GPU | ~180 |
| 48 | Baichuan2-13B | Baichuan | 2023-09-06 | 13B | 92.0 | Q6_K | fully on GPU | ~92 |
| 49 | Falcon-40B | TII | 2023-05-25 | 40B | 90.9 | Q4_K_M | fully on GPU | ~42 |
| 50 | Qwen-7B | Qwen | 2023-09-28 | 7B | 89.2 | Q6_K | fully on GPU | ~160 |
| 51 | MPT-30B | MosaicML | 2023-06-22 | 30B | 85.5 | Q6_K | fully on GPU | ~42 |
| 52 | LLaMA-13B | Meta | 2023-02-24 | 13B | 85.4 | Q6_K | fully on GPU | ~92 |
| 53 | Baichuan 2-7B | Baichuan | 2023-09-20 | 7B | 83.8 | Q6_K | fully on GPU | ~160 |
| 54 | Llama 2-7B | Meta | 2023-07-18 | 7B | 83.4 | Q6_K | fully on GPU | ~160 |
| 55 | LLaMA-7B | Meta | 2023-02-24 | 7B | 77.5 | Q6_K | fully on GPU | ~160 |
| 56 | DeepSeek Coder 33B | DeepSeek | 2023-11-02 | 33B | 76.6 | Q6_K | fully on GPU | ~38 |
| 57 | Gemma 2B | 2024-02-21 | 2B | 76.0 | Q6_K | fully on GPU | ~400 | |
| 58 | MPT-7B | MosaicML | 2023-05-05 | 7B | 73.7 | Q6_K | fully on GPU | ~160 |
| 59 | Falcon-7B | TII | 2023-04-24 | 7B | 73.4 | Q6_K | fully on GPU | ~160 |
| 60 | StarCoder 2 7B | Hugging Face | 2024-02-20 | 7B | 72.7 | Q6_K | fully on GPU | ~160 |
Newer models without benchmark results yet
| Model | Lab | Released | Size | ECI | Best quant | Runs | tok/s |
|---|---|---|---|---|---|---|---|
| KLM 1.6 | MinimaLabs | 2026-11-09 | 0.1B | – | Q6_K | fully on GPU | ~930 |
| Cola2 | ByteDance | 2026-11-06 | 8.8B | – | Q6_K | fully on GPU | ~130 |
| S5 nano | Maritaca-ai | 2026-11-02 | 4.7B | – | Q6_K | fully on GPU | ~220 |
| Raon 1 nano | KRAFTON | 2026-10-29 | 3.5B | – | Q6_K | fully on GPU | ~270 |
| SenseNova Loop 9B Preview | SenseNova | 2026-10-28 | 9.7B | – | Q6_K | fully on GPU | ~120 |
| LBS 27B | inclusionAI | 2026-10-26 | 27B | – | Q6_K | fully on GPU | ~46 |
| WeVisOne VL 4B | Tencent | 2026-10-22 | 4.8B | – | Q6_K | fully on GPU | ~210 |
| WeVisOne VL 2B | Tencent | 2026-10-22 | 2.4B | – | Q6_K | fully on GPU | ~350 |
| flint | RecursiveSI | 2026-10-21 | 40B MoE | – | Q5_K_M | fully on GPU | ~550 |
| MiniMax-M3.1-Flash | MiniMax | 2026-10-19 | 220B MoE | – | IQ1_M | GPU + experts in RAM | ~190 |
| Ling 3.0 flash Sante | inclusionAI | 2026-10-19 | 127B MoE | – | IQ3_XXS | GPU + experts in RAM | ~52 |
| Axion 1.0 35B | Axion1-0 | 2026-10-16 | 35B MoE | – | Q6_K | fully on GPU | ~250 |
| NVIDIA Nemotron Labs Dory1.0 4B BF16 | NVIDIA | 2026-10-16 | 4B | – | Q6_K | fully on GPU | ~250 |
| NVIDIA Nemotron Labs Dory1.0 4B MXFP8 | NVIDIA | 2026-10-16 | 4B | – | Q6_K | fully on GPU | ~250 |
| Hy Memory 30B A3B | Tencent | 2026-10-15 | 30B MoE | – | Q6_K | fully on GPU | ~300 |
| granite switch 4.1 8b | IBM | 2026-10-14 | 8.8B | – | Q6_K | fully on GPU | ~130 |
| granite switch 4.1 30b | IBM | 2026-10-14 | 30B | – | Q6_K | fully on GPU | ~42 |
| Youtu Parsing Omni | Tencent | 2026-10-09 | 5.3B | – | Q6_K | fully on GPU | ~200 |
| MiniCPM5 2B | Openbmb | 2026-09-06 | 2.5B | – | Q6_K | fully on GPU | ~340 |
| tiny aya en thinker | Cohere | 2026-09-02 | 3.4B | – | Q6_K | fully on GPU | ~280 |
| tiny aya l2 thinker | Cohere | 2026-09-02 | 3.4B | – | Q6_K | fully on GPU | ~280 |
| Qwen3.8 Flash Next | Qwen | 2026-08-24 | 125B MoE | – | Strata | via Strata | ~73 |
| Muse Glimmer | Meta | 2026-08-10 | 30B | – | Q6_K | fully on GPU | ~42 |
| Ling 3.0 tiny | inclusionAI | 2026-08-10 | 7.9B MoE | – | Q6_K | fully on GPU | ~490 |
| North Micro Vision Instruct | Cohere | 2026-08-10 | 2.5B | – | Q6_K | fully on GPU | ~350 |
| Muse Glimmer 30B assistant | Meta | 2026-08-09 | 30B | – | Q6_K | fully on GPU | ~42 |
| Mage VL | Microsoft | 2026-07-25 | 4.7B | – | Q6_K | fully on GPU | ~220 |
| Gemma 4 E4B | 2026-07-02 | 8B | – | Q6_K | fully on GPU | ~140 | |
| Gemma 4 E2B | 2026-07-02 | 5.1B | – | Q6_K | fully on GPU | ~200 | |
| DiffusionGemma 26B-A4B | DeepMind | 2026-06-10 | 26B MoE | – | Q6_K | fully on GPU | ~250 |
| Gemma 4 12B | 2026-06-03 | 12B | – | Q6_K | fully on GPU | ~99 | |
| Granite 4.1 30B | IBM | 2026-04-29 | 29B | – | Q6_K | fully on GPU | ~43 |
| Granite 4.1 8B | IBM | 2026-04-29 | 8.8B | – | Q6_K | fully on GPU | ~130 |
| MiniMax-M2.7 | MiniMax | 2026-03-18 | 229B MoE | – | IQ1_M | GPU + experts in RAM | ~34 |
| Qwen3.5-4B | Qwen | 2026-02-24 | 4.7B | – | Q6_K | fully on GPU | ~220 |
| Qwen3.5-2B | Qwen | 2026-02-24 | 2.3B | – | Q6_K | fully on GPU | ~370 |
| Qwen3.5-0.8B | Qwen | 2026-02-24 | 0.9B | – | Q6_K | fully on GPU | ~600 |
| Qwen3.5 27B | Qwen | 2026-02-23 | 28B | – | Q6_K | fully on GPU | ~45 |
| Qwen3.5 122B-A10B | Qwen | 2026-02-23 | 125B MoE | – | IQ3_XXS | GPU + experts in RAM | ~47 |
| MiniMax-M2.5 | MiniMax | 2026-02-12 | 229B MoE | – | IQ1_M | GPU + experts in RAM | ~34 |