Best LLMs for 8 GB of VRAM
111 open-weights models run on a GPU with 8 GB of VRAM and 32 GB of system RAM, 56 of them fully on the GPU. The strongest by benchmark score: Qwen 3.6 35B-A3B, Qwen3.5-35B-A3B, Gemma 4 26B A4B.
Each row shows the best quantization that fits, where it runs and a speed estimate at a 16K context, assuming a card with about 272 GB/s of memory bandwidth. Bigger mixture-of-experts models can keep their experts in system RAM, which is why some run well above 8 GB. Other sizes: 6 GB · 12 GB · 16 GB · 24 GB · 32 GB.
Ranked by benchmarks
| # | Model | Lab | Released | Size | ECI | Best quant | Runs | tok/s |
|---|---|---|---|---|---|---|---|---|
| 1 | Qwen 3.6 35B-A3B | Qwen | 2026-04-14 | 36B MoE | 144.0 | Q6_K | GPU + experts in RAM | ~28 |
| 2 | Qwen3.5-35B-A3B | Qwen | 2026-02-24 | 36B MoE | 144.0 | Q6_K | GPU + experts in RAM | ~28 |
| 3 | Gemma 4 26B A4B | 2026-04-02 | 26B MoE | 141.6 | Q6_K | GPU + experts in RAM | ~15 | |
| 4 | gpt-oss-120b | OpenAI | 2025-08-05 | 117B MoE | 140.2 | IQ2_XXS | GPU + experts in RAM | ~31 |
| 5 | Qwen3.5-9B | Qwen | 2026-02-24 | 9.7B | 139.3 | Q4_K_M | fully on GPU | ~27 |
| 6 | Qwen3-30B-A3B-Thinking (Jul 2025) | Qwen | 2025-07-30 | 30B MoE | 138.3 | Q6_K | GPU + experts in RAM | ~20 |
| 7 | Qwen3-14B | Qwen | 2025-04-29 | 15B | 136.1 | Q4_K_M | split GPU / CPU | ~5 |
| 8 | Qwen3-30B-A3B-Instruct (Jul 2025) | Qwen | 2025-07-29 | 30B MoE | 135.2 | Q6_K | GPU + experts in RAM | ~20 |
| 9 | gpt-oss-20b | OpenAI | 2025-08-05 | 21B MoE | 135.0 | Q6_K | GPU + experts in RAM | ~16 |
| 10 | Qwen3-30B-A3B | Qwen | 2025-04-29 | 31B MoE | 134.1 | Q6_K | GPU + experts in RAM | ~22 |
| 11 | Qwen3-8B | Qwen | 2025-04-28 | 8.2B | 132.1 | Q3_K_M | fully on GPU | ~39 |
| 12 | Phi-4 | Microsoft | 2024-12-12 | 15B | 129.0 | IQ4_XS | split GPU / CPU | ~6 |
| 13 | phi-3-small 7.4B | Microsoft | 2024-04-23 | 7.4B | 116.5 | IQ4_XS | fully on GPU | ~40 |
| 14 | phi-3-medium 14B | Microsoft | 2024-04-23 | 14B | 114.3 | Q4_K_M | split GPU / CPU | ~6 |
| 15 | Mistral NeMo | Mistral | 2024-07-18 | 12B | 113.9 | Q5_K_M | split GPU / CPU | ~5 |
| 16 | Gemma 2 9B | 2024-06-24 | 9.2B | 112.7 | Q6_K | split GPU / CPU | ~7 | |
| 17 | Llama 3.1-8B | Meta | 2024-07-23 | 8B | 111.1 | IQ4_XS | fully on GPU | ~37 |
| 18 | phi-3-mini 3.8B | Microsoft | 2024-04-23 | 3.8B | 110.6 | Q6_K | fully on GPU | ~50 |
| 19 | Llama 3-8B | Meta | 2024-04-18 | 8B | 109.4 | IQ4_XS | fully on GPU | ~37 |
| 20 | Gemma 7B | 2024-02-21 | 7B | 103.3 | Q4_K_M | fully on GPU | ~37 | |
| 21 | Mistral 7B v0.1 | Mistral | 2023-09-27 | 7B | 102.8 | Q4_K_M | fully on GPU | ~37 |
| 22 | Qwen-14B | Qwen | 2023-09-24 | 14B | 101.9 | Q4_K_M | split GPU / CPU | ~6 |
| 23 | Falcon 2 11B | TII | 2024-05-09 | 11B | 99.3 | Q6_K | split GPU / CPU | ~5 |
| 24 | Llama 2-13B | Meta | 2023-07-18 | 13B | 96.7 | Q4_K_M | split GPU / CPU | ~6 |
| 25 | StarCoder 2 15B | Hugging Face | 2024-02-20 | 15B | 96.3 | IQ4_XS | split GPU / CPU | ~6 |
| 26 | Yi 6B | 01.AI | 2023-11-22 | 6B | 94.4 | Q5_K_M | fully on GPU | ~37 |
| 27 | Baichuan2-13B | Baichuan | 2023-09-06 | 13B | 92.0 | Q4_K_M | split GPU / CPU | ~6 |
| 28 | Qwen-7B | Qwen | 2023-09-28 | 7B | 89.2 | Q4_K_M | fully on GPU | ~37 |
| 29 | LLaMA-13B | Meta | 2023-02-24 | 13B | 85.4 | Q4_K_M | split GPU / CPU | ~6 |
| 30 | Baichuan 2-7B | Baichuan | 2023-09-20 | 7B | 83.8 | Q4_K_M | fully on GPU | ~37 |
| 31 | Llama 2-7B | Meta | 2023-07-18 | 7B | 83.4 | Q4_K_M | fully on GPU | ~37 |
| 32 | LLaMA-7B | Meta | 2023-02-24 | 7B | 77.5 | Q4_K_M | fully on GPU | ~37 |
| 33 | Gemma 2B | 2024-02-21 | 2B | 76.0 | Q6_K | fully on GPU | ~91 | |
| 34 | MPT-7B | MosaicML | 2023-05-05 | 7B | 73.7 | Q4_K_M | fully on GPU | ~37 |
| 35 | Falcon-7B | TII | 2023-04-24 | 7B | 73.4 | Q4_K_M | fully on GPU | ~37 |
| 36 | StarCoder 2 7B | Hugging Face | 2024-02-20 | 7B | 72.7 | Q4_K_M | fully on GPU | ~37 |
| 37 | XGen-7B | Salesforce | 2023-06-27 | 7B | 70.6 | Q4_K_M | fully on GPU | ~37 |
| 38 | DeepSeek Coder 6.7B | DeepSeek | 2023-11-02 | 6.7B | 67.8 | Q4_K_M | fully on GPU | ~38 |
| 39 | StarCoder 2 3B | Hugging Face | 2024-02-22 | 3B | 66.9 | Q6_K | fully on GPU | ~62 |
| 40 | Dolly 2.0-12b | Databricks | 2023-04-11 | 12B | 55.8 | Q5_K_M | split GPU / CPU | ~6 |
| 41 | Cerebras-GPT-13B | Cerebras | 2023-03-20 | 13B | 49.3 | Q4_K_M | split GPU / CPU | ~6 |
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 | ~670 |
| Cola2 | ByteDance | 2026-11-06 | 8.8B | – | Q3_K_M | fully on GPU | ~37 |
| S5 nano | Maritaca-ai | 2026-11-02 | 4.7B | – | Q6_K | fully on GPU | ~41 |
| Raon 1 nano | KRAFTON | 2026-10-29 | 3.5B | – | Q6_K | fully on GPU | ~53 |
| SenseNova Loop 9B Preview | SenseNova | 2026-10-28 | 9.7B | – | Q4_K_M | fully on GPU | ~27 |
| LBS 27B | inclusionAI | 2026-10-26 | 27B | – | IQ3_XXS | split GPU / CPU | ~5 |
| WeVisOne VL 4B | Tencent | 2026-10-22 | 4.8B | – | Q6_K | fully on GPU | ~40 |
| WeVisOne VL 2B | Tencent | 2026-10-22 | 2.4B | – | Q6_K | fully on GPU | ~75 |
| flint | RecursiveSI | 2026-10-21 | 40B MoE | – | Q5_K_M | GPU + experts in RAM | ~54 |
| Axion 1.0 35B | Axion1-0 | 2026-10-16 | 35B MoE | – | Q6_K | GPU + experts in RAM | ~22 |
| NVIDIA Nemotron Labs Dory1.0 4B BF16 | NVIDIA | 2026-10-16 | 4B | – | Q6_K | fully on GPU | ~48 |
| NVIDIA Nemotron Labs Dory1.0 4B MXFP8 | NVIDIA | 2026-10-16 | 4B | – | Q6_K | fully on GPU | ~48 |
| Hy Memory 30B A3B | Tencent | 2026-10-15 | 30B MoE | – | Q6_K | GPU + experts in RAM | ~16 |
| granite switch 4.1 8b | IBM | 2026-10-14 | 8.8B | – | Q6_K | split GPU / CPU | ~7 |
| Youtu Parsing Omni | Tencent | 2026-10-09 | 5.3B | – | Q6_K | fully on GPU | ~36 |
| MiniCPM5 2B | Openbmb | 2026-09-06 | 2.5B | – | Q6_K | fully on GPU | ~73 |
| tiny aya en thinker | Cohere | 2026-09-02 | 3.4B | – | Q6_K | fully on GPU | ~56 |
| tiny aya l2 thinker | Cohere | 2026-09-02 | 3.4B | – | Q6_K | fully on GPU | ~56 |
| Qwen3.8 Flash Next | Qwen | 2026-08-24 | 125B MoE | – | IQ2_XXS | GPU + experts in RAM | ~43 |
| Ling 3.0 tiny | inclusionAI | 2026-08-10 | 7.9B MoE | – | Q5_K_M | fully on GPU | ~140 |
| North Micro Vision Instruct | Cohere | 2026-08-10 | 2.5B | – | Q6_K | fully on GPU | ~74 |
| Mage VL | Microsoft | 2026-07-25 | 4.7B | – | Q6_K | fully on GPU | ~40 |
| Gemma 4 E4B | 2026-07-02 | 8B | – | Q5_K_M | fully on GPU | ~28 | |
| Gemma 4 E2B | 2026-07-02 | 5.1B | – | Q6_K | fully on GPU | ~37 | |
| DiffusionGemma 26B-A4B | DeepMind | 2026-06-10 | 26B MoE | – | Q6_K | GPU + experts in RAM | ~16 |
| Gemma 4 12B | 2026-06-03 | 12B | – | Q5_K_M | split GPU / CPU | ~6 | |
| Granite 4.1 8B | IBM | 2026-04-29 | 8.8B | – | Q6_K | split GPU / CPU | ~7 |
| Qwen3.5-4B | Qwen | 2026-02-24 | 4.7B | – | Q6_K | fully on GPU | ~41 |
| Qwen3.5-2B | Qwen | 2026-02-24 | 2.3B | – | Q6_K | fully on GPU | ~81 |
| Qwen3.5-0.8B | Qwen | 2026-02-24 | 0.9B | – | Q6_K | fully on GPU | ~190 |
| Qwen3-Coder-Next | Qwen | 2026-02-02 | 80B MoE | – | IQ3_XXS | GPU + experts in RAM | ~40 |
| GLM-4.7-Flash | Z.ai | 2026-01-19 | 31B MoE | – | Q6_K | GPU + experts in RAM | ~16 |
| Solar Open 100B | Upstage | 2025-12-31 | 103B MoE | – | IQ2_XXS | GPU + experts in RAM | ~16 |
| VAETKI | NC AI | 2025-12-30 | 112B MoE | – | IQ2_XXS | GPU + experts in RAM | ~17 |
| Nemotron 3-Nano-30B-A3B | NVIDIA | 2025-12-15 | 30B MoE | – | Q6_K | GPU + experts in RAM | ~20 |
| Nemotron-Cascade 14B | NVIDIA | 2025-12-15 | 14B | – | Q4_K_M | split GPU / CPU | ~6 |
| GLM-4.6V-Flash | Z.ai | 2025-12-08 | 10B | – | IQ4_XS | fully on GPU | ~29 |
| GLM-4.6V | Z.ai | 2025-12-08 | 108B MoE | – | IQ2_XXS | GPU + experts in RAM | ~15 |
| Ministral 3 3B | Mistral | 2025-12-02 | 3.9B | – | Q6_K | fully on GPU | ~49 |
| Ministral 3 8B | Mistral | 2025-12-02 | 8.9B | – | Q6_K | split GPU / CPU | ~8 |