Open-weight models that run on GeForce RTX 4070
- Memory 12 GB
- Bandwidth 504 GB/s
- FP16 dense 58.3 TFLOPS
- Usable 10.8 GB
Specs: Memory · Compare Current and Previous GeForce Series of Graphics Cards | NVIDIA · Bandwidth · NVIDIA RTX Blackwell GPU Architecture (whitepaper) · FP16 dense · NVIDIA RTX Blackwell GPU Architecture (whitepaper)
| Fit | Model | Parameters / active | Precision | Memory | Est. speed | Longest context | Runs as |
|---|---|---|---|---|---|---|---|
| ●Runs well | Qwen2.5-14B-Instruct-1M Alibaba Qwen | 14.8B | IQ4_XS | 27–40 tok/s | 16K | One GPU | |
| ●Runs well | Qwen2.5-14B-Instruct Alibaba Qwen | 14.8B | IQ4_XS | 27–40 tok/s | 16K | One GPU | |
| ●Runs well | DeepSeek-R1-Distill-Qwen-14B DeepSeek | 14.8B | IQ4_XS | 27–40 tok/s | 16K | One GPU | |
| ●Runs well | Qwen3-14B Alibaba Qwen | 14.8B | Q4_0 | 26–39 tok/s | 16K | One GPU | |
| ●Runs well | Gemma 3 12B IT Google | 12.2B | Q4_0 | 26–38 tok/s | 11K | One GPU | |
| ●Runs well | Qwen2.5-Omni-7B Alibaba Qwen | 10.7B | Q6_K | 27–41 tok/s | 32K | One GPU | |
| ●Runs well | GLM-4.6V-Flash Zhipu AI | 10.3B | Q6_K | 29–43 tok/s | 62K | One GPU | |
| ●Runs well | Qwen3.5-9B Alibaba Qwen | 9.7B | Q6_K | 31–46 tok/s | 93K | One GPU | |
| ●Runs well | Qianfan-VL-8B Baidu | 8.8B | FP8 | 26–38 tok/s | 17K | One GPU | |
| ●Runs well | Granite Switch 4.1 8B IBM | 8.8B | Q6_Kmaker’s file | 27–40 tok/s | 17K | One GPU | |
| ●Runs well | Qwen3-VL-8B-Instruct Alibaba Qwen | 8.8B | Q6_K | 30–45 tok/s | 25K | One GPU | |
| ●Runs well | Qwen3-VL-8B-Thinking Alibaba Qwen | 8.8B | Q6_K | 30–45 tok/s | 25K | One GPU | |
| ●Runs well | pplx-embed-v2-context-9b Perplexity | 8.4B | Q8_0 | 27–41 tok/s | 63K | One GPU | |
| ●Runs well | Qwen2.5-VL-7B-Instruct Alibaba Qwen | 8.3B | Q8_0 | 27–41 tok/s | 38K | One GPU | |
| ●Runs well | Qwen3-8B Alibaba Qwen | 8.2B | Q6_Kmaker’s file | 32–48 tok/s | 28K | One GPU | |
| ●Runs well | DeepSeek-R1-Distill-Llama-8B DeepSeek | 8B | Q8_0 | 26–39 tok/s | 18K | One GPU | |
| ●Runs well | Gemma 4 E4B Google | 8B | Q8_0 | 29–44 tok/s | 125K | One GPU | |
| ●Runs well | Ling-3.0-tiny Ant Group | 7.9B / 1.3B | Q8_0maker’s file | 156–235 tok/s | 93K | One GPU | |
| ●Runs well | Qwen2.5-7B-Instruct-1M Alibaba Qwen | 7.6B | Q8_0 | 29–44 tok/s | 51K | One GPU | |
| ●Runs well | Qwen2.5-7B-Instruct Alibaba Qwen | 7.6B | Q8_0maker’s file | 29–44 tok/s | 32K | One GPU | |
| ●Runs well | DeepSeek-R1-Distill-Qwen-7B DeepSeek | 7.6B | Q8_0 | 29–44 tok/s | 32K | One GPU | |
| ●Runs well | Mistral-7B-Instruct-v0.1 Mistral AI | 7.2B | Q8_0 | 29–43 tok/s | 25K | One GPU | |
| ●Runs well | Gemma 4 E2B Google | 5.1B | Q8_0 | 46–69 tok/s | 125K | One GPU | |
| ●Runs well | WeVisOne-VL-4B Tencent | 4.8B | Q8_0 | 40–60 tok/s | 39K | One GPU | |
| ●Runs well | Qianfan-OCR Baidu | 4.7B | Q8_0 | 40–61 tok/s | 31K | One GPU | |
| ●Runs well | Qwen3.5-4B Alibaba Qwen | 4.7B | BF16 (native)maker’s file | 26–39 tok/s | 52K | One GPU | |
| ●Runs well | Qwen3-VL-4B-Instruct Alibaba Qwen | 4.4B | Q8_0maker’s file | 46–69 tok/s | 45K | One GPU | |
| ●Runs well | Qwen3-4B-Instruct-2507 Alibaba Qwen | 4B | BF16 (native)maker’s file | 27–41 tok/s | 20K | One GPU | |
| ●Runs well | Qwen3-4B Alibaba Qwen | 4B | BF16 (native)maker’s file | 27–41 tok/s | 20K | One GPU | |
| ●Runs well | DeepSeek-OCR DeepSeek | 3.3B | BF16 (native)maker’s file | 90–136 tok/s | 8K | One GPU | |
| ●Runs well | Granite-4.0-H-Micro IBM | 3.2B | BF16 (native)maker’s file | 39–59 tok/s | 128K | One GPU | |
| ●Runs well | Qwen3-1.7B Alibaba Qwen | 2B | BF16 (native)maker’s file | 50–76 tok/s | 40K | One GPU | |
| ●Runs well | DeepSeek-R1-Distill-Qwen-1.5B DeepSeek | 1.8B | BF16 (native)maker’s file | 67–100 tok/s | 125K | One GPU | |
| ●Runs well | GLM-OCR Zhipu AI | 1.3B | BF16 (native)maker’s file | 79–119 tok/s | 125K | One GPU | |
| ●Runs well | Qwen3-0.6B Alibaba Qwen | 0.8B | BF16 (native)maker’s file | 103–155 tok/s | 40K | One GPU | |
| ●Runs well | EmbeddingGemma 2 Google | 0.7B | BF16 (native)maker’s file | 160–240 tok/s | 256K | One GPU | |
| ●Runs well | Qwen3-Embedding-0.6B Alibaba Qwen | 0.6B | BF16 (native)maker’s file | 118–177 tok/s | 32K | One GPU | |
| ●Runs well | bge-reranker-v2-m3 BAAI | 0.6B | FP32 (native)maker’s file | 82–123 tok/s | 8K | One GPU | |
| ●Runs well | bge-m3-unsupervised BAAI | 0.6B | FP32 (native)maker’s file | 82–123 tok/s | 8K | One GPU | |
| ●Runs well | bge-large-en-v1.5 BAAI | 0.3B | FP32 (native)maker’s file | 181–272 tok/s | 512 | One GPU | |
| ●Runs well | bge-reranker-base BAAI | 0.3B | FP32 (native)maker’s file | 223–334 tok/s | 514 | One GPU | |
| ●Runs well | bge-base-en-v1.5 BAAI | 0.1B | FP32 (native)maker’s file | 552–827 tok/s | 512 | One GPU | |
| ●Runs well | bge-small-en-v1.5 BAAI | 0B | FP32 (native)maker’s file | 1763–2645 tok/s | 512 | One GPU | |
| ○Needs system memory | Ming-3.0-audio-flash Ant Group | 128.2B / 6.5B | IQ4_XS | 14–21 tok/s | — | 38/42 layers in system memory | |
| ○Needs system memory | Ling-3.0-flash Ant Group | 127.5B / 5.1B | IQ4_XS | 18–27 tok/s | — | 38/42 layers in system memory | |
| ○Needs system memory | Qwen3.5-122B-A10B Alibaba Qwen | 125.1B / 10B | IQ4_XS | 9–14 tok/s | — | 43/48 layers in system memory | |
| ○Needs system memory | Ling-3.0-flash-VL Ant Group | 124.8B / 5.5B | IQ4_XS | 17–25 tok/s | — | 38/42 layers in system memory | |
| ○Needs system memory | gpt-oss-120b OpenAI | 116.8B / 5.1B | MXFP4 (native)maker’s file | 17–26 tok/s | — | 32/36 layers in system memory | |
| ○Needs system memory | GLM-4.5-Air Zhipu AI | 110.5B | IQ4_XS | 10–16 tok/s | — | Experts in system memory | |
| ○Needs system memory | GLM-4.5V Zhipu AI | 107.7B / 12B | IQ4_XS | 11–17 tok/s | — | Experts in system memory | |
| ○Needs system memory | GLM-4.6V Zhipu AI | 107.7B | IQ4_XS | 11–17 tok/s | — | Experts in system memory | |
| ○Needs system memory | Ling-2.6-flash Ant Group | 107.5B / 7.4B | Q4_0 | 20–30 tok/s | — | Experts in system memory | |
| ○Needs system memory | Qwen3-Next-80B-A3B-Instruct Alibaba Qwen | 81.3B / 3B | Q6_Kmaker’s file | 20–30 tok/s | — | 43/48 layers in system memory | |
| ○Needs system memory | Qwen3-Next-80B-A3B-Thinking Alibaba Qwen | 81.3B / 3B | Q6_Kmaker’s file | 20–30 tok/s | — | 43/48 layers in system memory | |
| ○Needs system memory | Qwen3-Coder-Next Alibaba Qwen | 79.7B / 3B | Q6_Kmaker’s file | 20–30 tok/s | — | 43/48 layers in system memory | |
| ○Needs system memory | Qwen3.5-35B-A3B Alibaba Qwen | 36B / 3B | Q8_0 | 30–45 tok/s | — | Experts in system memory | |
| ○Needs system memory | Qwen3.6-35B-A3B Alibaba Qwen | 36B / 3B | Q8_0 | 30–45 tok/s | — | Experts in system memory | |
| ○Needs system memory | Qwen2.5-VL-32B-Instruct Alibaba Qwen | 33.5B | IQ4_XS | 4–6 tok/s | — | 34/64 layers in system memory | |
| ○Needs system memory | Qwen3-VL-32B-Instruct Alibaba Qwen | 33.4B | IQ4_XS | 4–6 tok/s | — | 34/64 layers in system memory | |
| ○Needs system memory | Qwen3-VL-32B-Thinking Alibaba Qwen | 33.4B | IQ4_XS | 4–6 tok/s | — | 34/64 layers in system memory | |
| ○Needs system memory | Qwen2.5-32B-Instruct Alibaba Qwen | 32.8B | IQ4_XS | 4–6 tok/s | — | 33/64 layers in system memory | |
| ○Needs system memory | Qwen2.5-Coder-32B-Instruct Alibaba Qwen | 32.8B | IQ4_XS | 4–6 tok/s | — | 33/64 layers in system memory | |
| ○Needs system memory | QwQ-32B Alibaba Qwen | 32.8B | IQ4_XS | 4–6 tok/s | — | 33/64 layers in system memory | |
| ○Needs system memory | DeepSeek-R1-Distill-Qwen-32B DeepSeek | 32.8B | IQ4_XS | 4–6 tok/s | — | 33/64 layers in system memory | |
| ○Needs system memory | Qwen3-32B Alibaba Qwen | 32.8B | IQ4_XS | 4–6 tok/s | — | 33/64 layers in system memory | |
| ○Needs system memory | Qwen3-Omni-30B-A3B-Captioner Alibaba Qwen | 31.7B | Q8_0 | 18–28 tok/s | — | Experts in system memory | |
| ○Needs system memory | Gemma 4 31B IT Google | 31.3B | IQ4_XS | 5–7 tok/s | — | 29/60 layers in system memory | |
| ○Needs system memory | GLM-4.7-Flash Zhipu AI | 31.2B | Q8_0 | 18–27 tok/s | — | Experts in system memory | |
| ○Needs system memory | Qwen3-VL-30B-A3B-Instruct Alibaba Qwen | 31.1B | Q8_0maker’s file | 20–29 tok/s | — | Experts in system memory | |
| ○Needs system memory | Qwen3-VL-30B-A3B-Thinking Alibaba Qwen | 31.1B | Q8_0maker’s file | 20–29 tok/s | — | Experts in system memory | |
| ○Needs system memory | Qwen3-30B-A3B-Instruct-2507 Alibaba Qwen | 30.5B / 3.3B | Q8_0 | 20–30 tok/s | — | Experts in system memory | |
| ○Needs system memory | Qwen3-30B-A3B-Thinking-2507 Alibaba Qwen | 30.5B / 3.3B | Q8_0 | 20–30 tok/s | — | Experts in system memory | |
| ○Needs system memory | Qwen3-30B-A3B Alibaba Qwen | 30.5B / 3.3B | Q8_0maker’s file | 20–30 tok/s | — | Experts in system memory | |
| ○Needs system memory | Qwen3-Coder-30B-A3B-Instruct Alibaba Qwen | 30.5B / 3.3B | Q8_0 | 20–30 tok/s | — | Experts in system memory | |
| ○Needs system memory | Hy-Memory-30B-A3B Tencent | 30.1B / 3B | Q8_0maker’s file | 21–31 tok/s | — | Experts in system memory | |
| ○Needs system memory | Granite Switch 4.1 30B IBM | 30B | IQ4_XS | 5–7 tok/s | — | 31/66 layers in system memory | |
| ○Needs system memory | Qwen3.5-27B Alibaba Qwen | 27.8B | IQ4_XS | 7–10 tok/s | — | 20/64 layers in system memory | |
| ○Needs system memory | Qwen3.6-27B Alibaba Qwen | 27.8B | IQ4_XS | 7–10 tok/s | — | 20/64 layers in system memory | |
| ○Needs system memory | Qwen3.8-27B Alibaba Qwen | 27.8B | IQ4_XS | 7–10 tok/s | — | 20/64 layers in system memory | |
| ○Needs system memory | Gemma 4 26B A4B IT Google | 25.8B / 3.8B | Q8_0 | 22–34 tok/s | — | Experts in system memory | |
| ○Needs system memory | Magistral Small 1.2 Mistral AI | 24B | IQ4_XS | 9–14 tok/s | — | 10/40 layers in system memory | |
| ○Needs system memory | Devstral Small 1.0 Mistral AI | 23.6B | IQ4_XS | 9–14 tok/s | — | 10/40 layers in system memory | |
| ○Needs system memory | Devstral Small 1.1 Mistral AI | 23.6B | IQ4_XS | 9–14 tok/s | — | 10/40 layers in system memory | |
| ○Needs system memory | Magistral Small 1.0 Mistral AI | 23.6B | IQ4_XS | 9–14 tok/s | — | 10/40 layers in system memory | |
| ○Needs system memory | Magistral Small 1.1 Mistral AI | 23.6B | IQ4_XS | 9–14 tok/s | — | 10/40 layers in system memory | |
| ○Needs system memory | ERNIE-4.5-21B-A3B-Thinking Baidu | 21.8B / 3B | Q8_0 | 22–33 tok/s | — | 16/28 layers in system memory | |
| ○Needs system memory | GPT OSS 20B OpenAI | 20.9B / 3.6B | MXFP4 (native)maker’s file | 49–74 tok/s | — | 6/24 layers in system memory |
Estimates from the makers’ Hugging Face repositories (as of 2026-11-06); filtered views aren’t indexed. How it’s estimated