Open-weight models that run on NVIDIA L40S
- Memory 48 GB
- Bandwidth 864 GB/s
- FP16 dense 362.05 TFLOPS
- Usable 43.2 GB
Specs: Memory · L40S GPU for AI and Graphics Performance | NVIDIA · Bandwidth · L40S GPU for AI and Graphics Performance | NVIDIA · FP16 dense · L40S GPU for AI and Graphics Performance | NVIDIA
| Fit | Model | Parameters / active | Precision | Memory | Est. speed | Longest context | Runs as |
|---|---|---|---|---|---|---|---|
| ●Runs well | Qwen3-Next-80B-A3B-Instruct Alibaba Qwen | 81.3B / 3B | IQ4_XS | 241–361 tok/s | 40K | One GPU | |
| ●Runs well | Qwen3-Next-80B-A3B-Thinking Alibaba Qwen | 81.3B / 3B | IQ4_XS | 241–361 tok/s | 40K | One GPU | |
| ●Runs well | Qwen3-Coder-Next Alibaba Qwen | 79.7B / 3B | IQ4_XS | 241–361 tok/s | 77K | One GPU | |
| ●Runs well | Qwen3.5-35B-A3B Alibaba Qwen | 36B / 3B | Q8_0 | 129–193 tok/s | 256K | One GPU | |
| ●Runs well | Qwen3.6-35B-A3B Alibaba Qwen | 36B / 3B | Q8_0 | 129–193 tok/s | 256K | One GPU | |
| ●Runs well | Qwen2.5-VL-32B-Instruct Alibaba Qwen | 33.5B | Q6_K | 15–22 tok/s | 65K | One GPU | |
| ●Runs well | Qwen3-VL-32B-Instruct Alibaba Qwen | 33.4B | Q6_K | 15–22 tok/s | 65K | One GPU | |
| ●Runs well | Qwen3-VL-32B-Thinking Alibaba Qwen | 33.4B | Q6_K | 15–22 tok/s | 65K | One GPU | |
| ●Runs well | Qwen2.5-32B-Instruct Alibaba Qwen | 32.8B | Q6_Kmaker’s file | 15–22 tok/s | 32K | One GPU | |
| ●Runs well | Qwen2.5-Coder-32B-Instruct Alibaba Qwen | 32.8B | INT4 (AWQ / GPTQ)maker’s file | 20–30 tok/s | 32K | One GPU | |
| ●Runs well | QwQ-32B Alibaba Qwen | 32.8B | Q6_Kmaker’s file | 15–22 tok/s | 40K | One GPU | |
| ●Runs well | DeepSeek-R1-Distill-Qwen-32B DeepSeek | 32.8B | Q6_K | 15–22 tok/s | 32K | One GPU | |
| ●Runs well | Qwen3-32B Alibaba Qwen | 32.8B | Q6_Kmaker’s file | 15–22 tok/s | 128K | One GPU | |
| ●Runs well | Qwen3-Omni-30B-A3B-Captioner Alibaba Qwen | 31.7B | Q8_0 | 77–115 tok/s | 64K | One GPU | |
| ●Runs well | Gemma 4 31B IT Google | 31.3B | FP8 | 13–19 tok/s | 75K | One GPU | |
| ●Runs well | GLM-4.7-Flash Zhipu AI | 31.2B | Q8_0 | 73–109 tok/s | 195K | One GPU | |
| ●Runs well | Qwen3-VL-30B-A3B-Instruct Alibaba Qwen | 31.1B | Q8_0maker’s file | 89–133 tok/s | 122K | One GPU | |
| ●Runs well | Qwen3-VL-30B-A3B-Thinking Alibaba Qwen | 31.1B | Q8_0maker’s file | 89–133 tok/s | 122K | One GPU | |
| ●Runs well | Qwen3-30B-A3B-Instruct-2507 Alibaba Qwen | 30.5B / 3.3B | Q8_0 | 100–150 tok/s | 122K | One GPU | |
| ●Runs well | Qwen3-30B-A3B-Thinking-2507 Alibaba Qwen | 30.5B / 3.3B | Q8_0 | 100–150 tok/s | 256K | One GPU | |
| ●Runs well | Qwen3-30B-A3B Alibaba Qwen | 30.5B / 3.3B | Q8_0maker’s file | 100–150 tok/s | 128K | One GPU | |
| ●Runs well | Qwen3-Coder-30B-A3B-Instruct Alibaba Qwen | 30.5B / 3.3B | Q8_0 | 100–150 tok/s | 122K | One GPU | |
| ●Runs well | Hy-Memory-30B-A3B Tencent | 30.1B / 3B | Q8_0maker’s file | 108–162 tok/s | 63K | One GPU | |
| ●Runs well | Granite Switch 4.1 30B IBM | 30B | FP8 | 13–20 tok/s | 54K | One GPU | |
| ●Runs well | Qwen3.5-27B Alibaba Qwen | 27.8B | Q8_0 | 14–22 tok/s | 229K | One GPU | |
| ●Runs well | Qwen3.6-27B Alibaba Qwen | 27.8B | Q8_0 | 14–22 tok/s | 229K | One GPU | |
| ●Runs well | Qwen3.8-27B Alibaba Qwen | 27.8B | Q8_0 | 14–22 tok/s | 229K | One GPU | |
| ●Runs well | Gemma 4 26B A4B IT Google | 25.8B / 3.8B | Q8_0 | 94–141 tok/s | 250K | One GPU | |
| ●Runs well | Magistral Small 1.2 Mistral AI | 24B | Q8_0maker’s file | 16–25 tok/s | 119K | One GPU | |
| ●Runs well | Devstral Small 1.0 Mistral AI | 23.6B | Q8_0 | 16–25 tok/s | 120K | One GPU | |
| ●Runs well | Devstral Small 1.1 Mistral AI | 23.6B | Q8_0 | 16–25 tok/s | 120K | One GPU | |
| ●Runs well | Magistral Small 1.0 Mistral AI | 23.6B | Q8_0 | 16–25 tok/s | 39K | One GPU | |
| ●Runs well | Magistral Small 1.1 Mistral AI | 23.6B | Q8_0maker’s file | 16–25 tok/s | 39K | One GPU | |
| ●Runs well | ERNIE-4.5-21B-A3B-Thinking Baidu | 21.8B / 3B | Q8_0 | 118–177 tok/s | 128K | One GPU | |
| ●Runs well | GPT OSS 20B OpenAI | 20.9B / 3.6B | MXFP4 (native)maker’s file | 168–252 tok/s | 128K | One GPU | |
| ●Runs well | Qwen2.5-14B-Instruct-1M Alibaba Qwen | 14.8B | BF16 (native)maker’s file | 14–21 tok/s | 76K | One GPU | |
| ●Runs well | Qwen2.5-14B-Instruct Alibaba Qwen | 14.8B | BF16 (native)maker’s file | 14–21 tok/s | 32K | One GPU | |
| ●Runs well | DeepSeek-R1-Distill-Qwen-14B DeepSeek | 14.8B | BF16 (native)maker’s file | 14–21 tok/s | 32K | One GPU | |
| ●Runs well | Qwen3-14B Alibaba Qwen | 14.8B | BF16 (native)maker’s file | 14–21 tok/s | 128K | One GPU | |
| ●Runs well | Gemma 3 12B IT Google | 12.2B | BF16 (native)maker’s file | 16–24 tok/s | 55K | One GPU | |
| ●Runs well | Qwen2.5-Omni-7B Alibaba Qwen | 10.7B | BF16 (native)maker’s file | 19–28 tok/s | 32K | One GPU | |
| ●Runs well | GLM-4.6V-Flash Zhipu AI | 10.3B | BF16 (native)maker’s file | 21–31 tok/s | 125K | One GPU | |
| ●Runs well | Qwen3.5-9B Alibaba Qwen | 9.7B | BF16 (native)maker’s file | 22–33 tok/s | 256K | One GPU | |
| ●Runs well | Qianfan-VL-8B Baidu | 8.8B | BF16 (native)maker’s file | 23–35 tok/s | 32K | One GPU | |
| ●Runs well | Granite Switch 4.1 8B IBM | 8.8B | BF16 (native)maker’s file | 23–34 tok/s | 128K | One GPU | |
| ●Runs well | Qwen3-VL-8B-Instruct Alibaba Qwen | 8.8B | BF16 (native)maker’s file | 23–35 tok/s | 128K | One GPU | |
| ●Runs well | Qwen3-VL-8B-Thinking Alibaba Qwen | 8.8B | BF16 (native)maker’s file | 23–35 tok/s | 128K | One GPU | |
| ●Runs well | pplx-embed-v2-context-9b Perplexity | 8.4B | FP32 (native)maker’s file | 13–19 tok/s | 256K | One GPU | |
| ●Runs well | Qwen2.5-VL-7B-Instruct Alibaba Qwen | 8.3B | BF16 (native)maker’s file | 25–38 tok/s | 125K | One GPU | |
| ●Runs well | Qwen3-8B Alibaba Qwen | 8.2B | BF16 (native)maker’s file | 25–37 tok/s | 128K | One GPU | |
| ●Runs well | DeepSeek-R1-Distill-Llama-8B DeepSeek | 8B | BF16 (native)maker’s file | 25–38 tok/s | 125K | One GPU | |
| ●Runs well | Gemma 4 E4B Google | 8B | BF16 (native)maker’s file | 27–40 tok/s | 125K | One GPU | |
| ●Runs well | Ling-3.0-tiny Ant Group | 7.9B / 1.3B | BF16 (native)maker’s file | 153–229 tok/s | 128K | One GPU | |
| ●Runs well | Qwen2.5-7B-Instruct-1M Alibaba Qwen | 7.6B | BF16 (native)maker’s file | 28–41 tok/s | 517K | One GPU | |
| ●Runs well | Qwen2.5-7B-Instruct Alibaba Qwen | 7.6B | BF16 (native)maker’s file | 28–41 tok/s | 32K | One GPU | |
| ●Runs well | DeepSeek-R1-Distill-Qwen-7B DeepSeek | 7.6B | BF16 (native)maker’s file | 28–41 tok/s | 32K | One GPU | |
| ●Runs well | Mistral-7B-Instruct-v0.1 Mistral AI | 7.2B | BF16 (native)maker’s file | 28–42 tok/s | 32K | One GPU | |
| ●Runs well | Gemma 4 E2B Google | 5.1B | BF16 (native)maker’s file | 42–63 tok/s | 125K | One GPU | |
| ●Runs well | WeVisOne-VL-4B Tencent | 4.8B | BF16 (native)maker’s file | 40–60 tok/s | 240K | One GPU | |
| ●Runs well | Qianfan-OCR Baidu | 4.7B | BF16 (native)maker’s file | 40–61 tok/s | 31K | One GPU | |
| ●Runs well | Qwen3.5-4B Alibaba Qwen | 4.7B | BF16 (native)maker’s file | 45–68 tok/s | 256K | One GPU | |
| ●Runs well | Qwen3-VL-4B-Instruct Alibaba Qwen | 4.4B | BF16 (native)maker’s file | 43–64 tok/s | 245K | One GPU | |
| ●Runs well | Qwen3-4B-Instruct-2507 Alibaba Qwen | 4B | BF16 (native)maker’s file | 47–70 tok/s | 250K | One GPU | |
| ●Runs well | Qwen3-4B Alibaba Qwen | 4B | BF16 (native)maker’s file | 47–70 tok/s | 128K | One GPU | |
| ●Runs well | DeepSeek-OCR DeepSeek | 3.3B | BF16 (native)maker’s file | 155–233 tok/s | 8K | One GPU | |
| ●Runs well | Granite-4.0-H-Micro IBM | 3.2B | BF16 (native)maker’s file | 67–100 tok/s | 128K | One GPU | |
| ●Runs well | Qwen3-1.7B Alibaba Qwen | 2B | BF16 (native)maker’s file | 86–130 tok/s | 40K | One GPU | |
| ●Runs well | DeepSeek-R1-Distill-Qwen-1.5B DeepSeek | 1.8B | BF16 (native)maker’s file | 114–171 tok/s | 125K | One GPU | |
| ●Runs well | GLM-OCR Zhipu AI | 1.3B | BF16 (native)maker’s file | 136–203 tok/s | 128K | One GPU | |
| ●Runs well | Qwen3-0.6B Alibaba Qwen | 0.8B | BF16 (native)maker’s file | 177–265 tok/s | 40K | One GPU | |
| ●Runs well | EmbeddingGemma 2 Google | 0.7B | BF16 (native)maker’s file | 274–411 tok/s | 256K | One GPU | |
| ●Runs well | Qwen3-Embedding-0.6B Alibaba Qwen | 0.6B | BF16 (native)maker’s file | 203–304 tok/s | 32K | One GPU | |
| ●Runs well | bge-reranker-v2-m3 BAAI | 0.6B | FP32 (native)maker’s file | 140–211 tok/s | 8K | One GPU | |
| ●Runs well | bge-m3-unsupervised BAAI | 0.6B | FP32 (native)maker’s file | 140–211 tok/s | 8K | One GPU | |
| ●Runs well | bge-large-en-v1.5 BAAI | 0.3B | FP32 (native)maker’s file | 311–466 tok/s | 512 | One GPU | |
| ●Runs well | bge-reranker-base BAAI | 0.3B | FP32 (native)maker’s file | 382–573 tok/s | 514 | One GPU | |
| ●Runs well | bge-base-en-v1.5 BAAI | 0.1B | FP32 (native)maker’s file | 946–1418 tok/s | 512 | One GPU | |
| ●Runs well | bge-small-en-v1.5 BAAI | 0B | FP32 (native)maker’s file | 3023–4535 tok/s | 512 | One GPU | |
| ◐Runs, slowly | Qwen2.5-VL-72B-Instruct Alibaba Qwen | 73.4B | Q4_0 | 10–15 tok/s | 9K | One GPU | |
| ◐Runs, slowly | Qwen2-VL-72B-Instruct Alibaba Qwen | 73.4B | Q4_0 | 10–15 tok/s | 9K | One GPU | |
| ◐Runs, slowly | Qwen2.5-72B-Instruct Alibaba Qwen | 72.7B | Q4_0maker’s file | 10–15 tok/s | 9K | One GPU | |
| ◐Runs, slowly | Qianfan-VL-70B Baidu | 71.8B | Q4_0 | 10–15 tok/s | 12K | One GPU | |
| ◐Runs, slowly | DeepSeek-R1-Distill-Llama-70B DeepSeek | 70.6B | Q4_0 | 10–15 tok/s | 14K | One GPU | |
| ○Needs system memory | Ming-3.0-audio-flash Ant Group | 128.2B / 6.5B | Q5_K_M | 16–24 tok/s | — | 23/42 layers in system memory | |
| ○Needs system memory | Ling-3.0-flash Ant Group | 127.5B / 5.1B | Q5_K_Mmaker’s file | 21–31 tok/s | — | 23/42 layers in system memory | |
| ○Needs system memory | Qwen3.5-122B-A10B Alibaba Qwen | 125.1B / 10B | Q4_K_M | 14–21 tok/s | — | 22/48 layers in system memory | |
| ○Needs system memory | Ling-3.0-flash-VL Ant Group | 124.8B / 5.5B | Q5_K_M | 19–29 tok/s | — | 23/42 layers in system memory | |
| ○Needs system memory | gpt-oss-120b OpenAI | 116.8B / 5.1B | MXFP4 (native)maker’s file | 37–55 tok/s | — | 13/36 layers in system memory | |
| ○Needs system memory | GLM-4.5-Air Zhipu AI | 110.5B | IQ4_XS | 13–20 tok/s | — | 14/46 layers in system memory | |
| ○Needs system memory | GLM-4.5V Zhipu AI | 107.7B / 12B | Q4_K_M | 14–21 tok/s | — | 17/46 layers in system memory | |
| ○Needs system memory | GLM-4.6V Zhipu AI | 107.7B | Q4_0 | 14–21 tok/s | — | 15/46 layers in system memory | |
| ○Needs system memory | Ling-2.6-flash Ant Group | 107.5B / 7.4B | Q6_K | 13–19 tok/s | — | 17/32 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