Best local AI models for NVIDIA Quadro M5000M
8 GB GDDR5. At a 4k context, 123 of the 233 models in our catalog with verified parameter counts fit fully, up to Mochi 1 at 10B parameters.
Check your own machine against every model →The largest models that fit fully
The 30 largest of the 123 models that fit; every smaller model in the catalog fits too. Best quant means the highest quality compression whose weights and 4k context both sit inside the memory.
| Model | Parameters | Best quant that fits | Memory used at 4k |
|---|---|---|---|
| Mochi 1 | 10B | Q4_K_M | 7.3 GB |
| Gemma 2 9B | 9B | Q4_K_M | 8 GB |
| Nemotron Nano 4B / 9B | 9B | Q5_K_M | 7.7 GB |
| GLM-4 9B / GLM-4.5-Air | 9B | Q5_K_M | 7.7 GB |
| Yi-Coder 1.5B / 9B | 9B | Q5_K_M | 7.7 GB |
| GLM-4-9B-Chat / CodeGeeX4 | 9B | Q5_K_M | 7.7 GB |
| GLM-4V-9B / GLM-4.1V-Thinking | 9B | Q5_K_M | 7.7 GB |
| Chroma | 8.9B | Q5_K_M | 7.6 GB |
| Llama 3.1 8B | 8B | Q5_K_M | 7.4 GB |
| Granite 3.3 2B / 8B | 8B | Q6_K | 7.9 GB |
| Ministral 3B / 8B | 8B | Q6_K | 7.9 GB |
| InternLM 3 8B | 8B | Q6_K | 7.9 GB |
| OpenCoder 1.5B / 8B | 8B | Q6_K | 7.9 GB |
| Seed-Coder 8B | 8B | Q6_K | 7.9 GB |
| MiniCPM-V 2.6 / MiniCPM-o 2.6 | 8B | Q6_K | 7.9 GB |
| Idefics 3 8B | 8B | Q6_K | 7.9 GB |
| Fuyu-8B | 8B | Q6_K | 7.9 GB |
| Emu3 | 8B | Q6_K | 7.9 GB |
| Stable Diffusion 3.5 Large / Turbo | 8B | Q6_K | 7.9 GB |
| EXAONE 3.5 2.4B / 7.8B | 7.8B | Q6_K | 7.7 GB |
| Mistral 7B | 7B | Q6_K | 7.4 GB |
| Qwen2.5 0.5B / 1.5B / 3B / 7B | 7B | Q6_K | 6.9 GB |
| OLMo 2 1B / 7B | 7B | Q6_K | 6.9 GB |
| Falcon 3 1B / 3B / 7B | 7B | Q6_K | 6.9 GB |
| Command R7B | 7B | Q6_K | 6.9 GB |
| OpenHermes 2.5 | 7B | Q6_K | 6.9 GB |
| Zephyr 7B Beta | 7B | Q6_K | 6.9 GB |
| OpenChat 3.5 | 7B | Q6_K | 6.9 GB |
| Starling LM 7B | 7B | Q6_K | 6.9 GB |
| Codestral Mamba 7B | 7B | Q6_K | 6.9 GB |
Close, but only with CPU offload
These need more than the card holds at their smallest practical quant, so part of the model runs from system memory (figures assume 32 GB of it). They work, several times slower.
| Model | Parameters | Memory at Q4_K_M | System RAM at 4k |
|---|---|---|---|
| Open-Sora 2.0 | 11B | 8.1 GB needed | 10.1 GB |
| FLUX.1 dev | 12B | 14.4 GB needed | 16.4 GB |
| Gemma 3 12B | 12B | 8.8 GB needed | 10.8 GB |
| Gemma 4 12B | 12B | 8.8 GB needed | 10.8 GB |
| Mistral NeMo 12B | 12B | 8.8 GB needed | 10.8 GB |
| Pixtral 12B | 12B | 8.8 GB needed | 10.8 GB |
| FLUX.1 schnell | 12B | 8.8 GB needed | 10.8 GB |
| FLUX.1 Kontext dev | 12B | 8.8 GB needed | 10.8 GB |
| FLUX.1 Krea dev | 12B | 8.8 GB needed | 10.8 GB |
| Vicuna 13B | 13B | 9.5 GB needed | 11.5 GB |
How to read this
The NVIDIA Quadro M5000M is a professional graphics card equipped with 8 GB of GDDR5 memory. This dedicated video memory determines the maximum size of the artificial intelligence models you can run entirely on the hardware. To run a model smoothly without system slowdowns, the model files and the active working memory must fit within this 8 GB limit.
Quantization is a compression method that reduces the size of these models. The quant column shows the best format that fits your hardware. For example, a Q4_K_M quant uses four bit quantization to compress models like Mochi 1 10B down to 7.3 GB of memory. Higher quants like Q5_K_M or Q6_K offer better precision and fit smaller models like Llama 3.1 8B at 7.4 GB or Mistral 7B at 7.4 GB.
When a model exceeds the 8 GB video memory limit, you must use CPU offloading. This process splits the model between your graphics card and your system RAM. Assuming your computer has 32 GB of system RAM, you can run larger models like FLUX.1 dev 12B which needs 14.4 GB of video memory and 16.4 GB of system RAM. You can also run Gemma 3 12B or Mistral NeMo 12B which require 8.8 GB of video memory and 10.8 GB of system RAM.
CPU offloading allows you to run advanced models but it comes with a performance cost. Transferring data between the graphics card and system RAM is much slower than using dedicated GDDR5 memory. This transfer bottleneck significantly reduces the generation speed of models like Vicuna 13B which needs 9.5 GB of video memory and 11.5 GB of system RAM.
You must also consider the memory cost of context length. The memory figures listed for models like Qwen2.5 7B at 6.9 GB or Granite 3.3 8B at 7.9 GB are calculated using a standard 4k context window. If you increase the context window to process longer documents or chat histories, the model will require significantly more memory and may exceed your 8 GB limit.