Best local AI models for AMD HD 7850M

The largest models that fit fully

The 30 largest of the 56 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.

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.

How to read this

The AMD Radeon HD 7850M is a mobile graphics card equipped with 2 GB of GDDR5 video memory. This dedicated memory pool is the primary constraint when running artificial intelligence models locally. To execute a model entirely on the graphics hardware, the model files and active memory must fit within this 2 GB limit. Running models directly on your graphics card ensures the fastest processing speeds for text generation, image creation, and audio synthesis.

The quantization column indicates the compression level applied to each model. Raw models are often too large for consumer hardware, so they undergo quantization to reduce their footprint. For example, a Q4_K_M quantization represents a medium four bit compression that balances size and output quality. Higher quantizations like Q6_K or Q8_0 offer better precision but require more memory. Choosing the right quantization allows you to run larger architectures like the Allegro 2.8B or Open-Sora Plan 2.7B within your strict hardware limits.

When a model exceeds your dedicated video memory, you must use CPU offloading. This technique splits the workload between your graphics card and your system RAM. If you have 32 GB of system RAM, you can run larger models like the 3.5B SDXL Turbo or the 3.3B MusicGen. Offloading allows these models to run, but it introduces a speed penalty. Data must travel between the system RAM and the graphics card, which is much slower than using dedicated GDDR5 memory.

For fully local execution on the graphics card, several compact models fit perfectly. The LFM2 2.6B and Playground v2.5 models both run at Q4_K_M quantization while using 1.9 GB of video memory. You can also deploy Stable Diffusion 3.5 Medium or Canary 2.5B at Q4_K_M quantization using 1.8 GB of memory. If you prefer higher precision, the Moondream 2 1.9B model fits at Q6_K quantization using 1.9 GB of memory. Extremely small models like TinyLlama 1.1B run at Q8_0 quantization and use only 1.4 GB of video memory.

System memory offloading expands your options to larger creative tools. The Stable Diffusion XL model requires 4.1 GB at FP8 or optimized settings, which utilizes 6.1 GB of system RAM alongside your graphics card. Similarly, the Kandinsky 3.1 model at 3B parameters needs 2.2 GB of video memory and 4.2 GB of system RAM at Q4_K_M quantization. While offloading makes these advanced models accessible, you should expect longer generation times compared to models that fit entirely within your dedicated video memory.

You must also consider the active context window when running local language models. The memory figures listed are for the model weights alone. As you input longer prompts or generate longer responses, the active memory usage increases. Running a model close to the 2 GB limit, such as the SeamlessM4T v2 2.3B at Q5_K_M, leaves very little room for context. For extended conversations or deep document analysis, choosing a smaller model like the SmolLM2 1.7B at Q6_K is safer because it leaves more memory free for your active session.