Best local AI models for AMD HD 7850

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 HD 7850 graphics card features 2 GB of GDDR5 video memory. This hardware limit dictates which artificial intelligence models can run locally on your system. To fit within this memory envelope, models must be compressed using quantization. The quant column indicates the specific level of compression applied to each model. A lower quantization level like Q4_K_M reduces the memory footprint but slightly lowers output precision. A higher level like Q8_0 maintains better quality but requires more video memory.

For models that fit entirely on the graphics card, the maximum size is highly constrained. The Allegro 2.8B model is the largest option utilizing 2 GB of video memory at Q4_K_M. Other options include Open-Sora Plan 2.7B at Q4_K_M using 2 GB and LFM2 2.6B at Q4_K_M using 1.9 GB. Playground v2.5 2.6B also uses 1.9 GB of video memory with the Q4_K_M quantization. Stable Diffusion 3.5 Medium 2.5B and Canary 2.5B both require 1.8 GB of video memory at Q4_K_M.

Slightly smaller models can run with less compression for better output quality. SeamlessM4T v2 2.3B uses 2 GB of video memory at Q5_K_M. Parler-TTS 2.2B uses 1.9 GB and Kimi K3 DSpark 2.2B uses 2 GB under the Q5_K_M quantization. Highly optimized models like SmolVLM 2B, Stable Diffusion 3 Medium 2B, Pyramid Flow 2B, and Wav2Vec2 XLS-R 2B all run at Q6_K using exactly 2 GB of video memory. Moondream 2 1.9B fits within 1.9 GB at Q6_K, while Qwen3 1.7B and SmolLM2 1.7B use 1.7 GB at Q6_K.

For maximum precision, you can run smaller models at Q8_0 quantization. StableLM 2 1.6B, Sana 1.6B, Zonos 0.1 1.6B, and Dia 1.6B all use exactly 2 GB of video memory at Q8_0. Whisper Large v3 1.55B also uses 2 GB at Q8_0. ControlNet 1.5B, Hunyuan-DiT 1.5B, Stable Video Diffusion 1.5B, Whisper Large v2 turbo 1.5B, AudioGen 1.5B, and AudioLDM 2 1.5B all require 1.9 GB at Q8_0. Tango 2 1.4B uses 1.8 GB at Q8_0. TinyLlama 1.1B and SantaCoder 1.1B run comfortably using 1.4 GB at Q8_0.

When a model exceeds the 2 GB video memory limit, you must use CPU offloading. This process splits the workload between your graphics card and your system RAM. Assuming you have 32 GB of system RAM, you can run larger models but processing speeds will decrease. For example, SmolLM3 3B, Replit Code v1.5 3B, Kandinsky 3.1 3B, Voxtral Mini 3B, Orpheus TTS 3B, and Higgs Audio v2 3B all need 2.2 GB of video memory at Q4_K_M and require an additional 4.2 GB of system RAM.

Larger offload options include MusicGen 3.3B which needs 2.4 GB of video memory at Q4_K_M and 4.4 GB of system RAM. SDXL Turbo 3.5B and SDXL Lightning 3.5B both need 2.6 GB of video memory at Q4_K_M and 4.6 GB of system RAM. Stable Diffusion XL 3.417B needs 4.1 GB of video memory at FP8 and 6.1 GB of system RAM. Be aware that running text models with a standard 4k context window increases memory usage during generation, which can push these models past your hardware limits.