Best Local AI Models for AMD Radeon RX 7600 (8 GB VRAM)
The AMD Radeon RX 7600 has 8 GB of VRAM. Here are the popular AI models it can run locally (4,096-token context, ~16.0 GB system RAM assumed), ranked by popularity.
See also: Best GPU for running local LLMs.
The AMD Radeon RX 7600 is built on AMD's Desktop architecture featuring GDDR6 delivering 256 GB/s of raw memory bandwidth. AMD Radeon RX 7600 with 8 GB VRAM for local language model execution.
Equipped with 8 GB of dedicated VRAM, the AMD Radeon RX 7600 can run 27 popular open-source models completely in GPU memory without offloading. This includes full-speed execution for weights like LFM2.5-2.6B-GGUF, LFM2.5-8B-A1B-GGUF, LFM2.5-230M-GGUF. For a comprehensive breakdown of compatible model weights, see our guide to the best LLMs for 8 GB VRAM.
With a memory bandwidth of 256 GB/s, this card can generate tokens at an estimated peak rate of ~42.7 tokens/second on an 8B parameter model (Q4_K_M). Its rated power draw is 150W TDP, so ensure your system's power supply and case ventilation are adequate for sustained local inferencing.
27 fit fully in VRAM · 9 run with offload
| Model | Size | Quant. | Quality | Memory | Speed~ | Verdict |
|---|---|---|---|---|---|---|
| LiquidAI/LFM2.5-2.6B-GGUF | 2.7B | BF16 | Excellent |
5.89 GB
|
79.5 t/s | Fits in VRAM |
| LiquidAI/LFM2.5-8B-A1B-GGUF | 8.47B | Q6_K | Excellent |
7.33 GB
|
61.7 t/s | Fits in VRAM |
| LiquidAI/LFM2.5-230M-GGUF | 0.23B | BF16 | Excellent |
1.28 GB
|
929.9 t/s | Fits in VRAM |
| Qwen/Qwen3-8B-GGUF | 8.19B | Q6_K | Excellent |
7.63 GB
|
63.9 t/s | Fits in VRAM |
| unsloth/Ornith-1.0-9B-GGUF | — | Q6_K | Excellent |
7.89 GB
|
57.4 t/s | Fits in VRAM |
| bartowski/DeepSeek-Coder-V2-Lite-Instruct-GGUF | 15.71B | IQ3_XXS | Fair |
7.96 GB
|
61.7 t/s | Fits in VRAM |
| unsloth/Qwen3-4B-GGUF | 4.02B | Q8_K_XL | Excellent |
6.07 GB
|
84.9 t/s | Fits in VRAM |
| Qwen/Qwen3-0.6B-GGUF | 0.75B | Q8_0 | Excellent |
1.83 GB
|
671.7 t/s | Fits in VRAM |
| bartowski/Meta-Llama-3.1-8B-Instruct-GGUF | 8.03B | Q6_K_L | Excellent |
7.66 GB
|
62.7 t/s | Fits in VRAM |
| LiquidAI/LFM2.5-1.2B-Instruct-GGUF | 1.17B | BF16 | Excellent |
3.03 GB
|
183.3 t/s | Fits in VRAM |
| Qwen/Qwen2.5-Coder-7B-Instruct-GGUF | 7.62B | Q6_K | Excellent |
6.84 GB
|
68.7 t/s | Fits in VRAM |
| janhq/Jan-v3.5-4B-gguf | 4.41B | Q8_0 | Excellent |
5.73 GB
|
91.5 t/s | Fits in VRAM |
| MaziyarPanahi/Qwen3-14B-GGUF | 14.77B | Q2_K | Low |
6.78 GB
|
74.6 t/s | Fits in VRAM |
| MaziyarPanahi/Qwen3-1.7B-GGUF | 2.03B | GGUF | Excellent |
5.03 GB
|
105.5 t/s | Fits in VRAM |
| Qwen/Qwen2.5-1.5B-Instruct-GGUF | 1.54B | GGUF | Excellent |
4.23 GB
|
120.6 t/s | Fits in VRAM |
| MaziyarPanahi/Yi-Coder-9B-Chat-GGUF | 8.83B | Q5_K_M | Very good |
7.0 GB
|
68.6 t/s | Fits in VRAM |
| MaziyarPanahi/Qwen3-4B-Instruct-2507-GGUF | 4.02B | Q6_K | Excellent |
4.44 GB
|
129.9 t/s | Fits in VRAM |
| Qwen/Qwen2.5-0.5B-Instruct-GGUF | 0.49B | GGUF | Excellent |
2.03 GB
|
339.1 t/s | Fits in VRAM |
| Qwen/Qwen2.5-3B-Instruct-GGUF | 3.09B | GGUF | Excellent |
7.27 GB
|
63.2 t/s | Fits in VRAM |
| bartowski/Qwen2.5-7B-Instruct-GGUF | 7.62B | Q6_K_L | Excellent |
7.09 GB
|
65.9 t/s | Fits in VRAM |
| unsloth/Llama-3.2-3B-Instruct-GGUF | 3.21B | F16 | Excellent |
7.23 GB
|
66.8 t/s | Fits in VRAM |
| MaziyarPanahi/Meta-Llama-3-8B-Instruct-GGUF | 8.03B | Q6_K | Excellent |
7.42 GB
|
65.1 t/s | Fits in VRAM |
| MaziyarPanahi/Yi-Coder-1.5B-Chat-GGUF | 1.48B | GGUF | Excellent |
4.3 GB
|
145.4 t/s | Fits in VRAM |
| MaziyarPanahi/Phi-3.5-mini-instruct-GGUF | 3.82B | Q8_0 | Excellent |
6.08 GB
|
105.8 t/s | Fits in VRAM |
| MaziyarPanahi/Mistral-7B-Instruct-v0.3-GGUF | 7.25B | Q6_K | Excellent |
6.84 GB
|
72.2 t/s | Fits in VRAM |
| MaziyarPanahi/gemma-3-4b-it-GGUF | 4.3B | Q8_0 | Excellent |
5.0 GB
|
104.0 t/s | Fits in VRAM |
| MaziyarPanahi/Llama-3.2-1B-Instruct-GGUF | 1.24B | GGUF | Excellent |
3.3 GB
|
173.2 t/s | Fits in VRAM |
| unsloth/Qwen3-Coder-30B-A3B-Instruct-GGUF | 30.53B | Q5_K_XL | Very good |
21.42 GB
|
2.5 t/s | Offload |
| unsloth/gpt-oss-20b-GGUF | 20.91B | F16 | Very good |
13.74 GB
|
3.9 t/s | Offload |
| unsloth/Qwen-AgentWorld-35B-A3B-GGUF | 34.66B | Q4_K_XL | Very good |
21.67 GB
|
2.4 t/s | Offload |
| bartowski/Kwaipilot_KAT-Coder-V2.5-Dev-GGUF | 34.66B | Q5_K_S | Very good |
23.38 GB
|
2.2 t/s | Offload |
| MaziyarPanahi/Qwen3-30B-A3B-GGUF | 30.53B | Q5_K_M | Very good |
21.41 GB
|
2.5 t/s | Offload |
| MaziyarPanahi/Qwen3-32B-GGUF | 32.76B | Q5_K_M | Very good |
23.42 GB
|
2.3 t/s | Offload |
| ggml-org/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-GGUF | 31.58B | Q4_0 | Good |
18.42 GB
|
2.8 t/s | Offload |
| bartowski/Qwen2.5-32B-Instruct-GGUF | 32.76B | Q5_K_L | Very good |
23.91 GB
|
2.3 t/s | Offload |
| unsloth/Qwen3-Coder-Next-GGUF | 79.67B | IQ2_XXS | Very low |
22.89 GB
|
2.3 t/s | Offload |
"Fits in VRAM" = fast, fully on GPU. "Offload" = part on system RAM, slower. Speed is a rough estimate.
Frequently asked questions
What is the VRAM and memory bandwidth of the AMD Radeon RX 7600?
The AMD Radeon RX 7600 features 8 GB of VRAM and a memory bandwidth of 256 GB/s (GDDR6). In local language model inference, VRAM determines which model sizes fit on the card, while memory bandwidth dictates how many tokens per second the GPU generates.
What is the best local AI model to run on a AMD Radeon RX 7600?
The best overall model for the AMD Radeon RX 7600 is LiquidAI/LFM2.5-2.6B-GGUF using the recommended BF16 quantization (5.89 GB total memory). With 8 GB of VRAM, this GPU typically runs a 7–8B model at Q6, entirely in VRAM at full GPU speed. Check our guide to the best LLMs for 8 GB VRAM for details.
What models can you run on an 8 GB GPU like the AMD Radeon RX 7600?
With 8 GB of VRAM, the AMD Radeon RX 7600 comfortably runs 7B and 8B models (such as Llama 3.1 8B, Mistral 7B, or Qwen 2.5 7B) using Q4_K_M or Q5_K_M quantizations (~5.5 to 6.8 GB). Running 14B models requires offloading memory to system RAM, which lowers token generation speed.
Does the AMD Radeon RX 7600 work with Ollama and LM Studio?
Yes. Under Linux, the AMD Radeon RX 7600 supports native ROCm acceleration in Ollama and llama.cpp. On Windows, Ollama and LM Studio utilize the Vulkan or DirectML backends to leverage the card's full 8 GB VRAM pool.
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