Best Local AI Models for AMD Radeon RX 7900 XTX (24 GB VRAM)
The AMD Radeon RX 7900 XTX has 24 GB of VRAM. Here are the popular AI models it can run locally (4,096-token context, ~32.0 GB system RAM assumed), ranked by popularity.
See also: Best GPU for running local LLMs.
The AMD Radeon RX 7900 XTX is built on AMD's RDNA 3 architecture featuring GDDR6 384-bit delivering 960 GB/s of raw memory bandwidth. AMD's 24 GB flagship: massive memory capacity for Linux/ROCm setups running 32B and 70B models.
Equipped with 24 GB of dedicated VRAM, the AMD Radeon RX 7900 XTX can run 36 popular open-source models completely in GPU memory without offloading. This includes full-speed execution for weights like Qwen3-Coder-30B-A3B-Instruct-GGUF, LFM2.5-2.6B-GGUF, LFM2.5-8B-A1B-GGUF. For a comprehensive breakdown of compatible model weights, see our guide to the best LLMs for 24 GB VRAM.
With a memory bandwidth of 960 GB/s, this card can generate tokens at an estimated peak rate of ~160.0 tokens/second on an 8B parameter model (Q4_K_M). Its rated power draw is 355W TDP, so ensure your system's power supply and case ventilation are adequate for sustained local inferencing.
36 fit fully in VRAM · 1 run with offload
| Model | Size | Quant. | Quality | Memory | Speed~ | Verdict |
|---|---|---|---|---|---|---|
| unsloth/Qwen3-Coder-30B-A3B-Instruct-GGUF | 30.53B | Q5_K_XL | Very good |
21.42 GB
|
19.8 t/s | Fits in VRAM |
| 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 | BF16 | Excellent |
16.63 GB
|
25.3 t/s | Fits in VRAM |
| LiquidAI/LFM2.5-230M-GGUF | 0.23B | BF16 | Excellent |
1.28 GB
|
929.9 t/s | Fits in VRAM |
| unsloth/gpt-oss-20b-GGUF | 20.91B | F16 | Very good |
13.74 GB
|
31.1 t/s | Fits in VRAM |
| Qwen/Qwen3-8B-GGUF | 8.19B | Q8_0 | Excellent |
9.47 GB
|
49.3 t/s | Fits in VRAM |
| unsloth/Ornith-1.0-9B-GGUF | — | BF16 | Excellent |
17.61 GB
|
24.0 t/s | Fits in VRAM |
| bartowski/DeepSeek-Coder-V2-Lite-Instruct-GGUF | 15.71B | Q8_0_L | Excellent |
17.39 GB
|
25.1 t/s | Fits in VRAM |
| unsloth/Qwen3-4B-GGUF | 4.02B | BF16 | Excellent |
8.86 GB
|
53.3 t/s | Fits in VRAM |
| unsloth/Qwen-AgentWorld-35B-A3B-GGUF | 34.66B | Q4_K_XL | Very good |
21.67 GB
|
19.2 t/s | Fits in VRAM |
| bartowski/Kwaipilot_KAT-Coder-V2.5-Dev-GGUF | 34.66B | Q5_K_S | Very good |
23.38 GB
|
17.8 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 | Q8_0 | Excellent |
9.23 GB
|
50.3 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 | GGUF | Excellent |
15.21 GB
|
28.2 t/s | Fits in VRAM |
| janhq/Jan-v3.5-4B-gguf | 4.41B | GGUF | Excellent |
9.59 GB
|
48.6 t/s | Fits in VRAM |
| MaziyarPanahi/Qwen3-14B-GGUF | 14.77B | Q6_K | Excellent |
12.71 GB
|
35.4 t/s | Fits in VRAM |
| MaziyarPanahi/Qwen3-1.7B-GGUF | 2.03B | GGUF | Excellent |
5.03 GB
|
105.5 t/s | Fits in VRAM |
| MaziyarPanahi/Qwen3-30B-A3B-GGUF | 30.53B | Q5_K_M | Very good |
21.41 GB
|
19.8 t/s | Fits in VRAM |
| MaziyarPanahi/Qwen3-32B-GGUF | 32.76B | Q5_K_M | Very good |
23.42 GB
|
18.5 t/s | Fits in VRAM |
| ggml-org/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-GGUF | 31.58B | Q4_0 | Good |
18.42 GB
|
22.7 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 | GGUF | Excellent |
17.62 GB
|
24.3 t/s | Fits in VRAM |
| bartowski/Qwen2.5-32B-Instruct-GGUF | 32.76B | Q5_K_L | Very good |
23.91 GB
|
18.1 t/s | Fits in VRAM |
| MaziyarPanahi/Qwen3-4B-Instruct-2507-GGUF | 4.02B | GGUF | Excellent |
8.86 GB
|
53.3 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 | F16 | Excellent |
15.21 GB
|
28.2 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 | GGUF | Excellent |
16.24 GB
|
26.7 t/s | Fits in VRAM |
| unsloth/Qwen3-Coder-Next-GGUF | 79.67B | IQ2_XXS | Very low |
22.89 GB
|
18.4 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 | GGUF | Excellent |
14.8 GB
|
29.6 t/s | Fits in VRAM |
| MaziyarPanahi/gemma-3-4b-it-GGUF | 4.3B | GGUF | Excellent |
8.38 GB
|
55.3 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 |
| MaziyarPanahi/Mixtral-8x22B-v0.1-GGUF | 140.62B | IQ3_XS | Fair |
55.9 GB
|
0.9 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 7900 XTX?
The AMD Radeon RX 7900 XTX features 24 GB of VRAM and a memory bandwidth of 960 GB/s (GDDR6 384-bit). 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 7900 XTX?
The best overall model for the AMD Radeon RX 7900 XTX is unsloth/Qwen3-Coder-30B-A3B-Instruct-GGUF using the recommended Q5_K_XL quantization (21.42 GB total memory). With 24 GB of VRAM, this GPU typically runs a 30–35B model at Q5, fully on the GPU at full GPU speed. Check our guide to the best LLMs for 24 GB VRAM for details.
Can the AMD Radeon RX 7900 XTX run 70B parameter models like Llama 3.3 70B?
Yes. With 24 GB of VRAM, the AMD Radeon RX 7900 XTX can run 70B parameter models quantized to 3-bit or 4-bit (such as IQ3_M or Q4_K_M) with moderate context windows (4k tokens). For larger context windows (16k+), pairing with a second GPU or slight CPU offloading may be required.
Does the AMD Radeon RX 7900 XTX work with Ollama and LM Studio?
Yes. Under Linux, the AMD Radeon RX 7900 XTX 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 24 GB VRAM pool.
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