Best AI Models for AMD Radeon RX 7900 XTX (24.0GB)
24 GB is the enthusiast tier for running AI models locally. It comfortably handles 7B–13B models at high quality and opens the door to larger 30B models at moderate quantization.
This is one of the most popular memory tiers for local AI, found in GPUs like the RTX 4090 and RTX 3090. You can run Llama 3 8B, Mistral 7B, and Qwen 2.5 7B at Q5_K_M or Q6_K quality with fast token generation and generous context windows. Larger 14B models like DeepSeek R1 Distill fit comfortably at Q4_K_M. For even bigger models, 30B class runs at Q2–Q3, but 70B models are generally too heavy for single-GPU inference at this tier.
Runs Well
- 7B models (Llama 3 8B, Mistral 7B) at Q5–Q8 quality
- 13B–14B models at Q4–Q5 quality
- Small models (3B–4B) at FP16 precision
- Multimodal models like LLaVA 7B
Challenging
- 30B models only at Q2–Q3 quantization
- 70B models do not fit in VRAM
- Large context windows with 14B+ models
What LLMs Can AMD Radeon RX 7900 XTX Run?
28 models · 2 excellent · 6 good
Showing compatibility for AMD Radeon RX 7900 XTX
| Model | Quant | VRAM | Speed | Context | Status | Grade |
|---|---|---|---|---|---|---|
Q4_K_M·98.0 t/s tok/s·131K ctx·EASY RUN | Q4_K_M | 5.4 GB | 98.0 t/s | 131K | EASY RUN | C37 |
Q4_K_M·24.6 t/s tok/s·4K ctx·FAIR FIT | Q4_K_M | 21.4 GB | 24.6 t/s | 4K | FAIR FIT | B56 |
Q4_K_M·105.8 t/s tok/s·131K ctx·EASY RUN | Q4_K_M | 5.0 GB | 105.8 t/s | 131K | EASY RUN | C36 |
Q8_0·107.5 t/s tok/s·4K ctx·EASY RUN | Q8_0 | 4.9 GB | 107.5 t/s | 4K | EASY RUN | C35 |
Q4_K_M·182.7 t/s tok/s·41K ctx·EASY RUN | Q4_K_M | 2.9 GB | 182.7 t/s | 41K | EASY RUN | C31 |
Q4_K_M·200.0 t/s tok/s·2K ctx·EASY RUN | Q4_K_M | 2.6 GB | 200.0 t/s | 2K | EASY RUN | C31 |
Q4_K_M·266.7 t/s tok/s·131K ctx·EASY RUN | Q4_K_M | 2.0 GB | 266.7 t/s | 131K | EASY RUN | D29 |
Q4_K_M·185.3 t/s tok/s·131K ctx·EASY RUN | Q4_K_M | 2.9 GB | 185.3 t/s | 131K | EASY RUN | C31 |
Q4_K_M·800.0 t/s tok/s·131K ctx·EASY RUN | Q4_K_M | 0.7 GB | 800.0 t/s | 131K | EASY RUN | D27 |
Q4_K_M·800.0 t/s tok/s·33K ctx·EASY RUN | Q4_K_M | 0.7 GB | 800.0 t/s | 33K | EASY RUN | D27 |
Q4_K_M·522.8 t/s tok/s·2K ctx·EASY RUN | Q4_K_M | 1.0 GB | 522.8 t/s | 2K | EASY RUN | D27 |
Q4_K_M·400.0 t/s tok/s·8K ctx·EASY RUN | Q4_K_M | 1.3 GB | 400.0 t/s | 8K | EASY RUN | D28 |
AMD Radeon RX 7900 XTX Specifications
- Brand
- AMD
- Architecture
- RDNA 3
- VRAM
- 24.0 GB GDDR6
- Memory Bandwidth
- 960.0 GB/s
- Stream Processors
- 6,144
- FP16 Performance
- 122.80 TFLOPS
- TDP
- 355W
- Release Date
- 2022-12-13
- MSRP
- $999
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GPUs to Consider Over AMD Radeon RX 7900 XTX
Similar GPUs and upgrades with more VRAM or higher bandwidth for AI
Frequently Asked Questions
- Can AMD Radeon RX 7900 XTX run Gemma 3 27B IT?
Yes, the AMD Radeon RX 7900 XTX with 24 GB can run Gemma 3 27B IT, Gemma 2 27B IT, Mistral Small 24B Instruct 2501, and 1130 other models. 125 models run at excellent quality, and 196 at good quality. Check the compatibility table above for the full list with VRAM usage and estimated speed.
- Is AMD Radeon RX 7900 XTX good for AI?
The AMD Radeon RX 7900 XTX has 24 GB of GDDR6, making it excellent for running local AI models. It supports 321 models at good quality or better. With 960.0 GB/s memory bandwidth, it delivers fast token generation speeds. This is an enthusiast-grade GPU that handles most popular open-source LLMs.
- How many parameters can AMD Radeon RX 7900 XTX handle?
With 24 GB, the AMD Radeon RX 7900 XTX supports models from 3B to 30B parameters depending on quantization level. At Q4_K_M (the recommended sweet spot), you can fit roughly 40B parameters. This means 7B models at high quality (Q6/Q8) or 30B+ models at Q4.
- What quantization should I use on AMD Radeon RX 7900 XTX?
For the best balance of quality and speed on the AMD Radeon RX 7900 XTX, start with Q4_K_M — it preserves ~85% of the original model quality while keeping VRAM usage reasonable. With 24+ GB, you have the headroom to run 7B models at Q5_K_M or even Q6_K for noticeably better output quality. For larger 30B models, Q4_K_M remains the sweet spot.
- How fast is AMD Radeon RX 7900 XTX for AI inference?
With 960.0 GB/s memory bandwidth, the AMD Radeon RX 7900 XTX achieves approximately 117 tokens/sec on a 7B model at Q4_K_M — that's very fast, well above conversational speed. A 14B model runs at ~59 tok/s. Token generation speed scales inversely with model size — smaller models are significantly faster.
tok/s = (960 GB/s ÷ model GB) × efficiency
Smaller models = faster inference. Memory bandwidth is the main bottleneck for token generation speed.
Estimated speed on AMD Radeon RX 7900 XTX
~29 tok/s~29 tok/s~35 tok/s~40 tok/sReal-world results typically within ±20%. Speed depends on quantization kernel, batch size, and software stack.
- What's the best model for AMD Radeon RX 7900 XTX?
The top-rated models for the AMD Radeon RX 7900 XTX are Gemma 3 27B IT, Gemma 2 27B IT, Mistral Small 24B Instruct 2501. The best choice depends on your use case: coding assistants benefit from code-tuned models, while general chat works well with instruction-tuned models like Llama or Qwen.