Best AI Models for AMD Radeon RX 7900 XT (20.0GB)
20 GB is a comfortable mid-range tier for local AI. Most 7B–13B models run smoothly at good quantization levels, and smaller models can run at near-full precision.
This memory tier strikes a nice balance between price and capability. Popular 7B models like Llama 3 8B, Mistral 7B, and Qwen 2.5 7B all run very well at Q4_K_M quantization with fast inference and reasonable context windows. You can also fit some larger 13B models at Q3–Q4, though you'll want to keep context lengths modest. Small models like Phi 3 Mini (3.8B) practically fly at Q8 or even FP16 quality.
Runs Well
- 7B models at Q4–Q6 quality with good speed
- Small models (3B–4B) at Q8 or FP16
- 9B models (Gemma 2 9B) at Q4_K_M
Challenging
- 13B–14B models need Q3 or lower
- 30B+ models do not fit in VRAM
- Long context (>8K tokens) with larger models
What LLMs Can AMD Radeon RX 7900 XT Run?
94 models · 7 excellent · 3 good
Showing compatibility for AMD Radeon RX 7900 XT
| Model | Quant | VRAM | Speed | Context | Status | Grade |
|---|---|---|---|---|---|---|
Q4_K_M·89.4 t/s tok/s·33K ctx·EASY RUN | Q4_K_M | 4.9 GB | 89.4 t/s | 33K | EASY RUN | C40 |
Q4_K_M·79.7 t/s tok/s·131K ctx·EASY RUN | Q4_K_M | 5.5 GB | 79.7 t/s | 131K | EASY RUN | C43 |
Q4_K_M·76.5 t/s tok/s·66K ctx·EASY RUN | Q4_K_M | 5.8 GB | 76.5 t/s | 66K | EASY RUN | C44 |
Q4_K_M·81.6 t/s tok/s·131K ctx·EASY RUN | Q4_K_M | 5.4 GB | 81.6 t/s | 131K | EASY RUN | C42 |
Q4_K_M·81.6 t/s tok/s·131K ctx·EASY RUN | Q4_K_M | 5.4 GB | 81.6 t/s | 131K | EASY RUN | C42 |
Q4_K_M·81.2 t/s tok/s·16K ctx·EASY RUN | Q4_K_M | 5.4 GB | 81.2 t/s | 16K | EASY RUN | C42 |
Q4_K_M·88.2 t/s tok/s·131K ctx·EASY RUN | Q4_K_M | 5.0 GB | 88.2 t/s | 131K | EASY RUN | C40 |
Q4_K_M·75.9 t/s tok/s·4K ctx·EASY RUN | Q4_K_M | 5.8 GB | 75.9 t/s | 4K | EASY RUN | C44 |
Q4_K_M·64.9 t/s tok/s·8K ctx·FAIR FIT | Q4_K_M | 6.8 GB | 64.9 t/s | 8K | FAIR FIT | B49 |
Q4_K_M·151.7 t/s tok/s·41K ctx·EASY RUN | Q4_K_M | 2.9 GB | 151.7 t/s | 41K | EASY RUN | C32 |
Q4_K_M·128.3 t/s tok/s·131K ctx·EASY RUN | Q4_K_M | 3.4 GB | 128.3 t/s | 131K | EASY RUN | C34 |
Q4_K_M·24.5 t/s tok/s·8K ctx·FAIR FIT | Q4_K_M | 18.0 GB | 24.5 t/s | 8K | FAIR FIT | B52 |
Q4_K_M·86.3 t/s tok/s·33K ctx·EASY RUN | Q4_K_M | 5.1 GB | 86.3 t/s | 33K | EASY RUN | C41 |
Q4_K_M·84.9 t/s tok/s·EASY RUN | Q4_K_M | 5.2 GB | 84.9 t/s | — | EASY RUN | C41 |
Q4_K_M·24.3 t/s tok/s·131K ctx·FAIR FIT | Q4_K_M | 18.1 GB | 24.3 t/s | 131K | FAIR FIT | B48 |
Q4_K_M·151.7 t/s tok/s·262K ctx·EASY RUN | Q4_K_M | 2.9 GB | 151.7 t/s | 262K | EASY RUN | C32 |
AMD Radeon RX 7900 XT Specifications
- Brand
- AMD
- Architecture
- RDNA 3
- VRAM
- 20.0 GB GDDR6
- Memory Bandwidth
- 800.0 GB/s
- Stream Processors
- 5,376
- FP16 Performance
- 103.20 TFLOPS
- TDP
- 315W
- Release Date
- 2022-12-13
- MSRP
- $899
Get Started
GPUs to Consider Over AMD Radeon RX 7900 XT
Similar GPUs and upgrades with more VRAM or higher bandwidth for AI
NVIDIA GeForce RTX 5090
NVIDIA · Blackwell
NVIDIA GeForce RTX 3090 Ti
NVIDIA · Ampere
NVIDIA GeForce RTX 4090
NVIDIA · Ada Lovelace
AMD Radeon RX 7900 XTX
AMD · RDNA 3
NVIDIA GeForce RTX 3090
NVIDIA · Ampere
NVIDIA GeForce RTX 5090 Laptop GPU
NVIDIA · Blackwell
Frequently Asked Questions
- Can AMD Radeon RX 7900 XT run Mistral Small 3.2 24B Instruct 2506?
Yes, the AMD Radeon RX 7900 XT with 20 GB can run Mistral Small 3.2 24B Instruct 2506, GPT OSS 20B, Mistral Small 24B Instruct 2501, and 1233 other models. 50 models run at excellent quality, and 103 at good quality. Check the compatibility table above for the full list with VRAM usage and estimated speed.
- Is AMD Radeon RX 7900 XT good for AI?
The AMD Radeon RX 7900 XT has 20 GB of GDDR6, making it very good for running local AI models. It supports 153 models at good quality or better. With 800.0 GB/s memory bandwidth, it delivers fast token generation speeds. This is a solid mid-range card for running 7B–14B parameter models at good quality.
- How many parameters can AMD Radeon RX 7900 XT handle?
With 20 GB, the AMD Radeon RX 7900 XT supports models from 3B to 14B parameters depending on quantization level. At Q4_K_M (the recommended sweet spot), you can fit roughly 33B parameters. 7B models run at high quality (Q5/Q6), while 14B models fit comfortably at Q4.
- What quantization should I use on AMD Radeon RX 7900 XT?
For the best balance of quality and speed on the AMD Radeon RX 7900 XT, start with Q4_K_M — it preserves ~85% of the original model quality while keeping VRAM usage reasonable. You can step up to Q5_K_M for 7B models to get better quality. For 14B models that just barely fit, Q4_K_M is ideal.
- How fast is AMD Radeon RX 7900 XT for AI inference?
With 800.0 GB/s memory bandwidth, the AMD Radeon RX 7900 XT achieves approximately 98 tokens/sec on a 7B model at Q4_K_M — that's very fast, well above conversational speed. A 14B model runs at ~49 tok/s. Token generation speed scales inversely with model size — smaller models are significantly faster.
tok/s = (800 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 XT
~29 tok/s~33 tok/s~30 tok/s~30 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 XT?
The top-rated models for the AMD Radeon RX 7900 XT are Mistral Small 3.2 24B Instruct 2506, GPT OSS 20B, 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.
- What power supply and cooling does AMD Radeon RX 7900 XT need?
The AMD Radeon RX 7900 XT has a TDP of 315 W. A good rule of thumb is to provide at least double the GPU's TDP to cover the rest of the system — that means a 650 W PSU or larger. At this power level, a high-airflow case matters: aim for at least two front intake fans and one rear exhaust, with tidy cabling so hot air isn't trapped around the card. LLM inference sustains full GPU load continuously — longer and more consistently than most gaming workloads — so also make sure your CPU cooler can keep up under combined load.