Every open model in our directory, ranked by how well it runs on AMD Radeon RX 9070 XT (16GB) at 4-bit. Grades weigh whether the model fits in memory and how fast it should run.
AMD Radeon RX 9070 XT has 16 GB of VRAM, enough to run 213 of the 297 open models we track at 4-bit. The largest that still fits well is AliceAI-Foundation-80B-A3B-Base (81.3B), and the top-rated is Mixtral 8x7B Instruct at about 45 tok/s.
213 of 297 models run comfortably on AMD Radeon RX 9070 XT.
See full AMD Radeon RX 9070 XT specs and pricingThe top models this device can run at 4-bit, ranked by fit and speed.
| Model | Grade | Speed | VRAM |
|---|---|---|---|
| Mixtral 8x7B InstructMistral AI | SS | 45.3 tok/s | 11.4 GB |
| AliceAI-Foundation-80B-A3B-Baseyandex | SS | 60.4 tok/s | 8.5 GB |
| DiffusionGemma 26B-A4BGoogle | SS | 49.1 tok/s | 10.5 GB |
| Gemma 4 26B-A4B ITGoogle | SS | 46.8 tok/s | 11.0 GB |
| Xing4.0-29B-A4BChina Telecom AI | SS | 46.0 tok/s | 11.2 GB |
| North Mini CodeCohere | SS | 61.5 tok/s | 8.4 GB |
| Nemotron 3 Nano OmniNVIDIA | SS | 60.4 tok/s | 8.5 GB |
| Qwen3.6 35B-A3BAlibaba | SS | 60.4 tok/s | 8.5 GB |
| Qwen3.5-35B-A3BAlibaba | SS | 60.4 tok/s | 8.5 GB |
| Llama 2 13B ChatMeta | SS | 60.9 tok/s | 8.5 GB |
| Qwen3-30B-A3BAlibaba | SS | 95.7 tok/s | 5.4 GB |
| LensVLM-9BApple | SS | 85.7 tok/s | 6.0 GB |
Mixtral 8x7B InstructMistral AI | 46.7B(12.9B active) | SS | 45.3 tok/s | 11.4 GB | |
| 81.29B(3B active) | SS | 60.4 tok/s | 8.5 GB | ||
DiffusionGemma 26B-A4BGoogle | 25.2B(3.8B active) | SS | 49.1 tok/s | 10.5 GB | |
Gemma 4 26B-A4B ITGoogle | 26B(4B active) | SS | 46.8 tok/s | 11.0 GB | |
Xing4.0-29B-A4BChina Telecom AI | 29B(4B active) | SS | 46.0 tok/s | 11.2 GB | |
North Mini CodeCohere | 30B(3B active) | SS | 61.5 tok/s | 8.4 GB | |
Nemotron 3 Nano OmniNVIDIA | 30B(3B active) | SS | 60.4 tok/s | 8.5 GB | |
Qwen3.6 35B-A3BAlibaba | 35B(3B active) | SS | 60.4 tok/s | 8.5 GB | |
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Qwen3.5-35B-A3BAlibaba | 35B(3B active) | SS | 60.4 tok/s | 8.5 GB | |
Llama 2 13B ChatMeta | 13B | SS | 60.9 tok/s | 8.5 GB | |
Qwen3-30B-A3BAlibaba | 30B(3B active) | SS | 95.7 tok/s | 5.4 GB | |
LensVLM-9BApple | 9B | SS | 85.7 tok/s | 6.0 GB | |
| 9B | SS | 85.7 tok/s | 6.0 GB | ||
| 8B | SS | 91.0 tok/s | 5.7 GB | ||
Apertus 8BEPFL, ETH Zurich, CSCS | 8B | SS | 95.4 tok/s | 5.4 GB | |
Holo4-35B-A3BHcompany | 35B(3B active) | SS | 220.3 tok/s | 2.3 GB | |
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Gemma 4 E4B ITGoogle | 4B | SS | 74.5 tok/s | 6.9 GB | |
Gemma 3 4B ITGoogle | 4B | SS | 74.5 tok/s | 6.9 GB | |
Youtu-Parsing-Omnitencent | 5.33B | SS | 80.7 tok/s | 6.4 GB | |
Mistral 7B InstructMistral AI | 7B | SS | 80.6 tok/s | 6.4 GB | |
Mellum2.1JetBrains | 12B(2.5B active) | SS | 253.6 tok/s | 2.0 GB | |
LFM2.5-8B-A1BLiquid AI | 8.3B(1.5B active) | AA | 177.3 tok/s | 2.9 GB | |
PersonaPlex 7BNVIDIA | 7B | AA | 107.6 tok/s | 4.8 GB | |
Llama 2 7B ChatMeta | 7B | AA | 107.6 tok/s | 4.8 GB | |
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| 8B | AA | 38.6 tok/s | 13.3 GB | ||

Model Compatibility
Use the calculator to weigh this device against any other, model by model, with speed and fit scores.
Straight answers to the questions we hear most often.
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Mixtral 8x7B Instruct is the highest-rated model that runs on AMD Radeon RX 9070 XT at 4-bit, at about 45 tok/s. In total, AMD Radeon RX 9070 XT can run 213 of the 297 open models we track.
We size each model at 4-bit (Q4_K_M), add the KV cache for its context length and a fixed runtime overhead, then compare that to this device VRAM. Each model is graded on how comfortably it fits and how fast it should run on this card.
It is an estimate of decode speed in tokens per second at 4-bit, based on this device memory bandwidth and each model size. Real speed depends on your runtime, batch size, and context length, so use it to compare models, not as a hard promise.
Lower-bit quantization shrinks a model so more of them fit. These grades already assume 4-bit, which is the common local default. Going below 4-bit can squeeze a larger model in at some quality cost; going above needs more VRAM and may not fit.
Mixture-of-experts models only activate a fraction of their parameters at a time, so their runtime memory is closer to the active size than the headline size. We score fit on the active parameters, which is why some very large models still fit.