Quick Answer: RX 7900 XTX offers 24GB VRAM and starts around $99.99. It delivers approximately 192 tokens/sec on WeiboAI/VibeThinker-1.5B. It typically draws 355W under load.
RX 7900 XTX gives AMD builders a 24GB option with competitive throughput for 7B–13B LLMs and diffusion workloads. Use ROCm-compatible stacks like llama.cpp or vLLM (AMD fork).
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| Model | Quantization | Tokens/sec | VRAM used |
|---|---|---|---|
| WeiboAI/VibeThinker-1.5B | Q4 | 191.98 tok/sEstimated Auto-generated benchmark | 1GB |
| Qwen/Qwen2.5-3B-Instruct | Q4 | 191.65 tok/sEstimated Auto-generated benchmark | 2GB |
| ibm-research/PowerMoE-3b | Q4 | 191.32 tok/sEstimated Auto-generated benchmark | 2GB |
| allenai/OLMo-2-0425-1B | Q4 | 190.23 tok/sEstimated Auto-generated benchmark | 1GB |
| ibm-granite/granite-3.3-2b-instruct | Q4 | 188.87 tok/sEstimated Auto-generated benchmark | 1GB |
| TinyLlama/TinyLlama-1.1B-Chat-v1.0 | Q4 | 187.96 tok/sEstimated Auto-generated benchmark | 1GB |
| unsloth/Llama-3.2-3B-Instruct | Q4 | 186.92 tok/sEstimated Auto-generated benchmark | 2GB |
| tencent/HunyuanOCR | Q4 | 183.83 tok/sEstimated Auto-generated benchmark | 1GB |
| apple/OpenELM-1_1B-Instruct | Q4 | 182.32 tok/sEstimated Auto-generated benchmark | 1GB |
| unsloth/Llama-3.2-1B-Instruct | Q4 | 182.10 tok/sEstimated Auto-generated benchmark | 1GB |
| inference-net/Schematron-3B | Q4 | 182.08 tok/sEstimated Auto-generated benchmark | 2GB |
| meta-llama/Llama-Guard-3-1B | Q4 | 180.34 tok/sEstimated Auto-generated benchmark | 1GB |
| unsloth/gemma-3-1b-it | Q4 | 179.86 tok/sEstimated Auto-generated benchmark | 1GB |
| facebook/sam3 | Q4 | 178.70 tok/sEstimated Auto-generated benchmark | 1GB |
| meta-llama/Llama-3.2-3B-Instruct | Q4 | 177.84 tok/sEstimated Auto-generated benchmark | 2GB |
| google/gemma-2b | Q4 | 177.84 tok/sEstimated Auto-generated benchmark | 1GB |
| google-bert/bert-base-uncased | Q4 | 176.40 tok/sEstimated Auto-generated benchmark | 1GB |
| deepseek-ai/DeepSeek-OCR | Q4 | 173.09 tok/sEstimated Auto-generated benchmark | 2GB |
| meta-llama/Llama-3.2-3B | Q4 | 171.53 tok/sEstimated Auto-generated benchmark | 2GB |
| meta-llama/Llama-3.2-1B | Q4 | 171.50 tok/sEstimated Auto-generated benchmark | 1GB |
| context-labs/meta-llama-Llama-3.2-3B-Instruct-FP16 | Q4 | 170.23 tok/sEstimated Auto-generated benchmark | 2GB |
| Qwen/Qwen2.5-3B | Q4 | 169.92 tok/sEstimated Auto-generated benchmark | 2GB |
| google/gemma-2-2b-it | Q4 | 169.47 tok/sEstimated Auto-generated benchmark | 1GB |
| bigcode/starcoder2-3b | Q4 | 167.23 tok/sEstimated Auto-generated benchmark | 2GB |
| LiquidAI/LFM2-1.2B | Q4 | 166.42 tok/sEstimated Auto-generated benchmark | 1GB |
| google/embeddinggemma-300m | Q4 | 162.44 tok/sEstimated Auto-generated benchmark | 1GB |
| microsoft/Phi-4-multimodal-instruct | Q4 | 159.91 tok/sEstimated Auto-generated benchmark | 4GB |
| GSAI-ML/LLaDA-8B-Instruct | Q4 | 159.65 tok/sEstimated Auto-generated benchmark | 4GB |
| meta-llama/Llama-3.2-1B-Instruct | Q4 | 159.52 tok/sEstimated Auto-generated benchmark | 1GB |
| deepseek-ai/deepseek-coder-1.3b-instruct | Q4 | 159.48 tok/sEstimated Auto-generated benchmark | 2GB |
| petals-team/StableBeluga2 | Q4 | 158.43 tok/sEstimated Auto-generated benchmark | 4GB |
| unsloth/mistral-7b-v0.3-bnb-4bit | Q4 | 158.16 tok/sEstimated Auto-generated benchmark | 4GB |
| google/gemma-3-1b-it | Q4 | 158.14 tok/sEstimated Auto-generated benchmark | 1GB |
| nari-labs/Dia2-2B | Q4 | 157.92 tok/sEstimated Auto-generated benchmark | 2GB |
| Gensyn/Qwen2.5-0.5B-Instruct | Q4 | 157.49 tok/sEstimated Auto-generated benchmark | 3GB |
| google-t5/t5-3b | Q4 | 157.37 tok/sEstimated Auto-generated benchmark | 2GB |
| Qwen/Qwen3-0.6B | Q4 | 156.94 tok/sEstimated Auto-generated benchmark | 3GB |
| microsoft/Phi-3.5-vision-instruct | Q4 | 156.93 tok/sEstimated Auto-generated benchmark | 4GB |
| kaitchup/Phi-3-mini-4k-instruct-gptq-4bit | Q4 | 156.86 tok/sEstimated Auto-generated benchmark | 2GB |
| Qwen/Qwen3-4B-Thinking-2507 | Q4 | 156.77 tok/sEstimated Auto-generated benchmark | 2GB |
| meta-llama/Llama-2-7b-chat-hf | Q4 | 156.70 tok/sEstimated Auto-generated benchmark | 4GB |
| meta-llama/Llama-3.1-8B | Q4 | 156.43 tok/sEstimated Auto-generated benchmark | 4GB |
| HuggingFaceH4/zephyr-7b-beta | Q4 | 156.01 tok/sEstimated Auto-generated benchmark | 4GB |
| skt/kogpt2-base-v2 | Q4 | 155.91 tok/sEstimated Auto-generated benchmark | 4GB |
| microsoft/Phi-3.5-mini-instruct | Q4 | 155.34 tok/sEstimated Auto-generated benchmark | 2GB |
| IlyaGusev/saiga_llama3_8b | Q4 | 154.62 tok/sEstimated Auto-generated benchmark | 4GB |
| Qwen/Qwen3-4B-Thinking-2507-FP8 | Q4 | 154.60 tok/sEstimated Auto-generated benchmark | 2GB |
| Qwen/Qwen2.5-1.5B | Q4 | 154.56 tok/sEstimated Auto-generated benchmark | 3GB |
| meta-llama/Meta-Llama-3-8B | Q4 | 154.46 tok/sEstimated Auto-generated benchmark | 4GB |
| unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit | Q4 | 154.43 tok/sEstimated Auto-generated benchmark | 4GB |
Note: Performance estimates are calculated. Real results may vary. Methodology · Submit real data
| Model | Quantization | Verdict | Estimated speed | VRAM needed |
|---|---|---|---|---|
| HuggingFaceH4/zephyr-7b-beta | Q4 | Fits comfortably | 156.01 tok/sEstimated | 4GB (have 24GB) |
| liuhaotian/llava-v1.5-7b | Q4 | Fits comfortably | 150.77 tok/sEstimated | 4GB (have 24GB) |
| Qwen/Qwen2.5-72B-Instruct | Q8 | Not supported | 20.20 tok/sEstimated | 70GB (have 24GB) |
| Qwen/Qwen2.5-72B-Instruct | FP16 | Not supported | 11.56 tok/sEstimated | 141GB (have 24GB) |
| BSC-LT/salamandraTA-7b-instruct | Q8 | Fits comfortably | 104.26 tok/sEstimated | 7GB (have 24GB) |
| deepseek-ai/deepseek-coder-33b-instruct | Q4 | Fits comfortably | 50.76 tok/sEstimated | 17GB (have 24GB) |
| deepseek-ai/deepseek-coder-33b-instruct | Q8 | Not supported | 35.83 tok/sEstimated | 34GB (have 24GB) |
| meta-llama/Llama-3.2-3B-Instruct | Q4 | Fits comfortably | 141.65 tok/sEstimated | 2GB (have 24GB) |
| meta-llama/Llama-3.2-3B-Instruct | Q8 | Fits comfortably | 97.68 tok/sEstimated | 3GB (have 24GB) |
| Qwen/Qwen3-Next-80B-A3B-Thinking | Q8 | Not supported | 21.52 tok/sEstimated | 78GB (have 24GB) |
| Qwen/Qwen3-Next-80B-A3B-Thinking | FP16 | Not supported | 10.51 tok/sEstimated | 156GB (have 24GB) |
| meta-llama/Llama-2-13b-chat-hf | Q4 | Fits comfortably | 102.99 tok/sEstimated | 7GB (have 24GB) |
| Qwen/Qwen3-30B-A3B-Thinking-2507 | FP16 | Not supported | 27.41 tok/sEstimated | 61GB (have 24GB) |
| lmstudio-community/Qwen3-Coder-30B-A3B-Instruct-MLX-5bit | Q8 | Not supported | 58.16 tok/sEstimated | 31GB (have 24GB) |
| lmstudio-community/Qwen3-Coder-30B-A3B-Instruct-MLX-5bit | FP16 | Not supported | 33.16 tok/sEstimated | 61GB (have 24GB) |
| Alibaba-NLP/gte-Qwen2-1.5B-instruct | Q4 | Fits comfortably | 149.48 tok/sEstimated | 3GB (have 24GB) |
| mistralai/Mistral-Small-Instruct-2409 | Q4 | Fits comfortably | 75.77 tok/sEstimated | 11GB (have 24GB) |
| mistralai/Mistral-Small-Instruct-2409 | Q8 | Fits (tight) | 57.28 tok/sEstimated | 23GB (have 24GB) |
| google/gemma-2-27b-it | Q4 | Fits comfortably | 77.91 tok/sEstimated | 14GB (have 24GB) |
| google/gemma-2-27b-it | Q8 | Not supported | 58.13 tok/sEstimated | 28GB (have 24GB) |
| black-forest-labs/FLUX.2-dev | FP16 | Fits comfortably | 60.22 tok/sEstimated | 16GB (have 24GB) |
| OpenPipe/Qwen3-14B-Instruct | Q8 | Fits comfortably | 82.40 tok/sEstimated | 14GB (have 24GB) |
| OpenPipe/Qwen3-14B-Instruct | FP16 | Not supported | 38.62 tok/sEstimated | 29GB (have 24GB) |
| openai/gpt-oss-120b | Q4 | Not supported | 27.67 tok/sEstimated | 59GB (have 24GB) |
| Qwen/Qwen3-4B | Q4 | Fits comfortably | 132.22 tok/sEstimated | 2GB (have 24GB) |
| OpenPipe/Qwen3-14B-Instruct | Q4 | Fits comfortably | 98.86 tok/sEstimated | 7GB (have 24GB) |
| openai-community/gpt2-xl | Q4 | Fits comfortably | 135.22 tok/sEstimated | 4GB (have 24GB) |
| meta-llama/Llama-3.2-3B-Instruct | Q8 | Fits comfortably | 117.75 tok/sEstimated | 3GB (have 24GB) |
| deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct | Q4 | Fits comfortably | 144.19 tok/sEstimated | 4GB (have 24GB) |
| facebook/opt-125m | Q8 | Fits comfortably | 100.64 tok/sEstimated | 7GB (have 24GB) |
| kaitchup/Phi-3-mini-4k-instruct-gptq-4bit | Q4 | Fits comfortably | 156.86 tok/sEstimated | 2GB (have 24GB) |
| kaitchup/Phi-3-mini-4k-instruct-gptq-4bit | FP16 | Fits comfortably | 59.08 tok/sEstimated | 9GB (have 24GB) |
| Qwen/Qwen2.5-1.5B | FP16 | Fits comfortably | 54.08 tok/sEstimated | 11GB (have 24GB) |
| Qwen/Qwen2.5-14B-Instruct | Q4 | Fits comfortably | 102.01 tok/sEstimated | 7GB (have 24GB) |
| Qwen/Qwen2.5-14B-Instruct | Q8 | Fits comfortably | 70.33 tok/sEstimated | 14GB (have 24GB) |
| meta-llama/Llama-3.3-70B-Instruct | FP16 | Not supported | 20.73 tok/sEstimated | 137GB (have 24GB) |
| Qwen/Qwen3-Embedding-8B | Q4 | Fits comfortably | 150.33 tok/sEstimated | 4GB (have 24GB) |
| Qwen/Qwen3-Embedding-8B | Q8 | Fits comfortably | 102.05 tok/sEstimated | 9GB (have 24GB) |
| Qwen/Qwen3-14B | Q8 | Fits comfortably | 74.21 tok/sEstimated | 14GB (have 24GB) |
| Qwen/Qwen3-14B | FP16 | Not supported | 38.58 tok/sEstimated | 29GB (have 24GB) |
| deepseek-ai/DeepSeek-R1-Distill-Qwen-7B | Q4 | Fits comfortably | 152.24 tok/sEstimated | 4GB (have 24GB) |
| deepseek-ai/DeepSeek-R1-Distill-Qwen-7B | Q8 | Fits comfortably | 105.47 tok/sEstimated | 7GB (have 24GB) |
| meta-llama/Llama-2-7b-hf | FP16 | Fits comfortably | 57.52 tok/sEstimated | 15GB (have 24GB) |
| deepseek-ai/DeepSeek-R1-Distill-Llama-8B | Q4 | Fits comfortably | 142.19 tok/sEstimated | 4GB (have 24GB) |
| deepseek-ai/DeepSeek-R1-Distill-Llama-8B | Q8 | Fits comfortably | 109.91 tok/sEstimated | 9GB (have 24GB) |
| Qwen/Qwen2-0.5B | Q4 | Fits comfortably | 139.70 tok/sEstimated | 3GB (have 24GB) |
| Qwen/Qwen2-0.5B | Q8 | Fits comfortably | 101.81 tok/sEstimated | 5GB (have 24GB) |
| deepseek-ai/deepseek-coder-1.3b-instruct | Q4 | Fits comfortably | 159.48 tok/sEstimated | 2GB (have 24GB) |
| deepseek-ai/deepseek-coder-1.3b-instruct | Q8 | Fits comfortably | 121.88 tok/sEstimated | 3GB (have 24GB) |
| Qwen/Qwen3-4B-Thinking-2507 | FP16 | Fits comfortably | 59.02 tok/sEstimated | 9GB (have 24GB) |
Note: Performance estimates are calculated. Real results may vary. Methodology · Submit real data
Data-backed answers pulled from community benchmarks, manufacturer specs, and live pricing.
On qwen3-30B Q4, Vulkan decode hits ~117 tok/sec once a 32K context fills, while ROCm drops to ~12 tok/sec—making Vulkan the faster option for long prompts.
Source: Reddit – /r/LocalLLaMA (mrdpho0)
The same benchmarks show Vulkan prompt prefill at ~486 tok/s on Windows drivers versus ~432 tok/s on ROCm, highlighting the driver advantage.
Source: Reddit – /r/LocalLLaMA (mrdpho0)
Yes. Builders highlight Ryzen AI 395 mini-PCs with RX 7900-class GPUs that can load 70B Q8 contexts—something 24 GB NVIDIA cards can’t do—though throughput is slower.
Source: Reddit – /r/LocalLLaMA (mqupq0a)
Not yet—FlashAttention under Vulkan falls back to the CPU on 7900 XTX, so enabling it doesn’t improve throughput the way it does on NVIDIA cards.
Source: Reddit – /r/LocalLLaMA (mrdpho0)
RX 7900 XTX offers 24 GB GDDR6 and a 355 W TBP. As of Nov 2025 Amazon listed it at $899 in stock.
Explore how RX 7900 XT stacks up for local inference workloads.
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Side-by-side VRAM, throughput, efficiency, and pricing benchmarks for both GPUs.
Side-by-side VRAM, throughput, efficiency, and pricing benchmarks for both GPUs.
Side-by-side VRAM, throughput, efficiency, and pricing benchmarks for both GPUs.