Quick Answer: RX 7900 XTX offers 24GB VRAM and starts around $99.99. It delivers approximately 199 tokens/sec on deepseek-ai/DeepSeek-OCR. 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).
With 24GB VRAM, RX 7900 XTX can run models up to approximately 60B parameters using 4-bit quantization. This handles most popular models including Llama 3 70B, Mistral 7B, and larger.
| Model | Quantization | Tokens/sec | VRAM used |
|---|---|---|---|
| deepseek-ai/DeepSeek-OCR | Q4 | 199.21 tok/sEstimated Auto-generated benchmark | 2GB |
| nari-labs/Dia2-2B | Q4 | 190.29 tok/sEstimated Auto-generated benchmark | 2GB |
| bigcode/starcoder2-3b | Q4 | 188.93 tok/sEstimated Auto-generated benchmark | 2GB |
| google/embeddinggemma-300m | Q4 | 188.16 tok/sEstimated Auto-generated benchmark | 1GB |
| meta-llama/Llama-Guard-3-1B | Q4 | 186.54 tok/sEstimated Auto-generated benchmark | 1GB |
| WeiboAI/VibeThinker-1.5B | Q4 | 186.28 tok/sEstimated Auto-generated benchmark | 1GB |
| unsloth/Llama-3.2-1B-Instruct | Q4 | 185.93 tok/sEstimated Auto-generated benchmark | 1GB |
| context-labs/meta-llama-Llama-3.2-3B-Instruct-FP16 | Q4 | 185.53 tok/sEstimated Auto-generated benchmark | 2GB |
| inference-net/Schematron-3B | Q4 | 185.30 tok/sEstimated Auto-generated benchmark | 2GB |
| meta-llama/Llama-3.2-3B-Instruct | Q4 | 184.83 tok/sEstimated Auto-generated benchmark | 2GB |
| ibm-research/PowerMoE-3b | Q4 | 184.13 tok/sEstimated Auto-generated benchmark | 2GB |
| unsloth/Llama-3.2-3B-Instruct | Q4 | 183.44 tok/sEstimated Auto-generated benchmark | 2GB |
| LiquidAI/LFM2-1.2B | Q4 | 183.41 tok/sEstimated Auto-generated benchmark | 1GB |
| unsloth/gemma-3-1b-it | Q4 | 182.82 tok/sEstimated Auto-generated benchmark | 1GB |
| meta-llama/Llama-3.2-1B-Instruct | Q4 | 182.47 tok/sEstimated Auto-generated benchmark | 1GB |
| apple/OpenELM-1_1B-Instruct | Q4 | 182.20 tok/sEstimated Auto-generated benchmark | 1GB |
| deepseek-ai/DeepSeek-OCR-2 | Q4 | 182.13 tok/sEstimated Auto-generated benchmark | 2GB |
| google/gemma-2-2b-it | Q4 | 180.39 tok/sEstimated Auto-generated benchmark | 1GB |
| nineninesix/kani-tts-2-en | Q4 | 180.23 tok/sEstimated Auto-generated benchmark | 1GB |
| meta-llama/Llama-3.2-1B | Q4 | 179.22 tok/sEstimated Auto-generated benchmark | 1GB |
| google/gemma-3-1b-it | Q4 | 179.03 tok/sEstimated Auto-generated benchmark | 1GB |
| google-bert/bert-base-uncased | Q4 | 178.73 tok/sEstimated Auto-generated benchmark | 1GB |
| google/gemma-2b | Q4 | 178.05 tok/sEstimated Auto-generated benchmark | 1GB |
| google-t5/t5-3b | Q4 | 175.43 tok/sEstimated Auto-generated benchmark | 2GB |
| Qwen/Qwen2.5-3B | Q4 | 175.30 tok/sEstimated Auto-generated benchmark | 2GB |
| facebook/sam3 | Q4 | 174.88 tok/sEstimated Auto-generated benchmark | 1GB |
| allenai/OLMo-2-0425-1B | Q4 | 173.78 tok/sEstimated Auto-generated benchmark | 1GB |
| TinyLlama/TinyLlama-1.1B-Chat-v1.0 | Q4 | 170.32 tok/sEstimated Auto-generated benchmark | 1GB |
| Qwen/Qwen3-ASR-1.7B | Q4 | 168.09 tok/sEstimated Auto-generated benchmark | 2GB |
| Qwen/Qwen2.5-3B-Instruct | Q4 | 164.12 tok/sEstimated Auto-generated benchmark | 2GB |
| meta-llama/Llama-3.2-3B | Q4 | 161.14 tok/sEstimated Auto-generated benchmark | 2GB |
| deepseek-ai/deepseek-coder-1.3b-instruct | Q4 | 160.24 tok/sEstimated Auto-generated benchmark | 2GB |
| trl-internal-testing/tiny-LlamaForCausalLM-3.2 | Q4 | 159.96 tok/sEstimated Auto-generated benchmark | 4GB |
| meta-llama/Llama-Guard-3-8B | Q4 | 159.27 tok/sEstimated Auto-generated benchmark | 4GB |
| HuggingFaceTB/SmolLM2-135M | Q4 | 159.23 tok/sEstimated Auto-generated benchmark | 4GB |
| Qwen/Qwen2.5-1.5B-Instruct | Q4 | 159.04 tok/sEstimated Auto-generated benchmark | 3GB |
| Qwen/Qwen3-4B-Base | Q4 | 159.03 tok/sEstimated Auto-generated benchmark | 2GB |
| ibm-granite/granite-3.3-2b-instruct | Q4 | 158.99 tok/sEstimated Auto-generated benchmark | 1GB |
| Qwen/Qwen3-8B | Q4 | 158.32 tok/sEstimated Auto-generated benchmark | 4GB |
| microsoft/phi-4 | Q4 | 158.27 tok/sEstimated Auto-generated benchmark | 4GB |
| bigscience/bloomz-560m | Q4 | 158.18 tok/sEstimated Auto-generated benchmark | 4GB |
| EleutherAI/gpt-neo-125m | Q4 | 157.94 tok/sEstimated Auto-generated benchmark | 4GB |
| openai-community/gpt2-xl | Q4 | 157.61 tok/sEstimated Auto-generated benchmark | 4GB |
| tencent/HunyuanOCR | Q4 | 157.27 tok/sEstimated Auto-generated benchmark | 1GB |
| microsoft/Phi-3.5-mini-instruct | Q4 | 157.27 tok/sEstimated Auto-generated benchmark | 2GB |
| Alibaba-NLP/gte-Qwen2-1.5B-instruct | Q4 | 157.03 tok/sEstimated Auto-generated benchmark | 3GB |
| moonshotai/Kimi-K2.5 | Q4 | 157.01 tok/sEstimated Auto-generated benchmark | 4GB |
| Qwen/Qwen2.5-Coder-1.5B | Q4 | 156.03 tok/sEstimated Auto-generated benchmark | 3GB |
| google/gemma-3-270m-it | Q4 | 155.46 tok/sEstimated Auto-generated benchmark | 4GB |
| GSAI-ML/LLaDA-8B-Base | Q4 | 155.32 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 |
|---|---|---|---|---|
| openai-community/gpt2 | Q8 | Fits comfortably | 92.26 tok/sEstimated | 7GB (have 24GB) |
| openai-community/gpt2 | FP16 | Fits comfortably | 50.88 tok/sEstimated | 15GB (have 24GB) |
| Qwen/Qwen2.5-7B-Instruct | Q4 | Fits comfortably | 151.90 tok/sEstimated | 4GB (have 24GB) |
| Qwen/Qwen2.5-7B-Instruct | Q8 | Fits comfortably | 102.21 tok/sEstimated | 7GB (have 24GB) |
| Qwen/Qwen2.5-7B-Instruct | FP16 | Fits comfortably | 52.78 tok/sEstimated | 15GB (have 24GB) |
| Qwen/Qwen3-0.6B | Q4 | Fits comfortably | 143.46 tok/sEstimated | 3GB (have 24GB) |
| Qwen/Qwen3-0.6B | Q8 | Fits comfortably | 102.36 tok/sEstimated | 6GB (have 24GB) |
| Qwen/Qwen3-0.6B | FP16 | Fits comfortably | 57.97 tok/sEstimated | 13GB (have 24GB) |
| Gensyn/Qwen2.5-0.5B-Instruct | Q4 | Fits comfortably | 141.83 tok/sEstimated | 3GB (have 24GB) |
| Gensyn/Qwen2.5-0.5B-Instruct | Q8 | Fits comfortably | 100.85 tok/sEstimated | 5GB (have 24GB) |
| Gensyn/Qwen2.5-0.5B-Instruct | FP16 | Fits comfortably | 59.76 tok/sEstimated | 11GB (have 24GB) |
| meta-llama/Llama-3.1-8B-Instruct | Q4 | Fits comfortably | 154.16 tok/sEstimated | 4GB (have 24GB) |
| meta-llama/Llama-3.1-8B-Instruct | Q8 | Fits comfortably | 101.00 tok/sEstimated | 9GB (have 24GB) |
| meta-llama/Llama-3.1-8B-Instruct | FP16 | Fits comfortably | 51.34 tok/sEstimated | 17GB (have 24GB) |
| dphn/dolphin-2.9.1-yi-1.5-34b | Q4 | Fits comfortably | 55.53 tok/sEstimated | 17GB (have 24GB) |
| dphn/dolphin-2.9.1-yi-1.5-34b | Q8 | Not supported | 34.18 tok/sEstimated | 35GB (have 24GB) |
| dphn/dolphin-2.9.1-yi-1.5-34b | FP16 | Not supported | 17.61 tok/sEstimated | 70GB (have 24GB) |
| openai/gpt-oss-20b | Q4 | Fits comfortably | 80.71 tok/sEstimated | 10GB (have 24GB) |
| openai/gpt-oss-20b | Q8 | Fits comfortably | 55.97 tok/sEstimated | 20GB (have 24GB) |
| openai/gpt-oss-20b | FP16 | Not supported | 28.32 tok/sEstimated | 41GB (have 24GB) |
| google/gemma-3-1b-it | Q4 | Fits comfortably | 179.03 tok/sEstimated | 1GB (have 24GB) |
| google/gemma-3-1b-it | Q8 | Fits comfortably | 129.43 tok/sEstimated | 1GB (have 24GB) |
| google/gemma-3-1b-it | FP16 | Fits comfortably | 71.46 tok/sEstimated | 2GB (have 24GB) |
| Qwen/Qwen3-Embedding-0.6B | Q4 | Fits comfortably | 138.02 tok/sEstimated | 3GB (have 24GB) |
| Qwen/Qwen3-Embedding-0.6B | Q8 | Fits comfortably | 105.58 tok/sEstimated | 6GB (have 24GB) |
| Qwen/Qwen3-Embedding-0.6B | FP16 | Fits comfortably | 51.64 tok/sEstimated | 13GB (have 24GB) |
| Qwen/Qwen2.5-1.5B-Instruct | Q4 | Fits comfortably | 159.04 tok/sEstimated | 3GB (have 24GB) |
| Qwen/Qwen2.5-1.5B-Instruct | Q8 | Fits comfortably | 109.53 tok/sEstimated | 5GB (have 24GB) |
| Qwen/Qwen2.5-1.5B-Instruct | FP16 | Fits comfortably | 56.48 tok/sEstimated | 11GB (have 24GB) |
| facebook/opt-125m | Q4 | Fits comfortably | 132.90 tok/sEstimated | 4GB (have 24GB) |
| facebook/opt-125m | Q8 | Fits comfortably | 110.71 tok/sEstimated | 7GB (have 24GB) |
| facebook/opt-125m | FP16 | Fits comfortably | 54.85 tok/sEstimated | 15GB (have 24GB) |
| TinyLlama/TinyLlama-1.1B-Chat-v1.0 | Q4 | Fits comfortably | 170.32 tok/sEstimated | 1GB (have 24GB) |
| TinyLlama/TinyLlama-1.1B-Chat-v1.0 | Q8 | Fits comfortably | 131.80 tok/sEstimated | 1GB (have 24GB) |
| TinyLlama/TinyLlama-1.1B-Chat-v1.0 | FP16 | Fits comfortably | 60.98 tok/sEstimated | 2GB (have 24GB) |
| trl-internal-testing/tiny-Qwen2ForCausalLM-2.5 | Q4 | Fits comfortably | 141.11 tok/sEstimated | 4GB (have 24GB) |
| trl-internal-testing/tiny-Qwen2ForCausalLM-2.5 | Q8 | Fits comfortably | 103.22 tok/sEstimated | 7GB (have 24GB) |
| trl-internal-testing/tiny-Qwen2ForCausalLM-2.5 | FP16 | Fits comfortably | 49.86 tok/sEstimated | 15GB (have 24GB) |
| Qwen/Qwen3-4B-Instruct-2507 | Q4 | Fits comfortably | 136.45 tok/sEstimated | 2GB (have 24GB) |
| Qwen/Qwen3-4B-Instruct-2507 | Q8 | Fits comfortably | 110.63 tok/sEstimated | 4GB (have 24GB) |
| Qwen/Qwen3-4B-Instruct-2507 | FP16 | Fits comfortably | 57.63 tok/sEstimated | 9GB (have 24GB) |
| meta-llama/Llama-3.2-1B-Instruct | Q4 | Fits comfortably | 182.47 tok/sEstimated | 1GB (have 24GB) |
| meta-llama/Llama-3.2-1B-Instruct | Q8 | Fits comfortably | 119.05 tok/sEstimated | 1GB (have 24GB) |
| meta-llama/Llama-3.2-1B-Instruct | FP16 | Fits comfortably | 71.10 tok/sEstimated | 2GB (have 24GB) |
| openai/gpt-oss-120b | Q4 | Not supported | 28.59 tok/sEstimated | 59GB (have 24GB) |
| openai/gpt-oss-120b | Q8 | Not supported | 21.69 tok/sEstimated | 117GB (have 24GB) |
| openai/gpt-oss-120b | FP16 | Not supported | 11.03 tok/sEstimated | 235GB (have 24GB) |
| Qwen/Qwen2.5-3B-Instruct | Q4 | Fits comfortably | 164.12 tok/sEstimated | 2GB (have 24GB) |
| Qwen/Qwen2.5-3B-Instruct | Q8 | Fits comfortably | 111.12 tok/sEstimated | 3GB (have 24GB) |
| openai-community/gpt2 | Q4 | Fits comfortably | 135.03 tok/sEstimated | 4GB (have 24GB) |
Note: Performance estimates are calculated. Real results may vary. Methodology · Submit real data
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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.
Explore how RX 6900 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.
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