Quick Answer: RX 7800 XT offers 16GB VRAM and starts around $599.99. It delivers approximately 123 tokens/sec on inference-net/Schematron-3B. It typically draws 263W under load.
This GPU offers reliable throughput for local AI workloads. Pair it with the right model quantization to hit your desired tokens/sec, and monitor prices below to catch the best deal.
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| Model | Quantization | Tokens/sec | VRAM used |
|---|---|---|---|
| inference-net/Schematron-3B | Q4 | 123.20 tok/sEstimated Auto-generated benchmark | 2GB |
| TinyLlama/TinyLlama-1.1B-Chat-v1.0 | Q4 | 122.21 tok/sEstimated Auto-generated benchmark | 1GB |
| nari-labs/Dia2-2B | Q4 | 120.63 tok/sEstimated Auto-generated benchmark | 2GB |
| google-t5/t5-3b | Q4 | 118.71 tok/sEstimated Auto-generated benchmark | 2GB |
| facebook/sam3 | Q4 | 118.16 tok/sEstimated Auto-generated benchmark | 1GB |
| deepseek-ai/DeepSeek-OCR | Q4 | 116.34 tok/sEstimated Auto-generated benchmark | 2GB |
| deepseek-ai/deepseek-coder-1.3b-instruct | Q4 | 116.13 tok/sEstimated Auto-generated benchmark | 2GB |
| Qwen/Qwen2.5-3B-Instruct | Q4 | 116.11 tok/sEstimated Auto-generated benchmark | 2GB |
| meta-llama/Llama-3.2-3B-Instruct | Q4 | 116.08 tok/sEstimated Auto-generated benchmark | 2GB |
| allenai/OLMo-2-0425-1B | Q4 | 115.10 tok/sEstimated Auto-generated benchmark | 1GB |
| tencent/HunyuanOCR | Q4 | 113.65 tok/sEstimated Auto-generated benchmark | 1GB |
| WeiboAI/VibeThinker-1.5B | Q4 | 113.51 tok/sEstimated Auto-generated benchmark | 1GB |
| meta-llama/Llama-3.2-1B-Instruct | Q4 | 111.85 tok/sEstimated Auto-generated benchmark | 1GB |
| meta-llama/Llama-Guard-3-1B | Q4 | 111.37 tok/sEstimated Auto-generated benchmark | 1GB |
| google/embeddinggemma-300m | Q4 | 111.01 tok/sEstimated Auto-generated benchmark | 1GB |
| unsloth/Llama-3.2-1B-Instruct | Q4 | 109.67 tok/sEstimated Auto-generated benchmark | 1GB |
| unsloth/Llama-3.2-3B-Instruct | Q4 | 109.17 tok/sEstimated Auto-generated benchmark | 2GB |
| LiquidAI/LFM2-1.2B | Q4 | 109.16 tok/sEstimated Auto-generated benchmark | 1GB |
| google/gemma-2b | Q4 | 109.04 tok/sEstimated Auto-generated benchmark | 1GB |
| google-bert/bert-base-uncased | Q4 | 108.34 tok/sEstimated Auto-generated benchmark | 1GB |
| Qwen/Qwen2.5-3B | Q4 | 106.93 tok/sEstimated Auto-generated benchmark | 2GB |
| meta-llama/Llama-3.2-1B | Q4 | 106.41 tok/sEstimated Auto-generated benchmark | 1GB |
| unsloth/gemma-3-1b-it | Q4 | 106.17 tok/sEstimated Auto-generated benchmark | 1GB |
| bigcode/starcoder2-3b | Q4 | 105.65 tok/sEstimated Auto-generated benchmark | 2GB |
| meta-llama/Llama-3.2-3B | Q4 | 105.56 tok/sEstimated Auto-generated benchmark | 2GB |
| context-labs/meta-llama-Llama-3.2-3B-Instruct-FP16 | Q4 | 105.51 tok/sEstimated Auto-generated benchmark | 2GB |
| ibm-research/PowerMoE-3b | Q4 | 105.35 tok/sEstimated Auto-generated benchmark | 2GB |
| apple/OpenELM-1_1B-Instruct | Q4 | 105.15 tok/sEstimated Auto-generated benchmark | 1GB |
| ibm-granite/granite-3.3-2b-instruct | Q4 | 104.03 tok/sEstimated Auto-generated benchmark | 1GB |
| NousResearch/Meta-Llama-3.1-8B-Instruct | Q4 | 103.47 tok/sEstimated Auto-generated benchmark | 4GB |
| Qwen/Qwen2-1.5B-Instruct | Q4 | 103.07 tok/sEstimated Auto-generated benchmark | 3GB |
| liuhaotian/llava-v1.5-7b | Q4 | 103.06 tok/sEstimated Auto-generated benchmark | 4GB |
| skt/kogpt2-base-v2 | Q4 | 102.89 tok/sEstimated Auto-generated benchmark | 4GB |
| meta-llama/Llama-Guard-3-8B | Q4 | 102.53 tok/sEstimated Auto-generated benchmark | 4GB |
| google/gemma-2-2b-it | Q4 | 102.40 tok/sEstimated Auto-generated benchmark | 1GB |
| Qwen/Qwen3-1.7B-Base | Q4 | 102.38 tok/sEstimated Auto-generated benchmark | 4GB |
| Qwen/Qwen3-8B-Base | Q4 | 102.31 tok/sEstimated Auto-generated benchmark | 4GB |
| deepseek-ai/DeepSeek-R1-Distill-Llama-8B | Q4 | 102.20 tok/sEstimated Auto-generated benchmark | 4GB |
| openai-community/gpt2-xl | Q4 | 102.20 tok/sEstimated Auto-generated benchmark | 4GB |
| google/gemma-3-1b-it | Q4 | 102.05 tok/sEstimated Auto-generated benchmark | 1GB |
| microsoft/Phi-3.5-mini-instruct | Q4 | 102.04 tok/sEstimated Auto-generated benchmark | 2GB |
| Qwen/Qwen2.5-0.5B | Q4 | 102.02 tok/sEstimated Auto-generated benchmark | 3GB |
| MiniMaxAI/MiniMax-M2 | Q4 | 101.81 tok/sEstimated Auto-generated benchmark | 4GB |
| Qwen/Qwen2.5-1.5B | Q4 | 101.65 tok/sEstimated Auto-generated benchmark | 3GB |
| lmstudio-community/DeepSeek-R1-0528-Qwen3-8B-MLX-4bit | Q4 | 101.25 tok/sEstimated Auto-generated benchmark | 4GB |
| Qwen/Qwen2-0.5B-Instruct | Q4 | 101.11 tok/sEstimated Auto-generated benchmark | 3GB |
| unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit | Q4 | 101.03 tok/sEstimated Auto-generated benchmark | 4GB |
| Qwen/Qwen3-4B-Base | Q4 | 101.00 tok/sEstimated Auto-generated benchmark | 2GB |
| deepseek-ai/DeepSeek-R1-Distill-Qwen-7B | Q4 | 101.00 tok/sEstimated Auto-generated benchmark | 4GB |
| Qwen/Qwen3-4B-Thinking-2507-FP8 | Q4 | 100.89 tok/sEstimated Auto-generated benchmark | 2GB |
Note: Performance estimates are calculated. Real results may vary. Methodology · Submit real data
| Model | Quantization | Verdict | Estimated speed | VRAM needed |
|---|---|---|---|---|
| google-t5/t5-3b | FP16 | Fits comfortably | 40.06 tok/sEstimated | 6GB (have 16GB) |
| Qwen/Qwen2.5-0.5B | FP16 | Fits comfortably | 38.94 tok/sEstimated | 11GB (have 16GB) |
| deepseek-ai/DeepSeek-R1-Distill-Qwen-7B | Q4 | Fits comfortably | 101.00 tok/sEstimated | 4GB (have 16GB) |
| meta-llama/Llama-2-7b-hf | Q4 | Fits comfortably | 99.03 tok/sEstimated | 4GB (have 16GB) |
| Qwen/Qwen2-0.5B | FP16 | Fits comfortably | 35.42 tok/sEstimated | 11GB (have 16GB) |
| deepseek-ai/DeepSeek-R1-0528 | FP16 | Fits (tight) | 33.81 tok/sEstimated | 15GB (have 16GB) |
| Qwen/Qwen2.5-32B-Instruct | Q8 | Not supported | 21.37 tok/sEstimated | 33GB (have 16GB) |
| LiquidAI/LFM2-1.2B | FP16 | Fits comfortably | 43.91 tok/sEstimated | 4GB (have 16GB) |
| Qwen/Qwen3-Coder-30B-A3B-Instruct | Q8 | Not supported | 39.55 tok/sEstimated | 31GB (have 16GB) |
| Qwen/Qwen3-Coder-30B-A3B-Instruct | FP16 | Not supported | 20.75 tok/sEstimated | 61GB (have 16GB) |
| Qwen/Qwen3-30B-A3B | FP16 | Not supported | 20.88 tok/sEstimated | 61GB (have 16GB) |
| rinna/japanese-gpt-neox-small | Q8 | Fits comfortably | 68.07 tok/sEstimated | 7GB (have 16GB) |
| unsloth/mistral-7b-v0.3-bnb-4bit | Q4 | Fits comfortably | 91.84 tok/sEstimated | 4GB (have 16GB) |
| unsloth/mistral-7b-v0.3-bnb-4bit | Q8 | Fits comfortably | 61.22 tok/sEstimated | 7GB (have 16GB) |
| Qwen/Qwen2-7B-Instruct | Q4 | Fits comfortably | 88.96 tok/sEstimated | 4GB (have 16GB) |
| Qwen/Qwen3-4B-Base | Q4 | Fits comfortably | 101.00 tok/sEstimated | 2GB (have 16GB) |
| nvidia/NVIDIA-Nemotron-Nano-9B-v2 | Q4 | Fits comfortably | 70.98 tok/sEstimated | 5GB (have 16GB) |
| nvidia/NVIDIA-Nemotron-Nano-9B-v2 | Q8 | Fits comfortably | 51.95 tok/sEstimated | 10GB (have 16GB) |
| NousResearch/Meta-Llama-3.1-8B-Instruct | Q8 | Fits comfortably | 67.89 tok/sEstimated | 9GB (have 16GB) |
| NousResearch/Meta-Llama-3.1-8B-Instruct | FP16 | Not supported | 37.40 tok/sEstimated | 17GB (have 16GB) |
| Qwen/Qwen3-30B-A3B-Thinking-2507 | FP16 | Not supported | 19.90 tok/sEstimated | 61GB (have 16GB) |
| lmstudio-community/Qwen3-Coder-30B-A3B-Instruct-MLX-5bit | Q4 | Fits (tight) | 55.59 tok/sEstimated | 15GB (have 16GB) |
| lmstudio-community/Qwen3-Coder-30B-A3B-Instruct-MLX-5bit | Q8 | Not supported | 32.95 tok/sEstimated | 31GB (have 16GB) |
| lmstudio-community/Qwen3-Coder-30B-A3B-Instruct-MLX-5bit | FP16 | Not supported | 17.80 tok/sEstimated | 61GB (have 16GB) |
| deepseek-ai/DeepSeek-V3 | Q8 | Fits comfortably | 65.13 tok/sEstimated | 7GB (have 16GB) |
| lmstudio-community/Qwen3-Coder-30B-A3B-Instruct-MLX-6bit | Q4 | Fits (tight) | 47.54 tok/sEstimated | 15GB (have 16GB) |
| lmstudio-community/Qwen3-Coder-30B-A3B-Instruct-MLX-6bit | Q8 | Not supported | 34.23 tok/sEstimated | 31GB (have 16GB) |
| lmstudio-community/Qwen3-Coder-30B-A3B-Instruct-MLX-6bit | FP16 | Not supported | 20.24 tok/sEstimated | 61GB (have 16GB) |
| dicta-il/dictalm2.0-instruct | Q4 | Fits comfortably | 84.97 tok/sEstimated | 4GB (have 16GB) |
| HuggingFaceM4/tiny-random-LlamaForCausalLM | Q8 | Fits comfortably | 68.46 tok/sEstimated | 7GB (have 16GB) |
| HuggingFaceM4/tiny-random-LlamaForCausalLM | FP16 | Fits (tight) | 34.38 tok/sEstimated | 15GB (have 16GB) |
| Qwen/Qwen2.5-72B-Instruct | Q8 | Not supported | 13.65 tok/sEstimated | 71GB (have 16GB) |
| Qwen/QwQ-32B-Preview | Q8 | Not supported | 23.20 tok/sEstimated | 34GB (have 16GB) |
| Qwen/QwQ-32B-Preview | FP16 | Not supported | 12.54 tok/sEstimated | 67GB (have 16GB) |
| mistralai/Mixtral-8x22B-Instruct-v0.1 | FP16 | Not supported | 8.99 tok/sEstimated | 275GB (have 16GB) |
| microsoft/Phi-3.5-mini-instruct | Q4 | Fits comfortably | 102.04 tok/sEstimated | 2GB (have 16GB) |
| microsoft/Phi-3.5-mini-instruct | Q8 | Fits comfortably | 60.31 tok/sEstimated | 4GB (have 16GB) |
| microsoft/Phi-3.5-mini-instruct | FP16 | Fits comfortably | 37.73 tok/sEstimated | 8GB (have 16GB) |
| microsoft/Phi-3-medium-128k-instruct | Q4 | Fits comfortably | 76.96 tok/sEstimated | 7GB (have 16GB) |
| NousResearch/Hermes-3-Llama-3.1-8B | Q4 | Fits comfortably | 64.40 tok/sEstimated | 4GB (have 16GB) |
| NousResearch/Hermes-3-Llama-3.1-8B | FP16 | Not supported | 28.79 tok/sEstimated | 17GB (have 16GB) |
| 01-ai/Yi-1.5-34B-Chat | Q4 | Not supported | 35.02 tok/sEstimated | 18GB (have 16GB) |
| 01-ai/Yi-1.5-34B-Chat | Q8 | Not supported | 21.05 tok/sEstimated | 35GB (have 16GB) |
| moonshotai/Kimi-Linear-48B-A3B-Instruct | FP16 | Not supported | 11.87 tok/sEstimated | 101GB (have 16GB) |
| moonshotai/Kimi-K2-Thinking | FP16 | Not supported | 13.11 tok/sEstimated | 1956GB (have 16GB) |
| deepseek-ai/DeepSeek-Math-V2 | Q4 | Not supported | 14.86 tok/sEstimated | 383GB (have 16GB) |
| deepseek-ai/DeepSeek-Math-V2 | Q8 | Not supported | 10.61 tok/sEstimated | 766GB (have 16GB) |
| deepseek-ai/DeepSeek-Math-V2 | FP16 | Not supported | 5.56 tok/sEstimated | 1532GB (have 16GB) |
| tencent/HunyuanOCR | Q8 | Fits comfortably | 79.68 tok/sEstimated | 2GB (have 16GB) |
| google-t5/t5-3b | Q8 | Fits comfortably | 77.97 tok/sEstimated | 3GB (have 16GB) |
Note: Performance estimates are calculated. Real results may vary. Methodology · Submit real data
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