Quick Answer: AMD Radeon Pro W7900 offers 48GB VRAM and starts around current market pricing. It delivers approximately 189 tokens/sec on unsloth/Llama-3.2-1B-Instruct. It typically draws 295W 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 |
|---|---|---|---|
| unsloth/Llama-3.2-1B-Instruct | Q4 | 188.67 tok/sEstimated Auto-generated benchmark | 1GB |
| google/gemma-3-1b-it | Q4 | 185.50 tok/sEstimated Auto-generated benchmark | 1GB |
| google/gemma-2-2b-it | Q4 | 185.22 tok/sEstimated Auto-generated benchmark | 1GB |
| meta-llama/Llama-3.2-1B-Instruct | Q4 | 184.38 tok/sEstimated Auto-generated benchmark | 1GB |
| nari-labs/Dia2-2B | Q4 | 183.43 tok/sEstimated Auto-generated benchmark | 2GB |
| apple/OpenELM-1_1B-Instruct | Q4 | 183.43 tok/sEstimated Auto-generated benchmark | 1GB |
| tencent/HunyuanOCR | Q4 | 183.02 tok/sEstimated Auto-generated benchmark | 1GB |
| google/embeddinggemma-300m | Q4 | 182.98 tok/sEstimated Auto-generated benchmark | 1GB |
| deepseek-ai/deepseek-coder-1.3b-instruct | Q4 | 182.68 tok/sEstimated Auto-generated benchmark | 2GB |
| ibm-granite/granite-3.3-2b-instruct | Q4 | 182.67 tok/sEstimated Auto-generated benchmark | 1GB |
| unsloth/Llama-3.2-3B-Instruct | Q4 | 182.40 tok/sEstimated Auto-generated benchmark | 2GB |
| google/gemma-2b | Q4 | 181.63 tok/sEstimated Auto-generated benchmark | 1GB |
| meta-llama/Llama-3.2-1B | Q4 | 180.89 tok/sEstimated Auto-generated benchmark | 1GB |
| context-labs/meta-llama-Llama-3.2-3B-Instruct-FP16 | Q4 | 179.95 tok/sEstimated Auto-generated benchmark | 2GB |
| meta-llama/Llama-3.2-3B | Q4 | 179.90 tok/sEstimated Auto-generated benchmark | 2GB |
| inference-net/Schematron-3B | Q4 | 179.64 tok/sEstimated Auto-generated benchmark | 2GB |
| TinyLlama/TinyLlama-1.1B-Chat-v1.0 | Q4 | 179.04 tok/sEstimated Auto-generated benchmark | 1GB |
| LiquidAI/LFM2-1.2B | Q4 | 177.72 tok/sEstimated Auto-generated benchmark | 1GB |
| meta-llama/Llama-3.2-3B-Instruct | Q4 | 177.41 tok/sEstimated Auto-generated benchmark | 2GB |
| WeiboAI/VibeThinker-1.5B | Q4 | 175.44 tok/sEstimated Auto-generated benchmark | 1GB |
| deepseek-ai/DeepSeek-OCR | Q4 | 175.29 tok/sEstimated Auto-generated benchmark | 2GB |
| unsloth/gemma-3-1b-it | Q4 | 174.94 tok/sEstimated Auto-generated benchmark | 1GB |
| Qwen/Qwen2.5-3B-Instruct | Q4 | 172.81 tok/sEstimated Auto-generated benchmark | 2GB |
| ibm-research/PowerMoE-3b | Q4 | 167.73 tok/sEstimated Auto-generated benchmark | 2GB |
| bigcode/starcoder2-3b | Q4 | 166.29 tok/sEstimated Auto-generated benchmark | 2GB |
| meta-llama/Llama-Guard-3-1B | Q4 | 165.70 tok/sEstimated Auto-generated benchmark | 1GB |
| facebook/sam3 | Q4 | 164.88 tok/sEstimated Auto-generated benchmark | 1GB |
| google-t5/t5-3b | Q4 | 164.77 tok/sEstimated Auto-generated benchmark | 2GB |
| google-bert/bert-base-uncased | Q4 | 162.62 tok/sEstimated Auto-generated benchmark | 1GB |
| allenai/OLMo-2-0425-1B | Q4 | 161.61 tok/sEstimated Auto-generated benchmark | 1GB |
| Alibaba-NLP/gte-Qwen2-1.5B-instruct | Q4 | 159.91 tok/sEstimated Auto-generated benchmark | 3GB |
| bigscience/bloomz-560m | Q4 | 159.85 tok/sEstimated Auto-generated benchmark | 4GB |
| microsoft/Phi-3-mini-128k-instruct | Q4 | 159.56 tok/sEstimated Auto-generated benchmark | 4GB |
| Qwen/Qwen3-4B-Thinking-2507 | Q4 | 159.52 tok/sEstimated Auto-generated benchmark | 2GB |
| Qwen/Qwen3-8B | Q4 | 159.12 tok/sEstimated Auto-generated benchmark | 4GB |
| meta-llama/Llama-3.1-8B-Instruct | Q4 | 158.88 tok/sEstimated Auto-generated benchmark | 4GB |
| Qwen/Qwen3-4B-Instruct-2507 | Q4 | 158.51 tok/sEstimated Auto-generated benchmark | 2GB |
| Qwen/Qwen2.5-3B | Q4 | 157.89 tok/sEstimated Auto-generated benchmark | 2GB |
| deepseek-ai/DeepSeek-R1-0528 | Q4 | 157.69 tok/sEstimated Auto-generated benchmark | 4GB |
| GSAI-ML/LLaDA-8B-Base | Q4 | 157.58 tok/sEstimated Auto-generated benchmark | 4GB |
| vikhyatk/moondream2 | Q4 | 157.43 tok/sEstimated Auto-generated benchmark | 4GB |
| openai-community/gpt2-large | Q4 | 157.26 tok/sEstimated Auto-generated benchmark | 4GB |
| Qwen/Qwen2-7B-Instruct | Q4 | 157.08 tok/sEstimated Auto-generated benchmark | 4GB |
| zai-org/GLM-4.5-Air | Q4 | 156.19 tok/sEstimated Auto-generated benchmark | 4GB |
| numind/NuExtract-1.5 | Q4 | 156.05 tok/sEstimated Auto-generated benchmark | 4GB |
| Qwen/Qwen2.5-Coder-7B-Instruct | Q4 | 155.98 tok/sEstimated Auto-generated benchmark | 4GB |
| kaitchup/Phi-3-mini-4k-instruct-gptq-4bit | Q4 | 155.95 tok/sEstimated Auto-generated benchmark | 2GB |
| allenai/Olmo-3-7B-Think | Q4 | 155.68 tok/sEstimated Auto-generated benchmark | 4GB |
| meta-llama/Llama-2-7b-chat-hf | Q4 | 155.32 tok/sEstimated Auto-generated benchmark | 4GB |
| ibm-granite/granite-3.3-8b-instruct | Q4 | 155.23 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 |
|---|---|---|---|---|
| mlx-community/gpt-oss-20b-MXFP4-Q8 | Q8 | Fits comfortably | 60.94 tok/sEstimated | 20GB (have 48GB) |
| Qwen/Qwen2.5-Coder-32B-Instruct | FP16 | Not supported | 20.55 tok/sEstimated | 67GB (have 48GB) |
| distilbert/distilgpt2 | FP16 | Fits comfortably | 50.10 tok/sEstimated | 15GB (have 48GB) |
| Qwen/Qwen3-32B | FP16 | Not supported | 18.39 tok/sEstimated | 66GB (have 48GB) |
| Qwen/Qwen3-1.7B | Q4 | Fits comfortably | 136.51 tok/sEstimated | 4GB (have 48GB) |
| Qwen/Qwen3-1.7B | Q8 | Fits comfortably | 107.68 tok/sEstimated | 7GB (have 48GB) |
| Qwen/Qwen2.5-14B-Instruct | Q8 | Fits comfortably | 76.25 tok/sEstimated | 14GB (have 48GB) |
| Qwen/Qwen2.5-14B-Instruct | FP16 | Fits comfortably | 39.50 tok/sEstimated | 29GB (have 48GB) |
| Qwen/Qwen3-Embedding-8B | FP16 | Fits comfortably | 59.44 tok/sEstimated | 17GB (have 48GB) |
| Qwen/Qwen3-14B | FP16 | Fits comfortably | 40.14 tok/sEstimated | 29GB (have 48GB) |
| Qwen/Qwen2.5-0.5B | Q4 | Fits comfortably | 150.50 tok/sEstimated | 3GB (have 48GB) |
| Qwen/Qwen2.5-0.5B | Q8 | Fits comfortably | 109.53 tok/sEstimated | 5GB (have 48GB) |
| Qwen/Qwen2.5-0.5B | FP16 | Fits comfortably | 55.04 tok/sEstimated | 11GB (have 48GB) |
| meta-llama/Llama-3.1-70B-Instruct | Q4 | Fits comfortably | 52.25 tok/sEstimated | 34GB (have 48GB) |
| meta-llama/Llama-3.1-70B-Instruct | Q8 | Not supported | 35.51 tok/sEstimated | 68GB (have 48GB) |
| meta-llama/Llama-3.1-70B-Instruct | FP16 | Not supported | 19.18 tok/sEstimated | 137GB (have 48GB) |
| microsoft/phi-2 | Q4 | Fits comfortably | 147.62 tok/sEstimated | 4GB (have 48GB) |
| microsoft/phi-2 | Q8 | Fits comfortably | 106.25 tok/sEstimated | 7GB (have 48GB) |
| microsoft/phi-2 | FP16 | Fits comfortably | 57.96 tok/sEstimated | 15GB (have 48GB) |
| deepseek-ai/DeepSeek-R1-Distill-Qwen-7B | Q4 | Fits comfortably | 150.98 tok/sEstimated | 4GB (have 48GB) |
| deepseek-ai/DeepSeek-R1-Distill-Qwen-7B | Q8 | Fits comfortably | 92.59 tok/sEstimated | 7GB (have 48GB) |
| deepseek-ai/DeepSeek-R1-Distill-Qwen-7B | FP16 | Fits comfortably | 52.50 tok/sEstimated | 15GB (have 48GB) |
| meta-llama/Llama-2-7b-hf | Q4 | Fits comfortably | 130.97 tok/sEstimated | 4GB (have 48GB) |
| meta-llama/Llama-2-7b-hf | Q8 | Fits comfortably | 93.61 tok/sEstimated | 7GB (have 48GB) |
| meta-llama/Llama-2-7b-hf | FP16 | Fits comfortably | 53.05 tok/sEstimated | 15GB (have 48GB) |
| deepseek-ai/DeepSeek-R1-Distill-Llama-8B | Q4 | Fits comfortably | 133.85 tok/sEstimated | 4GB (have 48GB) |
| deepseek-ai/DeepSeek-R1-Distill-Llama-8B | Q8 | Fits comfortably | 111.13 tok/sEstimated | 9GB (have 48GB) |
| deepseek-ai/DeepSeek-R1-Distill-Llama-8B | FP16 | Fits comfortably | 50.77 tok/sEstimated | 17GB (have 48GB) |
| deepseek-ai/DeepSeek-V3.1 | FP16 | Fits comfortably | 60.29 tok/sEstimated | 15GB (have 48GB) |
| Qwen/Qwen2.5-Math-1.5B | Q4 | Fits comfortably | 150.18 tok/sEstimated | 3GB (have 48GB) |
| Qwen/Qwen2.5-Math-1.5B | Q8 | Fits comfortably | 93.45 tok/sEstimated | 5GB (have 48GB) |
| Qwen/Qwen2.5-Math-1.5B | FP16 | Fits comfortably | 51.42 tok/sEstimated | 11GB (have 48GB) |
| trl-internal-testing/tiny-random-LlamaForCausalLM | Q4 | Fits comfortably | 137.31 tok/sEstimated | 4GB (have 48GB) |
| trl-internal-testing/tiny-random-LlamaForCausalLM | Q8 | Fits comfortably | 96.44 tok/sEstimated | 7GB (have 48GB) |
| trl-internal-testing/tiny-random-LlamaForCausalLM | FP16 | Fits comfortably | 59.01 tok/sEstimated | 15GB (have 48GB) |
| openai-community/gpt2-medium | Q4 | Fits comfortably | 138.47 tok/sEstimated | 4GB (have 48GB) |
| openai-community/gpt2-medium | Q8 | Fits comfortably | 95.97 tok/sEstimated | 7GB (have 48GB) |
| openai-community/gpt2-medium | FP16 | Fits comfortably | 59.10 tok/sEstimated | 15GB (have 48GB) |
| Qwen/Qwen3-0.6B-Base | Q4 | Fits comfortably | 134.77 tok/sEstimated | 3GB (have 48GB) |
| Qwen/Qwen3-0.6B-Base | FP16 | Fits comfortably | 60.26 tok/sEstimated | 13GB (have 48GB) |
| Qwen/Qwen3-4B-Base | Q8 | Fits comfortably | 94.48 tok/sEstimated | 4GB (have 48GB) |
| unsloth/DeepSeek-R1-Distill-Qwen-32B-bnb-4bit | Q8 | Fits comfortably | 34.51 tok/sEstimated | 33GB (have 48GB) |
| EleutherAI/gpt-neo-125m | Q8 | Fits comfortably | 100.18 tok/sEstimated | 7GB (have 48GB) |
| EleutherAI/gpt-neo-125m | FP16 | Fits comfortably | 60.37 tok/sEstimated | 15GB (have 48GB) |
| meta-llama/Llama-3.2-3B | Q4 | Fits comfortably | 179.90 tok/sEstimated | 2GB (have 48GB) |
| meta-llama/Llama-3.2-3B | FP16 | Fits comfortably | 61.97 tok/sEstimated | 6GB (have 48GB) |
| lmstudio-community/Qwen3-4B-Thinking-2507-MLX-4bit | Q4 | Fits comfortably | 152.80 tok/sEstimated | 2GB (have 48GB) |
| lmstudio-community/Qwen3-4B-Thinking-2507-MLX-4bit | Q8 | Fits comfortably | 91.65 tok/sEstimated | 4GB (have 48GB) |
| lmstudio-community/Qwen3-4B-Thinking-2507-MLX-4bit | FP16 | Fits comfortably | 51.61 tok/sEstimated | 9GB (have 48GB) |
| google/gemma-2-27b-it | Q4 | Fits comfortably | 87.66 tok/sEstimated | 14GB (have 48GB) |
Note: Performance estimates are calculated. Real results may vary. Methodology · Submit real data
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