Quick Answer: RTX 4080 Super offers 16GB VRAM and starts around $1149.99. It delivers approximately 166 tokens/sec on meta-llama/Llama-3.2-3B. It typically draws 320W 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 |
|---|---|---|---|
| meta-llama/Llama-3.2-3B | Q4 | 166.15 tok/sEstimated Auto-generated benchmark | 2GB |
| ibm-granite/granite-3.3-2b-instruct | Q4 | 165.93 tok/sEstimated Auto-generated benchmark | 1GB |
| unsloth/gemma-3-1b-it | Q4 | 163.98 tok/sEstimated Auto-generated benchmark | 1GB |
| meta-llama/Llama-3.2-3B-Instruct | Q4 | 163.73 tok/sEstimated Auto-generated benchmark | 2GB |
| meta-llama/Llama-Guard-3-1B | Q4 | 162.79 tok/sEstimated Auto-generated benchmark | 1GB |
| google/embeddinggemma-300m | Q4 | 162.35 tok/sEstimated Auto-generated benchmark | 1GB |
| google/gemma-3-1b-it | Q4 | 162.05 tok/sEstimated Auto-generated benchmark | 1GB |
| nari-labs/Dia2-2B | Q4 | 160.98 tok/sEstimated Auto-generated benchmark | 2GB |
| allenai/OLMo-2-0425-1B | Q4 | 160.89 tok/sEstimated Auto-generated benchmark | 1GB |
| meta-llama/Llama-3.2-1B | Q4 | 160.29 tok/sEstimated Auto-generated benchmark | 1GB |
| TinyLlama/TinyLlama-1.1B-Chat-v1.0 | Q4 | 156.97 tok/sEstimated Auto-generated benchmark | 1GB |
| google/gemma-2b | Q4 | 153.74 tok/sEstimated Auto-generated benchmark | 1GB |
| context-labs/meta-llama-Llama-3.2-3B-Instruct-FP16 | Q4 | 153.62 tok/sEstimated Auto-generated benchmark | 2GB |
| LiquidAI/LFM2-1.2B | Q4 | 152.97 tok/sEstimated Auto-generated benchmark | 1GB |
| Qwen/Qwen2.5-3B-Instruct | Q4 | 152.21 tok/sEstimated Auto-generated benchmark | 2GB |
| unsloth/Llama-3.2-3B-Instruct | Q4 | 151.76 tok/sEstimated Auto-generated benchmark | 2GB |
| deepseek-ai/DeepSeek-OCR | Q4 | 151.61 tok/sEstimated Auto-generated benchmark | 2GB |
| WeiboAI/VibeThinker-1.5B | Q4 | 148.90 tok/sEstimated Auto-generated benchmark | 1GB |
| google-t5/t5-3b | Q4 | 148.73 tok/sEstimated Auto-generated benchmark | 2GB |
| google-bert/bert-base-uncased | Q4 | 147.42 tok/sEstimated Auto-generated benchmark | 1GB |
| unsloth/Llama-3.2-1B-Instruct | Q4 | 147.37 tok/sEstimated Auto-generated benchmark | 1GB |
| apple/OpenELM-1_1B-Instruct | Q4 | 146.96 tok/sEstimated Auto-generated benchmark | 1GB |
| inference-net/Schematron-3B | Q4 | 146.25 tok/sEstimated Auto-generated benchmark | 2GB |
| google/gemma-2-2b-it | Q4 | 145.28 tok/sEstimated Auto-generated benchmark | 1GB |
| bigcode/starcoder2-3b | Q4 | 144.72 tok/sEstimated Auto-generated benchmark | 2GB |
| Qwen/Qwen2.5-3B | Q4 | 144.63 tok/sEstimated Auto-generated benchmark | 2GB |
| tencent/HunyuanOCR | Q4 | 144.18 tok/sEstimated Auto-generated benchmark | 1GB |
| meta-llama/Llama-3.2-1B-Instruct | Q4 | 143.95 tok/sEstimated Auto-generated benchmark | 1GB |
| deepseek-ai/deepseek-coder-1.3b-instruct | Q4 | 143.87 tok/sEstimated Auto-generated benchmark | 2GB |
| ibm-research/PowerMoE-3b | Q4 | 143.34 tok/sEstimated Auto-generated benchmark | 2GB |
| llamafactory/tiny-random-Llama-3 | Q4 | 139.32 tok/sEstimated Auto-generated benchmark | 4GB |
| tencent/HunyuanVideo-1.5 | Q4 | 139.21 tok/sEstimated Auto-generated benchmark | 4GB |
| IlyaGusev/saiga_llama3_8b | Q4 | 139.04 tok/sEstimated Auto-generated benchmark | 4GB |
| trl-internal-testing/tiny-Qwen2ForCausalLM-2.5 | Q4 | 138.55 tok/sEstimated Auto-generated benchmark | 4GB |
| huggyllama/llama-7b | Q4 | 138.51 tok/sEstimated Auto-generated benchmark | 4GB |
| facebook/sam3 | Q4 | 138.31 tok/sEstimated Auto-generated benchmark | 1GB |
| parler-tts/parler-tts-large-v1 | Q4 | 138.25 tok/sEstimated Auto-generated benchmark | 4GB |
| Qwen/Qwen2.5-0.5B-Instruct | Q4 | 138.01 tok/sEstimated Auto-generated benchmark | 3GB |
| ibm-granite/granite-docling-258M | Q4 | 137.89 tok/sEstimated Auto-generated benchmark | 4GB |
| microsoft/phi-2 | Q4 | 137.74 tok/sEstimated Auto-generated benchmark | 4GB |
| Qwen/Qwen2.5-Math-1.5B | Q4 | 137.63 tok/sEstimated Auto-generated benchmark | 3GB |
| HuggingFaceTB/SmolLM2-135M | Q4 | 137.58 tok/sEstimated Auto-generated benchmark | 4GB |
| Qwen/Qwen3-4B-Thinking-2507-FP8 | Q4 | 137.28 tok/sEstimated Auto-generated benchmark | 2GB |
| HuggingFaceH4/zephyr-7b-beta | Q4 | 137.26 tok/sEstimated Auto-generated benchmark | 4GB |
| allenai/Olmo-3-7B-Think | Q4 | 136.94 tok/sEstimated Auto-generated benchmark | 4GB |
| meta-llama/Llama-2-7b-chat-hf | Q4 | 136.83 tok/sEstimated Auto-generated benchmark | 4GB |
| microsoft/Phi-4-mini-instruct | Q4 | 136.40 tok/sEstimated Auto-generated benchmark | 4GB |
| Qwen/Qwen3-4B-Instruct-2507 | Q4 | 136.27 tok/sEstimated Auto-generated benchmark | 2GB |
| EleutherAI/pythia-70m-deduped | Q4 | 136.22 tok/sEstimated Auto-generated benchmark | 4GB |
| microsoft/Phi-4-multimodal-instruct | Q4 | 135.79 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 |
|---|---|---|---|---|
| dphn/dolphin-2.9.1-yi-1.5-34b | Q8 | Not supported | 33.10 tok/sEstimated | 35GB (have 16GB) |
| dphn/dolphin-2.9.1-yi-1.5-34b | FP16 | Not supported | 17.40 tok/sEstimated | 70GB (have 16GB) |
| openai/gpt-oss-20b | Q4 | Fits comfortably | 74.50 tok/sEstimated | 10GB (have 16GB) |
| openai/gpt-oss-20b | Q8 | Not supported | 44.66 tok/sEstimated | 20GB (have 16GB) |
| openai/gpt-oss-20b | FP16 | Not supported | 25.27 tok/sEstimated | 41GB (have 16GB) |
| google/gemma-3-1b-it | Q8 | Fits comfortably | 115.75 tok/sEstimated | 1GB (have 16GB) |
| google/gemma-3-1b-it | FP16 | Fits comfortably | 55.16 tok/sEstimated | 2GB (have 16GB) |
| Qwen/Qwen3-Embedding-0.6B | Q4 | Fits comfortably | 127.19 tok/sEstimated | 3GB (have 16GB) |
| Qwen/Qwen2.5-1.5B-Instruct | Q8 | Fits comfortably | 96.49 tok/sEstimated | 5GB (have 16GB) |
| Qwen/Qwen2.5-1.5B-Instruct | FP16 | Fits comfortably | 46.60 tok/sEstimated | 11GB (have 16GB) |
| trl-internal-testing/tiny-Qwen2ForCausalLM-2.5 | Q8 | Fits comfortably | 96.54 tok/sEstimated | 7GB (have 16GB) |
| trl-internal-testing/tiny-Qwen2ForCausalLM-2.5 | FP16 | Fits (tight) | 50.86 tok/sEstimated | 15GB (have 16GB) |
| openai/gpt-oss-120b | Q4 | Not supported | 23.77 tok/sEstimated | 59GB (have 16GB) |
| context-labs/meta-llama-Llama-3.2-3B-Instruct-FP16 | FP16 | Fits comfortably | 53.75 tok/sEstimated | 6GB (have 16GB) |
| deepseek-ai/DeepSeek-R1-Distill-Qwen-32B | Q4 | Fits (tight) | 40.01 tok/sEstimated | 16GB (have 16GB) |
| RedHatAI/Llama-3.3-70B-Instruct-FP8-dynamic | Q8 | Not supported | 29.70 tok/sEstimated | 68GB (have 16GB) |
| meta-llama/Llama-3.2-3B-Instruct | Q8 | Fits comfortably | 112.63 tok/sEstimated | 3GB (have 16GB) |
| meta-llama/Llama-3.2-3B-Instruct | FP16 | Fits comfortably | 58.17 tok/sEstimated | 6GB (have 16GB) |
| google-t5/t5-3b | Q8 | Fits comfortably | 100.35 tok/sEstimated | 3GB (have 16GB) |
| rednote-hilab/dots.ocr | Q8 | Fits comfortably | 84.44 tok/sEstimated | 7GB (have 16GB) |
| Qwen/Qwen3-Reranker-0.6B | Q8 | Fits comfortably | 92.73 tok/sEstimated | 6GB (have 16GB) |
| Qwen/Qwen3-Reranker-0.6B | FP16 | Fits comfortably | 47.00 tok/sEstimated | 13GB (have 16GB) |
| meta-llama/Meta-Llama-3-8B-Instruct | Q4 | Fits comfortably | 128.64 tok/sEstimated | 4GB (have 16GB) |
| meta-llama/Meta-Llama-3-8B-Instruct | Q8 | Fits comfortably | 87.07 tok/sEstimated | 9GB (have 16GB) |
| meta-llama/Meta-Llama-3-8B-Instruct | FP16 | Not supported | 47.75 tok/sEstimated | 17GB (have 16GB) |
| deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B | Q8 | Fits comfortably | 89.15 tok/sEstimated | 5GB (have 16GB) |
| deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B | FP16 | Fits comfortably | 48.75 tok/sEstimated | 11GB (have 16GB) |
| mlx-community/gpt-oss-20b-MXFP4-Q8 | Q4 | Fits comfortably | 63.91 tok/sEstimated | 10GB (have 16GB) |
| Qwen/Qwen2.5-14B-Instruct | FP16 | Not supported | 37.60 tok/sEstimated | 29GB (have 16GB) |
| unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit | Q4 | Fits comfortably | 124.64 tok/sEstimated | 4GB (have 16GB) |
| microsoft/phi-2 | Q8 | Fits comfortably | 87.97 tok/sEstimated | 7GB (have 16GB) |
| microsoft/phi-2 | FP16 | Fits (tight) | 49.96 tok/sEstimated | 15GB (have 16GB) |
| deepseek-ai/DeepSeek-R1-Distill-Qwen-7B | Q4 | Fits comfortably | 135.36 tok/sEstimated | 4GB (have 16GB) |
| deepseek-ai/DeepSeek-R1-Distill-Llama-8B | Q4 | Fits comfortably | 121.14 tok/sEstimated | 4GB (have 16GB) |
| HuggingFaceTB/SmolLM2-135M | FP16 | Fits (tight) | 47.53 tok/sEstimated | 15GB (have 16GB) |
| zai-org/GLM-4.6-FP8 | Q4 | Fits comfortably | 116.46 tok/sEstimated | 4GB (have 16GB) |
| zai-org/GLM-4.6-FP8 | Q8 | Fits comfortably | 90.09 tok/sEstimated | 7GB (have 16GB) |
| zai-org/GLM-4.6-FP8 | FP16 | Fits (tight) | 44.23 tok/sEstimated | 15GB (have 16GB) |
| MiniMaxAI/MiniMax-M2 | Q4 | Fits comfortably | 123.15 tok/sEstimated | 4GB (have 16GB) |
| MiniMaxAI/MiniMax-M2 | Q8 | Fits comfortably | 97.01 tok/sEstimated | 7GB (have 16GB) |
| MiniMaxAI/MiniMax-M2 | FP16 | Fits (tight) | 51.00 tok/sEstimated | 15GB (have 16GB) |
| Qwen/Qwen2-0.5B | Q8 | Fits comfortably | 86.26 tok/sEstimated | 5GB (have 16GB) |
| deepseek-ai/deepseek-coder-1.3b-instruct | Q8 | Fits comfortably | 98.73 tok/sEstimated | 3GB (have 16GB) |
| deepseek-ai/deepseek-coder-1.3b-instruct | FP16 | Fits comfortably | 58.27 tok/sEstimated | 6GB (have 16GB) |
| microsoft/phi-4 | Q4 | Fits comfortably | 133.91 tok/sEstimated | 4GB (have 16GB) |
| meta-llama/Llama-3.1-8B | FP16 | Not supported | 52.93 tok/sEstimated | 17GB (have 16GB) |
| deepseek-ai/DeepSeek-R1-0528 | Q4 | Fits comfortably | 130.75 tok/sEstimated | 4GB (have 16GB) |
| deepseek-ai/DeepSeek-R1-0528 | Q8 | Fits comfortably | 91.76 tok/sEstimated | 7GB (have 16GB) |
| deepseek-ai/DeepSeek-R1-0528 | FP16 | Fits (tight) | 52.38 tok/sEstimated | 15GB (have 16GB) |
| dphn/dolphin-2.9.1-yi-1.5-34b | Q4 | Not supported | 47.94 tok/sEstimated | 17GB (have 16GB) |
Note: Performance estimates are calculated. Real results may vary. Methodology · Submit real data
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