Quick Answer: NVIDIA A100 80GB SXM4 offers 80GB VRAM and starts around current market pricing. It delivers approximately 375 tokens/sec on google/embeddinggemma-300m. It typically draws 400W 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 |
|---|---|---|---|
| google/embeddinggemma-300m | Q4 | 375.43 tok/sEstimated Auto-generated benchmark | 1GB |
| nari-labs/Dia2-2B | Q4 | 374.38 tok/sEstimated Auto-generated benchmark | 2GB |
| TinyLlama/TinyLlama-1.1B-Chat-v1.0 | Q4 | 371.42 tok/sEstimated Auto-generated benchmark | 1GB |
| google/gemma-2b | Q4 | 368.53 tok/sEstimated Auto-generated benchmark | 1GB |
| Qwen/Qwen2.5-3B | Q4 | 367.02 tok/sEstimated Auto-generated benchmark | 2GB |
| apple/OpenELM-1_1B-Instruct | Q4 | 366.51 tok/sEstimated Auto-generated benchmark | 1GB |
| allenai/OLMo-2-0425-1B | Q4 | 364.80 tok/sEstimated Auto-generated benchmark | 1GB |
| WeiboAI/VibeThinker-1.5B | Q4 | 363.98 tok/sEstimated Auto-generated benchmark | 1GB |
| context-labs/meta-llama-Llama-3.2-3B-Instruct-FP16 | Q4 | 363.51 tok/sEstimated Auto-generated benchmark | 2GB |
| meta-llama/Llama-3.2-1B-Instruct | Q4 | 360.88 tok/sEstimated Auto-generated benchmark | 1GB |
| google/gemma-2-2b-it | Q4 | 357.97 tok/sEstimated Auto-generated benchmark | 1GB |
| google-bert/bert-base-uncased | Q4 | 356.15 tok/sEstimated Auto-generated benchmark | 1GB |
| meta-llama/Llama-3.2-3B | Q4 | 355.81 tok/sEstimated Auto-generated benchmark | 2GB |
| google-t5/t5-3b | Q4 | 351.09 tok/sEstimated Auto-generated benchmark | 2GB |
| meta-llama/Llama-3.2-3B-Instruct | Q4 | 343.19 tok/sEstimated Auto-generated benchmark | 2GB |
| google/gemma-3-1b-it | Q4 | 342.66 tok/sEstimated Auto-generated benchmark | 1GB |
| Qwen/Qwen2.5-3B-Instruct | Q4 | 341.75 tok/sEstimated Auto-generated benchmark | 2GB |
| unsloth/Llama-3.2-3B-Instruct | Q4 | 341.16 tok/sEstimated Auto-generated benchmark | 2GB |
| deepseek-ai/deepseek-coder-1.3b-instruct | Q4 | 339.54 tok/sEstimated Auto-generated benchmark | 2GB |
| inference-net/Schematron-3B | Q4 | 338.65 tok/sEstimated Auto-generated benchmark | 2GB |
| unsloth/Llama-3.2-1B-Instruct | Q4 | 337.25 tok/sEstimated Auto-generated benchmark | 1GB |
| bigcode/starcoder2-3b | Q4 | 337.12 tok/sEstimated Auto-generated benchmark | 2GB |
| deepseek-ai/DeepSeek-OCR | Q4 | 333.01 tok/sEstimated Auto-generated benchmark | 2GB |
| unsloth/gemma-3-1b-it | Q4 | 331.15 tok/sEstimated Auto-generated benchmark | 1GB |
| meta-llama/Llama-3.2-1B | Q4 | 326.85 tok/sEstimated Auto-generated benchmark | 1GB |
| meta-llama/Llama-Guard-3-1B | Q4 | 326.39 tok/sEstimated Auto-generated benchmark | 1GB |
| facebook/sam3 | Q4 | 324.85 tok/sEstimated Auto-generated benchmark | 1GB |
| ibm-granite/granite-3.3-2b-instruct | Q4 | 324.42 tok/sEstimated Auto-generated benchmark | 1GB |
| tencent/HunyuanOCR | Q4 | 322.54 tok/sEstimated Auto-generated benchmark | 1GB |
| Qwen/Qwen3-8B-Base | Q4 | 321.08 tok/sEstimated Auto-generated benchmark | 4GB |
| LiquidAI/LFM2-1.2B | Q4 | 319.18 tok/sEstimated Auto-generated benchmark | 1GB |
| HuggingFaceTB/SmolLM2-135M | Q4 | 318.81 tok/sEstimated Auto-generated benchmark | 4GB |
| meta-llama/Llama-Guard-3-8B | Q4 | 318.79 tok/sEstimated Auto-generated benchmark | 4GB |
| meta-llama/Llama-3.1-8B | Q4 | 318.76 tok/sEstimated Auto-generated benchmark | 4GB |
| parler-tts/parler-tts-large-v1 | Q4 | 318.33 tok/sEstimated Auto-generated benchmark | 4GB |
| Tongyi-MAI/Z-Image-Turbo | Q4 | 317.38 tok/sEstimated Auto-generated benchmark | 4GB |
| ibm-granite/granite-3.3-8b-instruct | Q4 | 316.86 tok/sEstimated Auto-generated benchmark | 4GB |
| mistralai/Mistral-7B-Instruct-v0.2 | Q4 | 316.42 tok/sEstimated Auto-generated benchmark | 4GB |
| Qwen/Qwen3-Reranker-0.6B | Q4 | 316.22 tok/sEstimated Auto-generated benchmark | 3GB |
| Gensyn/Qwen2.5-0.5B-Instruct | Q4 | 315.80 tok/sEstimated Auto-generated benchmark | 3GB |
| ibm-research/PowerMoE-3b | Q4 | 315.50 tok/sEstimated Auto-generated benchmark | 2GB |
| unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit | Q4 | 315.28 tok/sEstimated Auto-generated benchmark | 4GB |
| microsoft/phi-2 | Q4 | 314.83 tok/sEstimated Auto-generated benchmark | 4GB |
| Qwen/Qwen2.5-1.5B-Instruct | Q4 | 314.66 tok/sEstimated Auto-generated benchmark | 3GB |
| huggyllama/llama-7b | Q4 | 313.79 tok/sEstimated Auto-generated benchmark | 4GB |
| Qwen/Qwen2-7B-Instruct | Q4 | 313.59 tok/sEstimated Auto-generated benchmark | 4GB |
| Qwen/Qwen3-4B-Thinking-2507 | Q4 | 313.13 tok/sEstimated Auto-generated benchmark | 2GB |
| openai-community/gpt2-xl | Q4 | 312.64 tok/sEstimated Auto-generated benchmark | 4GB |
| microsoft/VibeVoice-1.5B | Q4 | 312.48 tok/sEstimated Auto-generated benchmark | 3GB |
| Qwen/Qwen3-4B-Thinking-2507-FP8 | Q4 | 312.22 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 |
|---|---|---|---|---|
| Qwen/Qwen3-Embedding-0.6B | Q8 | Fits comfortably | 212.40 tok/sEstimated | 6GB (have 80GB) |
| Qwen/Qwen3-Embedding-0.6B | FP16 | Fits comfortably | 113.11 tok/sEstimated | 13GB (have 80GB) |
| facebook/opt-125m | Q8 | Fits comfortably | 198.80 tok/sEstimated | 7GB (have 80GB) |
| facebook/opt-125m | FP16 | Fits comfortably | 103.65 tok/sEstimated | 15GB (have 80GB) |
| TinyLlama/TinyLlama-1.1B-Chat-v1.0 | Q4 | Fits comfortably | 371.42 tok/sEstimated | 1GB (have 80GB) |
| trl-internal-testing/tiny-Qwen2ForCausalLM-2.5 | FP16 | Fits comfortably | 106.17 tok/sEstimated | 15GB (have 80GB) |
| Qwen/Qwen3-4B-Instruct-2507 | Q4 | Fits comfortably | 280.04 tok/sEstimated | 2GB (have 80GB) |
| Qwen/Qwen3-4B-Instruct-2507 | Q8 | Fits comfortably | 193.14 tok/sEstimated | 4GB (have 80GB) |
| Qwen/Qwen3-4B-Instruct-2507 | FP16 | Fits comfortably | 119.15 tok/sEstimated | 9GB (have 80GB) |
| meta-llama/Llama-3.2-1B-Instruct | Q4 | Fits comfortably | 360.88 tok/sEstimated | 1GB (have 80GB) |
| meta-llama/Llama-3.2-1B-Instruct | Q8 | Fits comfortably | 250.39 tok/sEstimated | 1GB (have 80GB) |
| meta-llama/Llama-3.2-1B-Instruct | FP16 | Fits comfortably | 146.09 tok/sEstimated | 2GB (have 80GB) |
| openai/gpt-oss-120b | Q4 | Fits comfortably | 54.90 tok/sEstimated | 59GB (have 80GB) |
| openai/gpt-oss-120b | Q8 | Not supported | 41.05 tok/sEstimated | 117GB (have 80GB) |
| Qwen/Qwen2.5-3B-Instruct | FP16 | Fits comfortably | 123.52 tok/sEstimated | 6GB (have 80GB) |
| bigscience/bloomz-560m | Q4 | Fits comfortably | 297.58 tok/sEstimated | 4GB (have 80GB) |
| inference-net/Schematron-3B | Q4 | Fits comfortably | 338.65 tok/sEstimated | 2GB (have 80GB) |
| deepseek-ai/DeepSeek-R1-Distill-Qwen-32B | Q8 | Fits comfortably | 75.05 tok/sEstimated | 33GB (have 80GB) |
| RedHatAI/Llama-3.3-70B-Instruct-FP8-dynamic | Q4 | Fits comfortably | 100.23 tok/sEstimated | 34GB (have 80GB) |
| petals-team/StableBeluga2 | Q4 | Fits comfortably | 278.45 tok/sEstimated | 4GB (have 80GB) |
| petals-team/StableBeluga2 | Q8 | Fits comfortably | 200.22 tok/sEstimated | 7GB (have 80GB) |
| petals-team/StableBeluga2 | FP16 | Fits comfortably | 116.45 tok/sEstimated | 15GB (have 80GB) |
| meta-llama/Llama-3.2-1B | Q4 | Fits comfortably | 326.85 tok/sEstimated | 1GB (have 80GB) |
| meta-llama/Meta-Llama-3-8B | Q8 | Fits comfortably | 215.58 tok/sEstimated | 9GB (have 80GB) |
| meta-llama/Meta-Llama-3-8B | FP16 | Fits comfortably | 109.95 tok/sEstimated | 17GB (have 80GB) |
| Qwen/Qwen2.5-7B | Q4 | Fits comfortably | 269.48 tok/sEstimated | 4GB (have 80GB) |
| Qwen/Qwen2.5-7B | Q8 | Fits comfortably | 202.87 tok/sEstimated | 7GB (have 80GB) |
| Qwen/Qwen2.5-7B | FP16 | Fits comfortably | 111.76 tok/sEstimated | 15GB (have 80GB) |
| Qwen/Qwen2.5-0.5B-Instruct | Q4 | Fits comfortably | 303.12 tok/sEstimated | 3GB (have 80GB) |
| Qwen/Qwen2.5-0.5B-Instruct | FP16 | Fits comfortably | 121.48 tok/sEstimated | 11GB (have 80GB) |
| Qwen/Qwen3-32B | Q4 | Fits comfortably | 111.71 tok/sEstimated | 16GB (have 80GB) |
| Qwen/Qwen3-32B | FP16 | Fits comfortably | 40.13 tok/sEstimated | 66GB (have 80GB) |
| Qwen/Qwen3-Next-80B-A3B-Instruct | Q4 | Fits comfortably | 61.21 tok/sEstimated | 39GB (have 80GB) |
| Qwen/Qwen3-Next-80B-A3B-Instruct | Q8 | Fits comfortably | 44.21 tok/sEstimated | 78GB (have 80GB) |
| Qwen/Qwen3-Next-80B-A3B-Instruct | FP16 | Not supported | 24.10 tok/sEstimated | 156GB (have 80GB) |
| allenai/OLMo-2-0425-1B | Q4 | Fits comfortably | 364.80 tok/sEstimated | 1GB (have 80GB) |
| microsoft/Phi-3-mini-4k-instruct | FP16 | Fits comfortably | 104.32 tok/sEstimated | 15GB (have 80GB) |
| openai-community/gpt2-large | Q4 | Fits comfortably | 272.23 tok/sEstimated | 4GB (have 80GB) |
| openai-community/gpt2-large | Q8 | Fits comfortably | 199.16 tok/sEstimated | 7GB (have 80GB) |
| Qwen/Qwen3-1.7B | Q4 | Fits comfortably | 280.21 tok/sEstimated | 4GB (have 80GB) |
| Qwen/Qwen3-1.7B | Q8 | Fits comfortably | 185.55 tok/sEstimated | 7GB (have 80GB) |
| Qwen/Qwen3-1.7B | FP16 | Fits comfortably | 103.64 tok/sEstimated | 15GB (have 80GB) |
| Qwen/Qwen3-4B | Q4 | Fits comfortably | 272.44 tok/sEstimated | 2GB (have 80GB) |
| Qwen/Qwen3-4B | Q8 | Fits comfortably | 199.32 tok/sEstimated | 4GB (have 80GB) |
| Qwen/Qwen3-4B | FP16 | Fits comfortably | 103.07 tok/sEstimated | 9GB (have 80GB) |
| Qwen/Qwen3-30B-A3B-Instruct-2507 | Q4 | Fits comfortably | 166.14 tok/sEstimated | 15GB (have 80GB) |
| Qwen/Qwen3-30B-A3B-Instruct-2507 | Q8 | Fits comfortably | 110.46 tok/sEstimated | 31GB (have 80GB) |
| Qwen/Qwen3-30B-A3B-Instruct-2507 | FP16 | Fits comfortably | 66.73 tok/sEstimated | 61GB (have 80GB) |
| rednote-hilab/dots.ocr | FP16 | Fits comfortably | 107.38 tok/sEstimated | 15GB (have 80GB) |
| google/gemma-3-1b-it | Q4 | Fits comfortably | 342.66 tok/sEstimated | 1GB (have 80GB) |
Note: Performance estimates are calculated. Real results may vary. Methodology · Submit real data
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