Quick Answer: NVIDIA A4000 offers 16GB VRAM and starts around $21.99. It delivers approximately 101 tokens/sec on Qwen/Qwen2.5-3B. It typically draws 140W 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 |
|---|---|---|---|
| Qwen/Qwen2.5-3B | Q4 | 101.43 tok/sEstimated Auto-generated benchmark | 2GB |
| allenai/OLMo-2-0425-1B | Q4 | 100.22 tok/sEstimated Auto-generated benchmark | 1GB |
| LiquidAI/LFM2-1.2B | Q4 | 100.11 tok/sEstimated Auto-generated benchmark | 1GB |
| deepseek-ai/DeepSeek-OCR | Q4 | 99.96 tok/sEstimated Auto-generated benchmark | 2GB |
| google-t5/t5-3b | Q4 | 99.32 tok/sEstimated Auto-generated benchmark | 2GB |
| bigcode/starcoder2-3b | Q4 | 99.02 tok/sEstimated Auto-generated benchmark | 2GB |
| google/gemma-2-2b-it | Q4 | 98.78 tok/sEstimated Auto-generated benchmark | 1GB |
| google/gemma-2b | Q4 | 97.97 tok/sEstimated Auto-generated benchmark | 1GB |
| google/gemma-3-1b-it | Q4 | 96.76 tok/sEstimated Auto-generated benchmark | 1GB |
| meta-llama/Llama-Guard-3-1B | Q4 | 96.67 tok/sEstimated Auto-generated benchmark | 1GB |
| meta-llama/Llama-3.2-1B-Instruct | Q4 | 96.14 tok/sEstimated Auto-generated benchmark | 1GB |
| google/embeddinggemma-300m | Q4 | 95.54 tok/sEstimated Auto-generated benchmark | 1GB |
| unsloth/Llama-3.2-1B-Instruct | Q4 | 93.81 tok/sEstimated Auto-generated benchmark | 1GB |
| inference-net/Schematron-3B | Q4 | 93.21 tok/sEstimated Auto-generated benchmark | 2GB |
| ibm-research/PowerMoE-3b | Q4 | 93.03 tok/sEstimated Auto-generated benchmark | 2GB |
| tencent/HunyuanOCR | Q4 | 92.59 tok/sEstimated Auto-generated benchmark | 1GB |
| context-labs/meta-llama-Llama-3.2-3B-Instruct-FP16 | Q4 | 91.82 tok/sEstimated Auto-generated benchmark | 2GB |
| meta-llama/Llama-3.2-1B | Q4 | 91.12 tok/sEstimated Auto-generated benchmark | 1GB |
| apple/OpenELM-1_1B-Instruct | Q4 | 91.12 tok/sEstimated Auto-generated benchmark | 1GB |
| meta-llama/Llama-3.2-3B | Q4 | 89.93 tok/sEstimated Auto-generated benchmark | 2GB |
| deepseek-ai/deepseek-coder-1.3b-instruct | Q4 | 89.71 tok/sEstimated Auto-generated benchmark | 2GB |
| WeiboAI/VibeThinker-1.5B | Q4 | 89.41 tok/sEstimated Auto-generated benchmark | 1GB |
| facebook/sam3 | Q4 | 89.24 tok/sEstimated Auto-generated benchmark | 1GB |
| google-bert/bert-base-uncased | Q4 | 88.91 tok/sEstimated Auto-generated benchmark | 1GB |
| unsloth/Llama-3.2-3B-Instruct | Q4 | 87.99 tok/sEstimated Auto-generated benchmark | 2GB |
| unsloth/gemma-3-1b-it | Q4 | 86.96 tok/sEstimated Auto-generated benchmark | 1GB |
| nari-labs/Dia2-2B | Q4 | 86.92 tok/sEstimated Auto-generated benchmark | 2GB |
| Qwen/Qwen2.5-3B-Instruct | Q4 | 85.94 tok/sEstimated Auto-generated benchmark | 2GB |
| Qwen/Qwen2.5-Coder-1.5B | Q4 | 84.34 tok/sEstimated Auto-generated benchmark | 3GB |
| trl-internal-testing/tiny-LlamaForCausalLM-3.2 | Q4 | 84.34 tok/sEstimated Auto-generated benchmark | 4GB |
| allenai/Olmo-3-7B-Think | Q4 | 84.19 tok/sEstimated Auto-generated benchmark | 4GB |
| microsoft/Phi-3-mini-4k-instruct | Q4 | 84.16 tok/sEstimated Auto-generated benchmark | 4GB |
| ibm-granite/granite-3.3-2b-instruct | Q4 | 84.08 tok/sEstimated Auto-generated benchmark | 1GB |
| meta-llama/Llama-3.2-3B-Instruct | Q4 | 83.93 tok/sEstimated Auto-generated benchmark | 2GB |
| HuggingFaceH4/zephyr-7b-beta | Q4 | 83.76 tok/sEstimated Auto-generated benchmark | 4GB |
| Qwen/Qwen3-4B-Base | Q4 | 83.72 tok/sEstimated Auto-generated benchmark | 2GB |
| Qwen/Qwen3-8B | Q4 | 83.66 tok/sEstimated Auto-generated benchmark | 4GB |
| TinyLlama/TinyLlama-1.1B-Chat-v1.0 | Q4 | 83.60 tok/sEstimated Auto-generated benchmark | 1GB |
| mistralai/Mistral-7B-v0.1 | Q4 | 83.60 tok/sEstimated Auto-generated benchmark | 4GB |
| Qwen/Qwen2.5-0.5B-Instruct | Q4 | 82.85 tok/sEstimated Auto-generated benchmark | 3GB |
| dicta-il/dictalm2.0-instruct | Q4 | 82.80 tok/sEstimated Auto-generated benchmark | 4GB |
| EleutherAI/gpt-neo-125m | Q4 | 82.71 tok/sEstimated Auto-generated benchmark | 4GB |
| bigscience/bloomz-560m | Q4 | 82.70 tok/sEstimated Auto-generated benchmark | 4GB |
| lmstudio-community/Qwen3-4B-Thinking-2507-MLX-8bit | Q4 | 82.56 tok/sEstimated Auto-generated benchmark | 2GB |
| meta-llama/Meta-Llama-3-8B | Q4 | 82.11 tok/sEstimated Auto-generated benchmark | 4GB |
| meta-llama/Llama-2-7b-chat-hf | Q4 | 81.90 tok/sEstimated Auto-generated benchmark | 4GB |
| Qwen/Qwen2-0.5B | Q4 | 81.86 tok/sEstimated Auto-generated benchmark | 3GB |
| swiss-ai/Apertus-8B-Instruct-2509 | Q4 | 81.64 tok/sEstimated Auto-generated benchmark | 4GB |
| trl-internal-testing/tiny-random-LlamaForCausalLM | Q4 | 81.43 tok/sEstimated Auto-generated benchmark | 4GB |
| Alibaba-NLP/gte-Qwen2-1.5B-instruct | Q4 | 81.38 tok/sEstimated Auto-generated benchmark | 3GB |
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 | 19.26 tok/sEstimated | 35GB (have 16GB) |
| dphn/dolphin-2.9.1-yi-1.5-34b | FP16 | Not supported | 9.99 tok/sEstimated | 70GB (have 16GB) |
| openai/gpt-oss-20b | Q4 | Fits comfortably | 38.59 tok/sEstimated | 10GB (have 16GB) |
| openai/gpt-oss-20b | Q8 | Not supported | 27.86 tok/sEstimated | 20GB (have 16GB) |
| openai/gpt-oss-20b | FP16 | Not supported | 16.95 tok/sEstimated | 41GB (have 16GB) |
| google/gemma-3-1b-it | Q4 | Fits comfortably | 96.76 tok/sEstimated | 1GB (have 16GB) |
| google/gemma-3-1b-it | FP16 | Fits comfortably | 36.87 tok/sEstimated | 2GB (have 16GB) |
| Qwen/Qwen3-Embedding-0.6B | Q4 | Fits comfortably | 74.17 tok/sEstimated | 3GB (have 16GB) |
| Qwen/Qwen3-Embedding-0.6B | Q8 | Fits comfortably | 57.22 tok/sEstimated | 6GB (have 16GB) |
| Qwen/Qwen3-Embedding-0.6B | FP16 | Fits comfortably | 28.60 tok/sEstimated | 13GB (have 16GB) |
| meta-llama/Llama-3.2-1B-Instruct | Q8 | Fits comfortably | 70.70 tok/sEstimated | 1GB (have 16GB) |
| meta-llama/Llama-3.2-1B | Q8 | Fits comfortably | 70.78 tok/sEstimated | 1GB (have 16GB) |
| Qwen/Qwen3-4B | FP16 | Fits comfortably | 27.90 tok/sEstimated | 9GB (have 16GB) |
| Qwen/Qwen3-30B-A3B-Instruct-2507 | Q4 | Fits (tight) | 46.18 tok/sEstimated | 15GB (have 16GB) |
| meta-llama/Meta-Llama-3-8B-Instruct | Q4 | Fits comfortably | 79.06 tok/sEstimated | 4GB (have 16GB) |
| deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B | Q4 | Fits comfortably | 76.74 tok/sEstimated | 3GB (have 16GB) |
| deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B | Q8 | Fits comfortably | 51.65 tok/sEstimated | 5GB (have 16GB) |
| Qwen/Qwen2.5-1.5B | FP16 | Fits comfortably | 27.62 tok/sEstimated | 11GB (have 16GB) |
| Qwen/Qwen2.5-14B-Instruct | Q4 | Fits comfortably | 62.68 tok/sEstimated | 7GB (have 16GB) |
| Qwen/Qwen3-Embedding-8B | Q8 | Fits comfortably | 54.58 tok/sEstimated | 9GB (have 16GB) |
| Qwen/Qwen3-Embedding-8B | FP16 | Not supported | 31.39 tok/sEstimated | 17GB (have 16GB) |
| Qwen/Qwen2-0.5B | FP16 | Fits comfortably | 31.18 tok/sEstimated | 11GB (have 16GB) |
| deepseek-ai/deepseek-coder-1.3b-instruct | Q4 | Fits comfortably | 89.71 tok/sEstimated | 2GB (have 16GB) |
| deepseek-ai/deepseek-coder-1.3b-instruct | Q8 | Fits comfortably | 66.51 tok/sEstimated | 3GB (have 16GB) |
| deepseek-ai/deepseek-coder-1.3b-instruct | FP16 | Fits comfortably | 32.68 tok/sEstimated | 6GB (have 16GB) |
| microsoft/phi-4 | Q4 | Fits comfortably | 78.34 tok/sEstimated | 4GB (have 16GB) |
| microsoft/phi-4 | Q8 | Fits comfortably | 50.77 tok/sEstimated | 7GB (have 16GB) |
| microsoft/phi-4 | FP16 | Fits (tight) | 26.76 tok/sEstimated | 15GB (have 16GB) |
| deepseek-ai/DeepSeek-V3.1 | Q4 | Fits comfortably | 79.19 tok/sEstimated | 4GB (have 16GB) |
| deepseek-ai/DeepSeek-V3.1 | Q8 | Fits comfortably | 53.78 tok/sEstimated | 7GB (have 16GB) |
| deepseek-ai/DeepSeek-V3.1 | FP16 | Fits (tight) | 30.68 tok/sEstimated | 15GB (have 16GB) |
| meta-llama/Llama-3.1-8B | Q4 | Fits comfortably | 78.38 tok/sEstimated | 4GB (have 16GB) |
| meta-llama/Llama-3.1-8B | Q8 | Fits comfortably | 58.68 tok/sEstimated | 9GB (have 16GB) |
| meta-llama/Llama-3.1-8B | FP16 | Not supported | 29.36 tok/sEstimated | 17GB (have 16GB) |
| LiquidAI/LFM2-1.2B | Q4 | Fits comfortably | 100.11 tok/sEstimated | 1GB (have 16GB) |
| LiquidAI/LFM2-1.2B | Q8 | Fits comfortably | 65.32 tok/sEstimated | 2GB (have 16GB) |
| LiquidAI/LFM2-1.2B | FP16 | Fits comfortably | 31.57 tok/sEstimated | 4GB (have 16GB) |
| meta-llama/Meta-Llama-3-70B-Instruct | Q4 | Not supported | 28.07 tok/sEstimated | 34GB (have 16GB) |
| meta-llama/Meta-Llama-3-70B-Instruct | Q8 | Not supported | 17.91 tok/sEstimated | 68GB (have 16GB) |
| meta-llama/Meta-Llama-3-70B-Instruct | FP16 | Not supported | 10.04 tok/sEstimated | 137GB (have 16GB) |
| unsloth/gpt-oss-20b-BF16 | Q4 | Fits comfortably | 46.37 tok/sEstimated | 10GB (have 16GB) |
| unsloth/gpt-oss-20b-BF16 | FP16 | Not supported | 15.11 tok/sEstimated | 41GB (have 16GB) |
| HuggingFaceTB/SmolLM-135M | Q4 | Fits comfortably | 72.01 tok/sEstimated | 4GB (have 16GB) |
| HuggingFaceTB/SmolLM-135M | Q8 | Fits comfortably | 52.47 tok/sEstimated | 7GB (have 16GB) |
| HuggingFaceTB/SmolLM-135M | FP16 | Fits (tight) | 26.92 tok/sEstimated | 15GB (have 16GB) |
| Qwen/Qwen2.5-Math-1.5B | Q4 | Fits comfortably | 76.90 tok/sEstimated | 3GB (have 16GB) |
| trl-internal-testing/tiny-random-LlamaForCausalLM | Q4 | Fits comfortably | 81.43 tok/sEstimated | 4GB (have 16GB) |
| trl-internal-testing/tiny-random-LlamaForCausalLM | Q8 | Fits comfortably | 52.13 tok/sEstimated | 7GB (have 16GB) |
| trl-internal-testing/tiny-random-LlamaForCausalLM | FP16 | Fits (tight) | 27.74 tok/sEstimated | 15GB (have 16GB) |
| openai-community/gpt2 | Q4 | Fits comfortably | 70.73 tok/sEstimated | 4GB (have 16GB) |
Note: Performance estimates are calculated. Real results may vary. Methodology · Submit real data
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