Quick Answer: NVIDIA A5000 offers 24GB VRAM and starts around $10.40. It delivers approximately 163 tokens/sec on meta-llama/Llama-3.2-3B. It typically draws 230W 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 | 163.06 tok/sEstimated Auto-generated benchmark | 2GB |
| Qwen/Qwen2.5-3B | Q4 | 163.00 tok/sEstimated Auto-generated benchmark | 2GB |
| google/gemma-3-1b-it | Q4 | 162.60 tok/sEstimated Auto-generated benchmark | 1GB |
| unsloth/Llama-3.2-3B-Instruct | Q4 | 162.49 tok/sEstimated Auto-generated benchmark | 2GB |
| ibm-granite/granite-3.3-2b-instruct | Q4 | 162.16 tok/sEstimated Auto-generated benchmark | 1GB |
| unsloth/Llama-3.2-1B-Instruct | Q4 | 161.35 tok/sEstimated Auto-generated benchmark | 1GB |
| nari-labs/Dia2-2B | Q4 | 160.21 tok/sEstimated Auto-generated benchmark | 2GB |
| facebook/sam3 | Q4 | 159.75 tok/sEstimated Auto-generated benchmark | 1GB |
| deepseek-ai/DeepSeek-OCR | Q4 | 159.70 tok/sEstimated Auto-generated benchmark | 2GB |
| google-bert/bert-base-uncased | Q4 | 157.83 tok/sEstimated Auto-generated benchmark | 1GB |
| apple/OpenELM-1_1B-Instruct | Q4 | 157.75 tok/sEstimated Auto-generated benchmark | 1GB |
| meta-llama/Llama-Guard-3-1B | Q4 | 156.89 tok/sEstimated Auto-generated benchmark | 1GB |
| deepseek-ai/deepseek-coder-1.3b-instruct | Q4 | 155.38 tok/sEstimated Auto-generated benchmark | 2GB |
| unsloth/gemma-3-1b-it | Q4 | 152.33 tok/sEstimated Auto-generated benchmark | 1GB |
| bigcode/starcoder2-3b | Q4 | 152.00 tok/sEstimated Auto-generated benchmark | 2GB |
| google-t5/t5-3b | Q4 | 150.63 tok/sEstimated Auto-generated benchmark | 2GB |
| meta-llama/Llama-3.2-1B | Q4 | 150.38 tok/sEstimated Auto-generated benchmark | 1GB |
| TinyLlama/TinyLlama-1.1B-Chat-v1.0 | Q4 | 148.43 tok/sEstimated Auto-generated benchmark | 1GB |
| google/gemma-2b | Q4 | 148.30 tok/sEstimated Auto-generated benchmark | 1GB |
| inference-net/Schematron-3B | Q4 | 146.32 tok/sEstimated Auto-generated benchmark | 2GB |
| LiquidAI/LFM2-1.2B | Q4 | 145.97 tok/sEstimated Auto-generated benchmark | 1GB |
| meta-llama/Llama-3.2-3B-Instruct | Q4 | 144.83 tok/sEstimated Auto-generated benchmark | 2GB |
| WeiboAI/VibeThinker-1.5B | Q4 | 144.74 tok/sEstimated Auto-generated benchmark | 1GB |
| google/embeddinggemma-300m | Q4 | 144.08 tok/sEstimated Auto-generated benchmark | 1GB |
| google/gemma-2-2b-it | Q4 | 142.28 tok/sEstimated Auto-generated benchmark | 1GB |
| allenai/OLMo-2-0425-1B | Q4 | 138.84 tok/sEstimated Auto-generated benchmark | 1GB |
| Tongyi-MAI/Z-Image-Turbo | Q4 | 137.81 tok/sEstimated Auto-generated benchmark | 4GB |
| microsoft/Phi-3.5-vision-instruct | Q4 | 137.80 tok/sEstimated Auto-generated benchmark | 4GB |
| Qwen/Qwen3-4B-Thinking-2507 | Q4 | 137.76 tok/sEstimated Auto-generated benchmark | 2GB |
| deepseek-ai/DeepSeek-R1 | Q4 | 137.74 tok/sEstimated Auto-generated benchmark | 4GB |
| microsoft/Phi-4-mini-instruct | Q4 | 137.64 tok/sEstimated Auto-generated benchmark | 4GB |
| ibm-research/PowerMoE-3b | Q4 | 137.60 tok/sEstimated Auto-generated benchmark | 2GB |
| lmstudio-community/Qwen3-4B-Thinking-2507-MLX-6bit | Q4 | 137.37 tok/sEstimated Auto-generated benchmark | 2GB |
| microsoft/phi-2 | Q4 | 137.29 tok/sEstimated Auto-generated benchmark | 4GB |
| numind/NuExtract-1.5 | Q4 | 137.05 tok/sEstimated Auto-generated benchmark | 4GB |
| microsoft/Phi-3-mini-128k-instruct | Q4 | 137.04 tok/sEstimated Auto-generated benchmark | 4GB |
| ibm-granite/granite-3.3-8b-instruct | Q4 | 136.96 tok/sEstimated Auto-generated benchmark | 4GB |
| microsoft/VibeVoice-1.5B | Q4 | 136.95 tok/sEstimated Auto-generated benchmark | 3GB |
| GSAI-ML/LLaDA-8B-Base | Q4 | 136.86 tok/sEstimated Auto-generated benchmark | 4GB |
| deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B | Q4 | 136.59 tok/sEstimated Auto-generated benchmark | 3GB |
| hmellor/tiny-random-LlamaForCausalLM | Q4 | 136.54 tok/sEstimated Auto-generated benchmark | 4GB |
| deepseek-ai/DeepSeek-V3 | Q4 | 136.53 tok/sEstimated Auto-generated benchmark | 4GB |
| context-labs/meta-llama-Llama-3.2-3B-Instruct-FP16 | Q4 | 135.97 tok/sEstimated Auto-generated benchmark | 2GB |
| meta-llama/Llama-3.2-1B-Instruct | Q4 | 135.83 tok/sEstimated Auto-generated benchmark | 1GB |
| Qwen/Qwen2.5-1.5B-Instruct | Q4 | 135.66 tok/sEstimated Auto-generated benchmark | 3GB |
| tencent/HunyuanOCR | Q4 | 135.64 tok/sEstimated Auto-generated benchmark | 1GB |
| Qwen/Qwen2.5-3B-Instruct | Q4 | 135.62 tok/sEstimated Auto-generated benchmark | 2GB |
| microsoft/DialoGPT-medium | Q4 | 135.53 tok/sEstimated Auto-generated benchmark | 4GB |
| Gensyn/Qwen2.5-0.5B-Instruct | Q4 | 135.33 tok/sEstimated Auto-generated benchmark | 3GB |
| Qwen/Qwen2.5-Coder-1.5B | Q4 | 134.79 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 |
|---|---|---|---|---|
| google/gemma-3-1b-it | Q8 | Fits comfortably | 106.36 tok/sEstimated | 1GB (have 24GB) |
| Qwen/Qwen3-Embedding-0.6B | Q4 | Fits comfortably | 123.74 tok/sEstimated | 3GB (have 24GB) |
| Qwen/Qwen3-Embedding-0.6B | Q8 | Fits comfortably | 90.50 tok/sEstimated | 6GB (have 24GB) |
| Qwen/Qwen2.5-7B | Q4 | Fits comfortably | 132.84 tok/sEstimated | 4GB (have 24GB) |
| Qwen/Qwen2.5-7B | Q8 | Fits comfortably | 91.93 tok/sEstimated | 7GB (have 24GB) |
| Qwen/Qwen2.5-7B | FP16 | Fits comfortably | 45.18 tok/sEstimated | 15GB (have 24GB) |
| Qwen/Qwen2.5-0.5B-Instruct | Q4 | Fits comfortably | 119.07 tok/sEstimated | 3GB (have 24GB) |
| openai-community/gpt2-large | Q4 | Fits comfortably | 123.15 tok/sEstimated | 4GB (have 24GB) |
| openai-community/gpt2-large | Q8 | Fits comfortably | 93.76 tok/sEstimated | 7GB (have 24GB) |
| openai-community/gpt2-large | FP16 | Fits comfortably | 49.56 tok/sEstimated | 15GB (have 24GB) |
| Qwen/Qwen3-30B-A3B-Instruct-2507 | Q4 | Fits comfortably | 62.74 tok/sEstimated | 15GB (have 24GB) |
| Qwen/Qwen3-30B-A3B-Instruct-2507 | Q8 | Not supported | 52.41 tok/sEstimated | 31GB (have 24GB) |
| Qwen/Qwen3-30B-A3B-Instruct-2507 | FP16 | Not supported | 27.52 tok/sEstimated | 61GB (have 24GB) |
| google-t5/t5-3b | Q4 | Fits comfortably | 150.63 tok/sEstimated | 2GB (have 24GB) |
| google-t5/t5-3b | Q8 | Fits comfortably | 104.85 tok/sEstimated | 3GB (have 24GB) |
| google-t5/t5-3b | FP16 | Fits comfortably | 62.65 tok/sEstimated | 6GB (have 24GB) |
| rednote-hilab/dots.ocr | Q4 | Fits comfortably | 121.07 tok/sEstimated | 4GB (have 24GB) |
| deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B | Q8 | Fits comfortably | 82.92 tok/sEstimated | 5GB (have 24GB) |
| deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B | FP16 | Fits comfortably | 48.29 tok/sEstimated | 11GB (have 24GB) |
| mlx-community/gpt-oss-20b-MXFP4-Q8 | Q4 | Fits comfortably | 65.03 tok/sEstimated | 10GB (have 24GB) |
| mlx-community/gpt-oss-20b-MXFP4-Q8 | Q8 | Fits comfortably | 51.67 tok/sEstimated | 20GB (have 24GB) |
| Qwen/Qwen2.5-14B-Instruct | Q8 | Fits comfortably | 61.91 tok/sEstimated | 14GB (have 24GB) |
| Qwen/Qwen3-Embedding-8B | Q8 | Fits comfortably | 88.20 tok/sEstimated | 9GB (have 24GB) |
| Qwen/Qwen2.5-0.5B | Q4 | Fits comfortably | 121.39 tok/sEstimated | 3GB (have 24GB) |
| Qwen/Qwen2.5-0.5B | Q8 | Fits comfortably | 90.20 tok/sEstimated | 5GB (have 24GB) |
| microsoft/phi-2 | Q8 | Fits comfortably | 91.34 tok/sEstimated | 7GB (have 24GB) |
| microsoft/phi-2 | FP16 | Fits comfortably | 43.39 tok/sEstimated | 15GB (have 24GB) |
| deepseek-ai/DeepSeek-R1-Distill-Qwen-7B | Q4 | Fits comfortably | 120.88 tok/sEstimated | 4GB (have 24GB) |
| deepseek-ai/DeepSeek-R1-Distill-Qwen-7B | Q8 | Fits comfortably | 87.65 tok/sEstimated | 7GB (have 24GB) |
| deepseek-ai/DeepSeek-R1-Distill-Qwen-7B | FP16 | Fits comfortably | 51.39 tok/sEstimated | 15GB (have 24GB) |
| meta-llama/Llama-2-7b-hf | Q4 | Fits comfortably | 117.31 tok/sEstimated | 4GB (have 24GB) |
| meta-llama/Llama-2-7b-hf | Q8 | Fits comfortably | 93.74 tok/sEstimated | 7GB (have 24GB) |
| meta-llama/Llama-2-7b-hf | FP16 | Fits comfortably | 48.84 tok/sEstimated | 15GB (have 24GB) |
| deepseek-ai/DeepSeek-R1-Distill-Llama-8B | Q4 | Fits comfortably | 115.44 tok/sEstimated | 4GB (have 24GB) |
| deepseek-ai/DeepSeek-R1-Distill-Llama-8B | Q8 | Fits comfortably | 87.66 tok/sEstimated | 9GB (have 24GB) |
| HuggingFaceTB/SmolLM2-135M | Q4 | Fits comfortably | 121.22 tok/sEstimated | 4GB (have 24GB) |
| HuggingFaceTB/SmolLM2-135M | Q8 | Fits comfortably | 81.94 tok/sEstimated | 7GB (have 24GB) |
| HuggingFaceTB/SmolLM2-135M | FP16 | Fits comfortably | 44.65 tok/sEstimated | 15GB (have 24GB) |
| zai-org/GLM-4.6-FP8 | Q4 | Fits comfortably | 133.76 tok/sEstimated | 4GB (have 24GB) |
| zai-org/GLM-4.6-FP8 | Q8 | Fits comfortably | 81.31 tok/sEstimated | 7GB (have 24GB) |
| zai-org/GLM-4.6-FP8 | FP16 | Fits comfortably | 47.49 tok/sEstimated | 15GB (have 24GB) |
| microsoft/DialoGPT-medium | Q4 | Fits comfortably | 135.53 tok/sEstimated | 4GB (have 24GB) |
| microsoft/DialoGPT-medium | Q8 | Fits comfortably | 82.42 tok/sEstimated | 7GB (have 24GB) |
| microsoft/DialoGPT-medium | FP16 | Fits comfortably | 47.99 tok/sEstimated | 15GB (have 24GB) |
| MiniMaxAI/MiniMax-M2 | FP16 | Fits comfortably | 47.75 tok/sEstimated | 15GB (have 24GB) |
| Qwen/Qwen2-0.5B | Q4 | Fits comfortably | 121.24 tok/sEstimated | 3GB (have 24GB) |
| deepseek-ai/DeepSeek-V3.1 | Q4 | Fits comfortably | 120.02 tok/sEstimated | 4GB (have 24GB) |
| meta-llama/Llama-3.1-8B | Q8 | Fits comfortably | 91.78 tok/sEstimated | 9GB (have 24GB) |
| meta-llama/Llama-3.1-8B | FP16 | Fits comfortably | 47.98 tok/sEstimated | 17GB (have 24GB) |
| google/gemma-3-1b-it | Q4 | Fits comfortably | 162.60 tok/sEstimated | 1GB (have 24GB) |
Note: Performance estimates are calculated. Real results may vary. Methodology · Submit real data
Data-backed answers pulled from community benchmarks, manufacturer specs, and live pricing.
RunPod benchmarks show the 24 GB RTX A5000 pushing ~49 tokens/sec on Mixtral 8x7B Q2_K under Ollama, and about 38 tok/s at Q3_K_S.
Source: Reddit – /r/LocalLLaMA (19428v9)
Yes—with low-bit EXL2 quantization. Community guides note that 2.4 bpw EXL2 plus 4-bit KV cache lets Miqu 70B run entirely within 24 GB on cards like the A5000.
Source: Reddit – /r/LocalLLaMA (kx452no)
Operators of quad-A5000 rigs suggest disabling NVLink peer-to-peer via NCCL env flags when vLLM underperforms—removing the bridges boosted throughput from ~14 tok/s to ~25 tok/s.
Source: Reddit – /r/LocalLLaMA (n3vnbez)
RTX A5000 is rated at 230 W, uses a single 8-pin connector, and NVIDIA recommends a 600 W PSU.
Source: TechPowerUp – RTX A5000 Specs
Our Nov 2025 snapshot showed RTX A5000 cards around $2,399 on Amazon (check current availability).
Source: Supabase price tracker snapshot – 2025-11-03
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