Quick Answer: NVIDIA A6000 offers 48GB VRAM and starts around $11.79. It delivers approximately 175 tokens/sec on apple/OpenELM-1_1B-Instruct. It typically draws 300W 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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Rotate out primary variants whenever validation flags an issue.
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| Model | Quantization | Tokens/sec | VRAM used |
|---|---|---|---|
| apple/OpenELM-1_1B-Instruct | Q4 | 174.68 tok/sEstimated Auto-generated benchmark | 1GB |
| google/gemma-2b | Q4 | 174.56 tok/sEstimated Auto-generated benchmark | 1GB |
| bigcode/starcoder2-3b | Q4 | 173.17 tok/sEstimated Auto-generated benchmark | 2GB |
| deepseek-ai/DeepSeek-OCR | Q4 | 172.91 tok/sEstimated Auto-generated benchmark | 2GB |
| context-labs/meta-llama-Llama-3.2-3B-Instruct-FP16 | Q4 | 170.69 tok/sEstimated Auto-generated benchmark | 2GB |
| google/embeddinggemma-300m | Q4 | 168.76 tok/sEstimated Auto-generated benchmark | 1GB |
| WeiboAI/VibeThinker-1.5B | Q4 | 167.39 tok/sEstimated Auto-generated benchmark | 1GB |
| ibm-research/PowerMoE-3b | Q4 | 165.67 tok/sEstimated Auto-generated benchmark | 2GB |
| meta-llama/Llama-3.2-3B-Instruct | Q4 | 163.94 tok/sEstimated Auto-generated benchmark | 2GB |
| LiquidAI/LFM2-1.2B | Q4 | 163.94 tok/sEstimated Auto-generated benchmark | 1GB |
| meta-llama/Llama-3.2-1B | Q4 | 163.88 tok/sEstimated Auto-generated benchmark | 1GB |
| facebook/sam3 | Q4 | 161.67 tok/sEstimated Auto-generated benchmark | 1GB |
| google-bert/bert-base-uncased | Q4 | 160.17 tok/sEstimated Auto-generated benchmark | 1GB |
| unsloth/Llama-3.2-3B-Instruct | Q4 | 159.80 tok/sEstimated Auto-generated benchmark | 2GB |
| allenai/OLMo-2-0425-1B | Q4 | 159.30 tok/sEstimated Auto-generated benchmark | 1GB |
| unsloth/Llama-3.2-1B-Instruct | Q4 | 158.05 tok/sEstimated Auto-generated benchmark | 1GB |
| google-t5/t5-3b | Q4 | 157.29 tok/sEstimated Auto-generated benchmark | 2GB |
| google/gemma-2-2b-it | Q4 | 155.84 tok/sEstimated Auto-generated benchmark | 1GB |
| tencent/HunyuanOCR | Q4 | 153.67 tok/sEstimated Auto-generated benchmark | 1GB |
| meta-llama/Llama-3.2-3B | Q4 | 152.17 tok/sEstimated Auto-generated benchmark | 2GB |
| meta-llama/Llama-3.2-1B-Instruct | Q4 | 151.51 tok/sEstimated Auto-generated benchmark | 1GB |
| Qwen/Qwen2.5-3B | Q4 | 150.52 tok/sEstimated Auto-generated benchmark | 2GB |
| inference-net/Schematron-3B | Q4 | 149.59 tok/sEstimated Auto-generated benchmark | 2GB |
| nari-labs/Dia2-2B | Q4 | 148.37 tok/sEstimated Auto-generated benchmark | 2GB |
| Qwen/Qwen2.5-3B-Instruct | Q4 | 148.25 tok/sEstimated Auto-generated benchmark | 2GB |
| deepseek-ai/deepseek-coder-1.3b-instruct | Q4 | 147.85 tok/sEstimated Auto-generated benchmark | 2GB |
| TinyLlama/TinyLlama-1.1B-Chat-v1.0 | Q4 | 147.22 tok/sEstimated Auto-generated benchmark | 1GB |
| ibm-granite/granite-3.3-2b-instruct | Q4 | 146.39 tok/sEstimated Auto-generated benchmark | 1GB |
| google/gemma-3-1b-it | Q4 | 145.91 tok/sEstimated Auto-generated benchmark | 1GB |
| Qwen/Qwen3-Reranker-0.6B | Q4 | 145.62 tok/sEstimated Auto-generated benchmark | 3GB |
| deepseek-ai/DeepSeek-V3.1 | Q4 | 145.44 tok/sEstimated Auto-generated benchmark | 4GB |
| IlyaGusev/saiga_llama3_8b | Q4 | 145.13 tok/sEstimated Auto-generated benchmark | 4GB |
| unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit | Q4 | 145.06 tok/sEstimated Auto-generated benchmark | 4GB |
| deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct | Q4 | 144.76 tok/sEstimated Auto-generated benchmark | 4GB |
| GSAI-ML/LLaDA-8B-Base | Q4 | 144.66 tok/sEstimated Auto-generated benchmark | 4GB |
| unsloth/Meta-Llama-3.1-8B-Instruct | Q4 | 144.27 tok/sEstimated Auto-generated benchmark | 4GB |
| meta-llama/Llama-Guard-3-1B | Q4 | 144.19 tok/sEstimated Auto-generated benchmark | 1GB |
| microsoft/Phi-4-mini-instruct | Q4 | 144.04 tok/sEstimated Auto-generated benchmark | 4GB |
| Qwen/Qwen3-8B-FP8 | Q4 | 143.92 tok/sEstimated Auto-generated benchmark | 4GB |
| Qwen/Qwen2.5-Math-1.5B | Q4 | 143.89 tok/sEstimated Auto-generated benchmark | 3GB |
| unsloth/gemma-3-1b-it | Q4 | 143.83 tok/sEstimated Auto-generated benchmark | 1GB |
| mistralai/Mistral-7B-Instruct-v0.2 | Q4 | 143.61 tok/sEstimated Auto-generated benchmark | 4GB |
| Qwen/Qwen3-4B | Q4 | 143.37 tok/sEstimated Auto-generated benchmark | 2GB |
| meta-llama/Llama-3.1-8B-Instruct | Q4 | 143.26 tok/sEstimated Auto-generated benchmark | 4GB |
| Qwen/Qwen2-0.5B | Q4 | 143.16 tok/sEstimated Auto-generated benchmark | 3GB |
| bigscience/bloomz-560m | Q4 | 143.10 tok/sEstimated Auto-generated benchmark | 4GB |
| ibm-granite/granite-docling-258M | Q4 | 143.07 tok/sEstimated Auto-generated benchmark | 4GB |
| Qwen/Qwen2-1.5B-Instruct | Q4 | 142.38 tok/sEstimated Auto-generated benchmark | 3GB |
| microsoft/Phi-3.5-vision-instruct | Q4 | 142.25 tok/sEstimated Auto-generated benchmark | 4GB |
| trl-internal-testing/tiny-LlamaForCausalLM-3.2 | Q4 | 142.19 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 | Q4 | Fits comfortably | 75.26 tok/sEstimated | 10GB (have 48GB) |
| Qwen/Qwen2.5-1.5B | Q4 | Fits comfortably | 119.57 tok/sEstimated | 3GB (have 48GB) |
| Qwen/Qwen2.5-1.5B | Q8 | Fits comfortably | 96.78 tok/sEstimated | 5GB (have 48GB) |
| Qwen/Qwen2.5-1.5B | FP16 | Fits comfortably | 47.99 tok/sEstimated | 11GB (have 48GB) |
| Qwen/Qwen2.5-14B-Instruct | Q4 | Fits comfortably | 95.23 tok/sEstimated | 7GB (have 48GB) |
| unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit | FP16 | Fits comfortably | 53.45 tok/sEstimated | 17GB (have 48GB) |
| Qwen/Qwen3-Embedding-8B | Q8 | Fits comfortably | 89.48 tok/sEstimated | 9GB (have 48GB) |
| microsoft/DialoGPT-medium | FP16 | Fits comfortably | 47.51 tok/sEstimated | 15GB (have 48GB) |
| Qwen/Qwen2.5-3B | Q8 | Fits comfortably | 105.00 tok/sEstimated | 3GB (have 48GB) |
| Qwen/Qwen2.5-3B | FP16 | Fits comfortably | 65.18 tok/sEstimated | 6GB (have 48GB) |
| NousResearch/Meta-Llama-3.1-8B-Instruct | Q4 | Fits comfortably | 139.10 tok/sEstimated | 4GB (have 48GB) |
| NousResearch/Meta-Llama-3.1-8B-Instruct | Q8 | Fits comfortably | 99.68 tok/sEstimated | 9GB (have 48GB) |
| NousResearch/Meta-Llama-3.1-8B-Instruct | FP16 | Fits comfortably | 51.52 tok/sEstimated | 17GB (have 48GB) |
| Qwen/Qwen3-Next-80B-A3B-Instruct-FP8 | FP16 | Not supported | 9.28 tok/sEstimated | 156GB (have 48GB) |
| Qwen/Qwen2.5-72B-Instruct | Q4 | Fits comfortably | 25.97 tok/sEstimated | 36GB (have 48GB) |
| Qwen/Qwen2.5-Coder-32B-Instruct | Q4 | Fits comfortably | 45.15 tok/sEstimated | 17GB (have 48GB) |
| Qwen/Qwen2.5-Coder-32B-Instruct | Q8 | Fits comfortably | 34.49 tok/sEstimated | 34GB (have 48GB) |
| Qwen/Qwen2.5-Coder-32B-Instruct | FP16 | Not supported | 17.46 tok/sEstimated | 67GB (have 48GB) |
| google-bert/bert-base-uncased | Q4 | Fits comfortably | 160.17 tok/sEstimated | 1GB (have 48GB) |
| WeiboAI/VibeThinker-1.5B | Q4 | Fits comfortably | 167.39 tok/sEstimated | 1GB (have 48GB) |
| Qwen/Qwen2.5-7B-Instruct | Q4 | Fits comfortably | 138.76 tok/sEstimated | 4GB (have 48GB) |
| Qwen/Qwen2.5-7B-Instruct | Q8 | Fits comfortably | 99.19 tok/sEstimated | 7GB (have 48GB) |
| Qwen/Qwen2.5-7B-Instruct | FP16 | Fits comfortably | 52.67 tok/sEstimated | 15GB (have 48GB) |
| Qwen/Qwen3-0.6B | Q4 | Fits comfortably | 139.87 tok/sEstimated | 3GB (have 48GB) |
| Qwen/Qwen3-0.6B | Q8 | Fits comfortably | 84.74 tok/sEstimated | 6GB (have 48GB) |
| Qwen/Qwen3-0.6B | FP16 | Fits comfortably | 49.83 tok/sEstimated | 13GB (have 48GB) |
| Gensyn/Qwen2.5-0.5B-Instruct | Q4 | Fits comfortably | 139.07 tok/sEstimated | 3GB (have 48GB) |
| Gensyn/Qwen2.5-0.5B-Instruct | Q8 | Fits comfortably | 83.94 tok/sEstimated | 5GB (have 48GB) |
| dphn/dolphin-2.9.1-yi-1.5-34b | Q4 | Fits comfortably | 43.94 tok/sEstimated | 17GB (have 48GB) |
| dphn/dolphin-2.9.1-yi-1.5-34b | Q8 | Fits comfortably | 34.32 tok/sEstimated | 35GB (have 48GB) |
| dphn/dolphin-2.9.1-yi-1.5-34b | FP16 | Not supported | 17.43 tok/sEstimated | 70GB (have 48GB) |
| google/gemma-3-1b-it | FP16 | Fits comfortably | 57.11 tok/sEstimated | 2GB (have 48GB) |
| Qwen/Qwen3-Embedding-0.6B | Q4 | Fits comfortably | 120.20 tok/sEstimated | 3GB (have 48GB) |
| Qwen/Qwen3-Embedding-0.6B | Q8 | Fits comfortably | 95.61 tok/sEstimated | 6GB (have 48GB) |
| openai-community/gpt2-large | Q4 | Fits comfortably | 139.80 tok/sEstimated | 4GB (have 48GB) |
| openai-community/gpt2-large | Q8 | Fits comfortably | 83.50 tok/sEstimated | 7GB (have 48GB) |
| Qwen/Qwen3-Embedding-0.6B | FP16 | Fits comfortably | 47.62 tok/sEstimated | 13GB (have 48GB) |
| Qwen/Qwen2.5-1.5B-Instruct | Q8 | Fits comfortably | 91.54 tok/sEstimated | 5GB (have 48GB) |
| Qwen/Qwen2.5-1.5B-Instruct | FP16 | Fits comfortably | 52.23 tok/sEstimated | 11GB (have 48GB) |
| facebook/opt-125m | Q4 | Fits comfortably | 133.03 tok/sEstimated | 4GB (have 48GB) |
| facebook/opt-125m | Q8 | Fits comfortably | 98.73 tok/sEstimated | 7GB (have 48GB) |
| facebook/opt-125m | FP16 | Fits comfortably | 49.92 tok/sEstimated | 15GB (have 48GB) |
| TinyLlama/TinyLlama-1.1B-Chat-v1.0 | Q4 | Fits comfortably | 147.22 tok/sEstimated | 1GB (have 48GB) |
| TinyLlama/TinyLlama-1.1B-Chat-v1.0 | Q8 | Fits comfortably | 114.52 tok/sEstimated | 1GB (have 48GB) |
| openai/gpt-oss-120b | Q4 | Not supported | 27.42 tok/sEstimated | 59GB (have 48GB) |
| openai/gpt-oss-120b | Q8 | Not supported | 17.97 tok/sEstimated | 117GB (have 48GB) |
| openai/gpt-oss-120b | FP16 | Not supported | 10.17 tok/sEstimated | 235GB (have 48GB) |
| Qwen/Qwen2.5-3B-Instruct | Q4 | Fits comfortably | 148.25 tok/sEstimated | 2GB (have 48GB) |
| Qwen/Qwen2.5-3B-Instruct | Q8 | Fits comfortably | 112.24 tok/sEstimated | 3GB (have 48GB) |
| meta-llama/Meta-Llama-3-8B-Instruct | FP16 | Fits comfortably | 46.65 tok/sEstimated | 17GB (have 48GB) |
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.
Operators running dual RTX A6000/RTX 8000 cards inside oobabooga report roughly 6–7 tokens/sec on 70B IQ4 MiQu workloads—adequate for shared inference queues.
Source: Reddit – /r/LocalLLaMA (lnv0ww3)
Enthusiasts caution that consumer boards seldom provide x16/x16 for two A6000s; dropping to x8/x4 starves llama.cpp workloads and erodes throughput.
Source: Reddit – /r/LocalLLaMA (mqpg0wp)
Even 2020-era RTX A6000 cards still list near $5,000, and the community expects scalpers to follow new workstation launches—showing how demand stays high.
Source: Reddit – /r/LocalLLaMA (movlqi2)
Some builders consider 48 GB 4090s, which keep full VRAM for inference but drop to 24 GB for PCIe peer-to-peer training—making the trade-off workload dependent.
Source: Reddit – /r/LocalLLaMA (mqoerg0)
RTX A6000 ships with 48 GB GDDR6 ECC and a 300 W TDP. As of Nov 2025 pricing on Amazon was around $4,899.
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Side-by-side VRAM, throughput, efficiency, and pricing benchmarks for both GPUs.
Side-by-side VRAM, throughput, efficiency, and pricing benchmarks for both GPUs.
Side-by-side VRAM, throughput, efficiency, and pricing benchmarks for both GPUs.