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.
Quick Answer: NVIDIA A6000 has 48GB VRAM, enough for models up to roughly 120B parameters at 4-bit quantization. It draws 300W under load.
With 48GB VRAM, NVIDIA A6000 can run models up to approximately 120B parameters using 4-bit quantization. That covers most popular models, including 70B-class ones at 4-bit.
Consider H100 or MI300X — Maximum VRAM for enterprise workloads.
Buy directly on Amazon with fast shipping and reliable customer service.
Essential accessories to pair with NVIDIA A6000
Accessories Total
Typical prices — check Amazon for current pricing
💡 Not ready to buy? Try cloud GPUs first
Test NVIDIA A6000 performance in the cloud before investing in hardware. Pay by the hour with no commitment.
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.
Showing 12 of 80 rows. Speeds are calculated estimates, not measurements — search for your model to jump straight to it.
| Model | Size | Quantization | Tokens/sec | VRAM used |
|---|---|---|---|---|
| Deepseek AI Deepseek Coder 1.3B Instruct | 1.3B | Q4 | ~200 tok/sEstimated | 1GB |
| Deepseek AI Deepseek R1 Distill Qwen 1.5B | 1.5B | Q4 | ~200 tok/sEstimated | 1GB |
| Deepseek AI Deepseek Ocr 2 | Unknown | Q4 | ~165 tok/sEstimated | 2GB |
| Deepseek AI Deepseek Ocr | Unknown | Q4 | ~165 tok/sEstimated | 2GB |
| Lmstudio Community Deepseek R1 0528 Qwen3 8B Mlx 8bit | 8B | Q4 | ~165 tok/sEstimated | 4GB |
| Lmstudio Community Deepseek R1 0528 Qwen3 8B Mlx 4bit | 8B | Q4 | ~165 tok/sEstimated | 4GB |
| Deepseek AI Deepseek R1 Distill Qwen 7B | 7B | Q4 | ~165 tok/sEstimated | 4GB |
| Nineninesix Kani Tts 2 En | Unknown | Q4 | ~160 tok/sEstimated | 1GB |
| Qwen Qwen3 Tts 12hz 1 7B Customvoice | 7B | Q4 | ~160 tok/sEstimated | 1GB |
| Zai Org Glm Ocr | Unknown | Q4 | ~160 tok/sEstimated | 1GB |
| Qwen Qwen3 Asr 1 7B | 7B | Q4 | ~160 tok/sEstimated | 2GB |
| Nari Labs Dia2 2B | 2B | Q4 | ~160 tok/sEstimated | 1GB |
Showing 12 of 240 rows.
| Model | Size | Quantization | Verdict | Estimated speed | VRAM needed |
|---|---|---|---|---|---|
| 01 AI Yi 1 5 34B Chat | 34B | Q4 | Fits comfortably | ~46 tok/sEstimated | 18GB (have 48GB) |
| 01 AI Yi 1 5 34B Chat | 34B | Q8 | Fits comfortably | ~32 tok/sEstimated | 35GB (have 48GB) |
| 01 AI Yi 1 5 34B Chat | 34B | FP16 | Not supported | ~18 tok/sEstimated | 69GB (have 48GB) |
| AI Forever Rugpt 3.5 13B | 13B | Q4 | Fits comfortably | ~99 tok/sEstimated | 7GB (have 48GB) |
| AI Forever Rugpt 3.5 13B | 13B | Q8 | Fits comfortably | ~70 tok/sEstimated | 13GB (have 48GB) |
| AI Forever Rugpt 3.5 13B | 13B | FP16 | Fits comfortably | ~38 tok/sEstimated | 26GB (have 48GB) |
| AI Mo Kimina Prover 72B | 72B | Q4 | Fits comfortably | ~26 tok/sEstimated | 37GB (have 48GB) |
| AI Mo Kimina Prover 72B | 72B | Q8 | Not supported | ~19 tok/sEstimated | 73GB (have 48GB) |
| AI Mo Kimina Prover 72B | 72B | FP16 | Not supported | ~10 tok/sEstimated | 146GB (have 48GB) |
| Alibaba Nlp Gte Qwen2 1.5B Instruct | 1.5B | Q4 | Fits comfortably | ~160 tok/sEstimated | 1GB (have 48GB) |
| Alibaba Nlp Gte Qwen2 1.5B Instruct | 1.5B | Q8 | Fits comfortably | ~110 tok/sEstimated | 2GB (have 48GB) |
| Alibaba Nlp Gte Qwen2 1.5B Instruct | 1.5B | FP16 | Fits comfortably | ~60 tok/sEstimated | 4GB (have 48GB) |
Note: Performance estimates are calculated. Real results may vary. Methodology · Submit real data
Explore how RTX 4090 stacks up for local inference workloads.
Explore how RTX 4080 stacks up for local inference workloads.
Explore how RTX 4070 Ti stacks up for local inference workloads.
Explore how RTX 3090 stacks up for local inference workloads.
Explore how RX 7900 XTX stacks up for local inference workloads.