# RTX A6000 cloud rental: 48 GB Ampere GPUs in the US URL: https://cloudj.net/blog/rtx-a6000-cloud-usa Published: 2026-11-12 ยท Updated: 2026-11-12 > RTX A6000 cloud rental in the US: what 48 GB of VRAM is good for, 2x setups for 4-bit 70B models, searching US and CA, and how it compares with RTX 6000 Ada. RTX A6000 cloud rental is a common answer to a common problem: your model or batch does not fit in 24 GB, and you do not need a top datacenter card to fix that. The A6000 is a 48 GB Ampere workstation GPU, and 48 GB per card changes what you can run on one or two GPUs. This article covers the jobs it suits, how to search for it in the United States and Canada on Cloudj, and when the newer RTX 6000 Ada is worth considering instead. For renting in the US in general, see the [GPU cloud USA guide](https://cloudj.net/blog/gpu-cloud-usa). ## Three jobs that fit 48 GB The [RTX A6000](https://cloudj.net/gpu-cloud/rtx-a6000) has 48 GB of memory, twice that of an RTX 3090 or 4090. That headroom matters most for: **1. Large-model inference on two cards.** A 70B language model quantized to 4 bits needs roughly 40 GB for the weights, plus memory for the KV cache that grows with context length and parallel users. One 48 GB card can hold the weights with little room left; two A6000s in one machine give comfortable space for cache and longer prompts. Inference servers that support tensor parallelism can split the model across both cards. For a deeper walkthrough, see [running 70B models on multiple GPUs](https://cloudj.net/blog/llm-70b-multi-gpu-europe). **2. Fine-tuning that is cramped on 24 GB.** LoRA on 13B-34B models, larger batch sizes or longer sequence lengths often fit on a single 48 GB card where a 24 GB card would force aggressive tricks. **3. Big image-generation batches.** Stable Diffusion and SDXL pipelines can run larger batches or higher resolutions on 48 GB, which helps when you generate many images per run. These are general planning ranges, not benchmarks. Test your own model on a short deployment before a long run. ## RTX A6000 cloud rental in the US and Canada Location on Cloudj is a country filter over a live list of independent host machines. Open the deploy page with the model preselected, [deploy an RTX A6000](https://app.cloudj.net/deploy/gpu?model=rtx-a6000), then: 1. Enter `US` in the **Country** field, or `US, CA` to include Canada. 2. Choose the GPU count: 1x for most fine-tuning, 2x for 4-bit 70B inference. 3. Set the disk size. It is fixed at deploy, so include model weights, datasets and outputs. 4. Compare the results, sorted by total price, lowest first. Each listing shows the country (not the state or city), reliability, max duration, a Datacenter badge where it applies, and storage and bandwidth prices. If a machine is listed, you can deploy it. If not, try the other GPU count, add `CA`, loosen filters, or check again later; stock changes during the day. ## Account level The A6000 is a workstation-class GPU, and workstation and datacenter GPUs need a higher account level than new accounts have. If the console shows "This GPU class is not available at your account level", contact Cloudj support from the console. Meanwhile, a pair of 24 GB consumer cards can be a way to start testing. ## A6000 or RTX 6000 Ada? The [RTX 6000 Ada](https://cloudj.net/gpu-cloud/rtx-6000ada) is the next generation of the same 48 GB workstation class. | | RTX A6000 | RTX 6000 Ada | |---|---|---| | Architecture | Ampere | Ada Lovelace | | Memory | 48 GB | 48 GB | | FP8 support | No | Yes, on Ada tensor cores | | Multi-GPU bridge (general background) | Supports two-way NVLink bridges | No NVLink; GPUs communicate over PCIe | | Typical reason to pick it | Value for "I need 48 GB" | Faster per GPU for the same memory | Whether a particular machine has an NVLink bridge fitted is not something the listing promises, so do not design around it. Run a short test on each card with your workload and compare cost per finished job: hourly price times hours to finish. Live "from" prices are on each model page. If you are serving European users with the newer card, [RTX 6000 Ada in Europe](https://cloudj.net/blog/rtx-6000-ada-europe) covers that case. ## Setting up a 2x A6000 machine After deploy, connect with SSH using the host and port from the instance's Connection tab, then confirm both cards are visible: ```bash ssh -p root@ nvidia-smi --query-gpu=index,name,memory.total --format=csv ``` Install your inference server over SSH or bring it in a custom public Docker image (custom images also need a higher account level). Start with a small context and a few concurrent requests, watch memory with `nvidia-smi`, then raise limits gradually. The [single vs multi-GPU guide](https://cloudj.net/blog/single-gpu-vs-multi-gpu-cloud) explains when splitting across cards helps and when a bigger single card is simpler. ## Costs and safety Billing is per minute from a prepaid balance, with the deploy price locked for the instance. Running cost is GPU plus disk; stopped cost is storage only, and stopping releases the GPU, so it may be rented by someone else before you start again. Destroy instances you no longer need. Every listing is an independent host with no hardware SLA. Each machine has an "available until" date when the instance and disk are removed, and if a host goes offline, billing pauses but the instance does not move. Keep model weights and checkpoints somewhere you control. ## FAQ ### Can I run a 70B model on one A6000? A 4-bit 70B model's weights take roughly 40 GB, leaving little room for cache. Two A6000s are the comfortable choice. ### Why is the A6000 greyed out for me? Workstation GPUs need a higher account level. Contact Cloudj support from the console. ### Is there always an A6000 in the US? No. Search with `US` or `US, CA`; if nothing is listed, try a different GPU count or check back later. ### A6000 or RTX 6000 Ada for fine-tuning? Both have 48 GB. The Ada card is newer and usually quicker; the A6000 can be the better value. Test both and compare cost per job. ## Search for an RTX A6000 Open the [RTX A6000 deploy page](https://app.cloudj.net/deploy/gpu?model=rtx-a6000), enter `US, CA`, and pick 1x or 2x. Compare live prices with other 48 GB cards on [GPU cloud](https://cloudj.net/gpu-cloud).