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Top-tier Ada Lovelace workstation GPU for AI and visualization.
Weekly median price per GPU per hour · Get the data
By provider shows one card per company, placed where its best offer ranked. By configuration lists every offer.
What you can rent comes first: in stock, then waitlist, then not reported, then out of stock. Priced offers rank ahead of quote-only, and on-demand ahead of other billing types.
Within a group, five factors set the order:
We match the provider, its country, GPU model, form factor, billing type, availability and instance name. Matching is partial and case-insensitive. Everyday words work too, so "interruptible" finds spot and "sold out" finds out of stock.
No providers match .
Heads up: A provider's own page may quote a different figure, for example on tax, region or a promotion. A monthly-only plan shows a derived hourly rate. Verify before provisioning. More on how we price.
One RTX 6000 Ada has 48 GB of VRAM. In practice, that's enough memory for roughly 68B parameters at 4-bit or 17B at 16-bit, assuming a 32K context. Below are some open-weight LLMs, with the estimated memory and GPUs each one needs.
| Model | Memory (INT4 / FP4) | RTX 6000 Adas needed | Cost /hr | Cost /mo |
|---|---|---|---|---|
|
|
18 GB
|
1
|
$0.86
|
$619
|
|
|
22 GB
|
1
|
$0.86
|
$619
|
|
|
67 GB
|
2
|
$1.72
|
$1,238
|
|
|
178 GB
|
8
|
$6.88
|
$4,954
|
|
|
239 GB
|
8
|
$6.88
|
$4,954
|
|
|
306 GB
|
8
|
$6.88
|
$4,954
|
|
|
1,544 GB
|
36
|
–
|
–
|
Estimates based on the median on-demand rate. Memory is weights plus FP8 KV cache at 32K context per request (FP16/BF16 in the 16-bit column). GPU counts assume 90% of advertised VRAM is usable. No guarantee of runtime support, usable performance, or that a matching quantized build exists. Pricing methodology.
Nvidia RTX 6000 Ada · Per GPU
| Compute · dense | |
|---|---|
| FP8 | 728.5 TFLOPS 1,457 with sparsity |
| FP16 / BF16 | 364.2 TFLOPS 728.5 with sparsity |
| FP32 | 91.1 TFLOPS |
| Precision support | FP8FP16BF16TF32FP32INT4INT8 |
| Memory | |
|---|---|
| Capacity | 48 GB GDDR6 |
| Bandwidth | 960 GB/s |
| Bus width | 384-bit |
| ECC | Yes |
| Silicon | |
|---|---|
| Architecture | Ada Lovelace |
| Process | TSMC 4N |
| Transistors | 76.3 billion |
| Shader cores | 18,176 CUDA cores |
| Matrix cores | 568 Tensor cores |
| Compute units | 142 SMs |
| Fabric and host | |
|---|---|
| Host interface | PCIe 4.0 x16 |
| Power | |
|---|---|
| Board power | 300 W |
| Cooling | Active |
As of October 3, 2026, the median on-demand price is $0.86 per GPU per hour across 11 providers with a priced on-demand config.
| Billing type | Configs | Median /GPU/hr | Cheapest |
|---|---|---|---|
On-demand | 42 | $0.86 | $0.48 (in stock, Vast.ai) |
Reserved | 45 | $0.46 (1 mo, in stock, Vast.ai) |
|
Spot | 12 | $0.31 (in stock, Vast.ai) |
No median for reserved and spot: we only show this when at least 3 providers list the GPU on that billing type.
At 720 hours per month, one RTX 6000 Ada costs an estimated $619 at the median on-demand price. Cheapest verified in stock: $346 per month on-demand, $331 reserved (1 mo), $223 spot.
GetDeploying currently tracks RTX 6000 Ada configs from 14 providers. The cheapest verified in-stock on-demand configs come from Vast.ai, GPU.ai, Lium and Massed Compute. See the full price comparison above for every provider and config.
As of October 3, 2026, the median on-demand price has been flat over the past 90 days, at about $0.86 per GPU per hour, though it is about 6% below where it was a year ago.
One RTX 6000 Ada runs models up to roughly 68B parameters at 4-bit quantization or 17B at 16-bit, assuming a 32K context. Larger models run across multiple GPUs: the model table above shows the estimated memory and GPU count for popular open-weight LLMs.
48GB GDDR6 with Ada Lovelace FP8 support. Professional-grade with ECC memory and ISV certifications. Strong for AI development, rendering, and scientific visualization.
Professional workstation pricing premium. For cloud AI inference, the L40S uses the same Ada architecture at lower cost without the ISV certification overhead.
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