Not viable for modern AI workloads.
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 GTX 1060 has 6 GB of VRAM. Even at 4-bit, the weights of most modern AI models exceed the card's usable VRAM, and context adds KV cache on top. Below are some open-weight LLMs, with the estimated memory and GPUs each one needs.
| Model | Memory (INT4 / FP4) | GTX 1060s needed | Cost /hr | Cost /mo |
|---|---|---|---|---|
Qwen3.8-27B
27B
|
19 GB
|
4
|
–
|
–
|
Gemma 4 31B
30.7B
|
24 GB
|
5
|
–
|
–
|
GPT-OSS-120B
117B · 5.1B active
|
68 GB
|
13
|
–
|
–
|
GLM-5.3-Flash
320B · 18B active
|
178 GB
|
34
|
–
|
–
|
MiniMax-M3
428B · 23B active
|
241 GB
|
45
|
–
|
–
|
DeepSeek V4.1 Flash
552B · 16B active
|
306 GB
|
57
|
–
|
–
|
Kimi K3
2.8T · 104B active
|
1,545 GB
|
287
|
–
|
–
|
Estimates based on the median on-demand rate. Memory is weights plus FP16/BF16 KV cache at 32K context per request. 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 GTX 1060 · Per GPU
| Compute · dense | |
|---|---|
| FP32 | 4.4 TFLOPS |
| Precision support | FP16FP32 |
| Memory | |
|---|---|
| Capacity | 6 GB GDDR5 |
| Bandwidth | 192 GB/s |
| Bus width | 192-bit |
| ECC | No |
| Silicon | |
|---|---|
| Architecture | Pascal |
| Process | TSMC 16nm FinFET |
| Transistors | 4.4 billion |
| Shader cores | 1,280 CUDA cores |
| Compute units | 10 SMs |
| Fabric and host | |
|---|---|
| Host interface | PCIe 3.0 |
| Power | |
|---|---|
| Board power | 120 W |
| Cooling | Active |
Source: official Nvidia GTX 1060 datasheet.
The cheapest verified in-stock estimate is $22 per month on-demand, $14 spot.
GetDeploying currently tracks GTX 1060 configs from 1 provider. Salad has verified in-stock on-demand configs. See the full price comparison above for every provider and config.
More than one: even at 4-bit, the weights of most modern AI models exceed one GTX 1060's usable VRAM, and context adds KV cache on top. Larger models run across multiple GPUs: the model table above shows the estimated memory and GPU count for popular open-weight LLMs.
6GB GDDR5. Entry-level Pascal GPU at minimal cost.
No Tensor Cores and only 6GB VRAM. Only viable for legacy CUDA applications or lightweight cloud desktop use.
Nvidia RTX 3060
12 GB GDDR6 · 88% more bandwidth than the GTX 1060
Nvidia GTX 1070
8 GB GDDR5 · 33% more bandwidth than the GTX 1060
Nvidia RTX 2060
6 GB GDDR6 · 75% more bandwidth than the GTX 1060
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