Best budget Ampere option for small model fine-tuning.
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 3060 has 12 GB of VRAM. In practice, that's enough memory for roughly 1B parameters at 4-bit or 0.4B 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 3060s needed | Cost /hr | Cost /mo |
|---|---|---|---|---|
Qwen3.8-27B
27B
|
19 GB
|
2
|
–
|
–
|
Gemma 4 31B
30.7B
|
24 GB
|
3
|
–
|
–
|
GPT-OSS-120B
117B · 5.1B active
|
68 GB
|
7
|
–
|
–
|
GLM-5.3-Flash
320B · 18B active
|
178 GB
|
17
|
–
|
–
|
MiniMax-M3
428B · 23B active
|
241 GB
|
23
|
–
|
–
|
DeepSeek V4.1 Flash
552B · 16B active
|
306 GB
|
29
|
–
|
–
|
Kimi K3
2.8T · 104B active
|
1,545 GB
|
144
|
–
|
–
|
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 RTX 3060 · Per GPU
| Compute · dense | |
|---|---|
| FP16 / BF16 | 25.5 TFLOPS 50.9 with sparsity |
| INT8 | 101.9 TOPS 203.8 with sparsity |
| FP32 | 12.7 TFLOPS |
| Precision support | FP16BF16TF32FP32INT4INT8 |
| Memory | |
|---|---|
| Capacity | 12 GB GDDR6 |
| Bandwidth | 360 GB/s |
| Bus width | 192-bit |
| ECC | No |
| Silicon | |
|---|---|
| Architecture | Ampere |
| Process | Samsung 8N |
| Shader cores | 3,584 CUDA cores |
| Matrix cores | 112 Tensor cores |
| Compute units | 28 SMs |
| Fabric and host | |
|---|---|
| Host interface | PCIe 4.0 |
| Power | |
|---|---|
| Board power | 170 W |
| Cooling | Active |
Source: official Nvidia RTX 3060 datasheet.
As of October 3, 2026, the median on-demand price is $0.07 per GPU per hour across 3 providers.
| Billing type | Configs | Median /GPU/hr | Cheapest |
|---|---|---|---|
On-demand | 18 | $0.07 | $0.05 (in stock, Vast.ai) |
Reserved | 7 | $0.05 (1 mo, in stock, Vast.ai) |
|
Spot | 15 | $0.03 |
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 3060 costs an estimated $50 at the median on-demand price. Cheapest verified in stock: $36 per month on-demand, $36 reserved (1 mo), $22 spot.
GetDeploying currently tracks RTX 3060 configs from 3 providers. The cheapest verified in-stock on-demand configs come from Vast.ai, Theta EdgeCloud and Salad. See the full price comparison above for every provider and config.
One RTX 3060 runs models up to roughly 1B parameters at 4-bit quantization or 0.4B 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.
12GB GDDR6 with Ampere Tensor Cores. Budget GPU with decent VRAM for its price tier. Can run small quantized models.
Limited compute and bandwidth. 12GB is minimum for useful AI work. Subject to Nvidia's consumer EULA, which may restrict data center use.
Nvidia RTX 4060
8 GB GDDR6 · 24% less bandwidth than the RTX 3060
Nvidia RTX 3070
8 GB GDDR6 · 24% more bandwidth than the RTX 3060
Nvidia RTX 3060 Ti
8 GB GDDR6 · 24% more bandwidth than the RTX 3060
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