GPU cloud: ikri NVIDIA GPU servers bis-siegħa
Ikri NVIDIA GPU servers għal inferenza ta' LLM, fine-tuning u ġenerazzjoni ta' stampi. Prezzijiet f'ħin reali, kontijiet bis-siegħa, templates għal PyTorch, vLLM u ComfyUI, aċċess b'SSH.
- Inventarju f'ħin reali, prezz ikkonfermat qabel tordna
- Templates għal PyTorch, vLLM, Ollama u ComfyUI
- Servers bis-siegħa li jkunu mitfija jiġu ffatturati biss għad-disk
B'Cloudj GPU Cloud tista' tikri server tal-GPU mill-inventarju dirett: agħżel il-mudell tal-GPU, VRAM u disk, ibda minn template bħal PyTorch, vLLM, Ollama jew ComfyUI, imbagħad ikkonnettja permezz ta' SSH. Servers ta' kull siegħa jiġu ċċarġjati mill-bilanċ tal-kont tiegħek, u server ta' kull siegħa li jkun imwaqqaf jiġi ffatturat biss għad-disk tiegħu.
Prezzijiet tal-kiri tal-GPU u disponibbiltà f'ħin reali
Inventarju dirett: id-disponibbiltà u l-prezzijiet jinbidlu skont is-suq. Il-console tikkonferma l-prezz finali qabel ma tordna.
| GPU | VRAM għal kull GPU | Arkitettura | Komputazzjoni FP32 | Limitu tal-enerġija | Minn | |
|---|---|---|---|---|---|---|
| NVIDIA RTX PRO 6000 Max-Q | 96 GB | Blackwell | ≈ 119 TFLOPS | 300 W | – | Dettalji → |
| NVIDIA RTX PRO 6000 WS | 96 GB | Blackwell | ≈ 119 TFLOPS | 600 W | – | Dettalji → |
| NVIDIA RTX 5090 | 32 GB | Blackwell | ≈ 107.6 TFLOPS | 575 W | – | Dettalji → |
| NVIDIA RTX 5090D | 32 GB | Blackwell | ≈ 107.6 TFLOPS | 575 W | – | Dettalji → |
| NVIDIA RTX PRO 6000 S | 96 GB | Blackwell | ≈ 93.6 TFLOPS | 600 W | – | Dettalji → |
| NVIDIA RTX 4090 | 24 GB | Ada Lovelace | ≈ 81.4 TFLOPS | 450 W | – | Dettalji → |
| NVIDIA RTX 6000Ada | 48 GB | Ada Lovelace | ≈ 81.4 TFLOPS | 300 W | – | Dettalji → |
| NVIDIA RTX 6000D | 84 GB | Blackwell | ≈ 77.6 TFLOPS | 600 W | – | Dettalji → |
| NVIDIA L40S datacenter | 45 GB | Ada Lovelace | ≈ 73.3 TFLOPS | 350 W | – | Dettalji → |
| NVIDIA RTX PRO 5000 | 48 GB | Blackwell | ≈ 69.6 TFLOPS | 300 W | – | Dettalji → |
| NVIDIA H100 SXM datacenter | 80 GB | Hopper | ≈ 53.5 TFLOPS | 700 W | – | Dettalji → |
| NVIDIA RTX 5080 | 16 GB | Blackwell | ≈ 53.4 TFLOPS | 360 W | – | Dettalji → |
| NVIDIA RTX 4080S | 16 GB | Ada Lovelace | ≈ 50.9 TFLOPS | 320 W | – | Dettalji → |
| NVIDIA RTX 4080 | 16 GB | Ada Lovelace | ≈ 48.6 TFLOPS | 285 W | – | Dettalji → |
| NVIDIA H100 NVL datacenter | 94 GB | Hopper | ≈ 48.3 TFLOPS | 400 W | – | Dettalji → |
| NVIDIA H200 NVL datacenter | 140 GB | Hopper | ≈ 48.3 TFLOPS | 600 W | – | Dettalji → |
| NVIDIA RTX 5070 Ti | 16 GB | Blackwell | ≈ 44.3 TFLOPS | 300 W | – | Dettalji → |
| NVIDIA RTX PRO 4000 | 24 GB | Blackwell | ≈ 44.3 TFLOPS | 145 W | – | Dettalji → |
| NVIDIA RTX 4070S Ti | 16 GB | Ada Lovelace | ≈ 42.6 TFLOPS | 285 W | – | Dettalji → |
| NVIDIA H100 PCIE datacenter | 80 GB | Hopper | ≈ 41 TFLOPS | 350 W | – | Dettalji → |
| NVIDIA RTX 4500Ada | 24 GB | Ada Lovelace | ≈ 38.2 TFLOPS | 210 W | – | Dettalji → |
| NVIDIA RTX 3090 Ti | 24 GB | Ampere | ≈ 36.4 TFLOPS | 450 W | – | Dettalji → |
| NVIDIA RTX A6000 | 48 GB | Ampere | ≈ 36.1 TFLOPS | 300 W | – | Dettalji → |
| NVIDIA RTX 4070S | 12 GB | Ada Lovelace | ≈ 35.8 TFLOPS | 220 W | – | Dettalji → |
| NVIDIA RTX 3090 | 24 GB | Ampere | ≈ 35.4 TFLOPS | 350 W | – | Dettalji → |
| NVIDIA RTX 3080 Ti | 12 GB | Ampere | ≈ 35.1 TFLOPS | 350 W | – | Dettalji → |
| NVIDIA A40 datacenter | 45 GB | Ampere | ≈ 29.9 TFLOPS | 300 W | – | Dettalji → |
| NVIDIA RTX 3080 | 10 GB | Ampere | ≈ 29.5 TFLOPS | 380 W | – | Dettalji → |
| NVIDIA RTX 4070 | 12 GB | Ada Lovelace | ≈ 29.3 TFLOPS | 200 W | – | Dettalji → |
| NVIDIA L4 datacenter | 22 GB | Ada Lovelace | ≈ 24.2 TFLOPS | 72 W | – | Dettalji → |
| NVIDIA RTX 5060 Ti | 16 GB | Blackwell | ≈ 22.8 TFLOPS | 180 W | – | Dettalji → |
| NVIDIA RTX 4060 Ti | 16 GB | Ada Lovelace | ≈ 21.6 TFLOPS | 165 W | – | Dettalji → |
| NVIDIA RTX A4000 | 16 GB | Ampere | ≈ 20.6 TFLOPS | 140 W | – | Dettalji → |
| NVIDIA RTX 3070 | 8 GB | Ampere | ≈ 19.8 TFLOPS | 220 W | – | Dettalji → |
| NVIDIA RTX 3060 Ti | 8 GB | Ampere | ≈ 15.7 TFLOPS | 200 W | – | Dettalji → |
| NVIDIA A100 PCIE datacenter | 40 GB | Ampere | ≈ 15.6 TFLOPS | 250 W | – | Dettalji → |
| NVIDIA A100 SXM4 datacenter | 40 GB | Ampere | ≈ 15.6 TFLOPS | 400 W | – | Dettalji → |
| NVIDIA A800 PCIE datacenter | 80 GB | Ampere | ≈ 15.6 TFLOPS | 300 W | – | Dettalji → |
| NVIDIA RTX 2080 Ti | 11 GB | Turing | ≈ 14.6 TFLOPS | 250 W | – | Dettalji → |
| NVIDIA RTX 3060 laptop | 12 GB | Ampere | ≈ 12.9 TFLOPS | 80 W | – | Dettalji → |
| NVIDIA Tesla V100 datacenter | 16 GB | Volta | ≈ 12.5 TFLOPS | 300 W | – | Dettalji → |
| NVIDIA CMP 170HX datacenter | 64 GB | Ampere | ≈ 12.1 TFLOPS | 250 W | – | Dettalji → |
| NVIDIA RTX 3060 | 12 GB | Ampere | ≈ 12 TFLOPS | 170 W | – | Dettalji → |
| NVIDIA Titan Xp | 12 GB | Pascal | ≈ 11.7 TFLOPS | 160 W | – | Dettalji → |
| NVIDIA RTX A2000 | 6 GB | Ampere | ≈ 11.2 TFLOPS | 70 W | – | Dettalji → |
| NVIDIA GTX 1080 Ti | 11 GB | Pascal | ≈ 11 TFLOPS | 250 W | – | Dettalji → |
| NVIDIA GTX 1080 | 8 GB | Pascal | ≈ 7.8 TFLOPS | 130 W | – | Dettalji → |
| NVIDIA GTX 1070 Ti | 8 GB | Pascal | ≈ 7.4 TFLOPS | 180 W | – | Dettalji → |
| NVIDIA GTX 1650 | 4 GB | ≈ undefined TFLOPS | W | – | Dettalji → | |
| NVIDIA Q RTX 8000 | 45 GB | Turing | ≈ 11.9 TFLOPS | 250 W | – | Dettalji → |
| NVIDIA Tesla P40 datacenter | 24 GB | Pascal | ≈ 9.4 TFLOPS | 250 W | – | Dettalji → |
| NVIDIA RTX 2070S | 8 GB | Turing | ≈ 8.8 TFLOPS | 210 W | – | Dettalji → |
| NVIDIA Q RTX 4000 | 8 GB | Turing | ≈ 7.7 TFLOPS | 125 W | – | Dettalji → |
| NVIDIA RTX 2070 | 8 GB | Turing | ≈ 7.7 TFLOPS | 150 W | – | Dettalji → |
| NVIDIA RTX 2060S | 8 GB | Turing | ≈ 7.5 TFLOPS | 175 W | – | Dettalji → |
| NVIDIA GTX TITAN X | 12 GB | Maxwell | ≈ 6.8 TFLOPS | 150 W | – | Dettalji → |
| NVIDIA RTX 2060 | 12 GB | Turing | ≈ 6.5 TFLOPS | 130 W | – | Dettalji → |
| NVIDIA GTX 1660 Ti | 6 GB | Turing | ≈ 5.3 TFLOPS | 120 W | – | Dettalji → |
| NVIDIA GTX 1660 S | 6 GB | Turing | ≈ 3.6 TFLOPS | 125 W | – | Dettalji → |
Templates tal-GPU għal workloads ta' AI, LLM u rendering
Inferenza tal-LLM
vLLM, SGLang jew Ollama b'Open WebUI biex tħaddem u tittestja mudelli tal-lingwa.
Fine-tuning
Unsloth Studio għal fine-tuning ta' mudelli tal-lingwa fuq id-dejta tiegħek stess.
Ġenerazzjoni ta' stampi u vidjows
ComfyUI għal flussi tax-xogħol ta' Flux, SDXL u vidjow.
Żvilupp f'PyTorch u CUDA
Immaġnijiet ta' PyTorch u Ubuntu 24.04 b'NVIDIA CUDA, aċċessibbli permezz ta' SSH.
Desktop jew VM sħiħa
Desktop ta' Ubuntu fil-browser, jew magna virtwali KVM sħiħa meta jkollok bżonnha.
Kif tikri GPU server f'4 passi
- 01
Agħżel template u daqs tad-disk
- 02
Agħżel GPU kompatibbli mill-inventarju dirett
- 03
Ikkonferma l-prezz u oħloq is-server mill-bilanċ tal-kont tiegħek
- 04
Ikkonnettja permezz ta' SSH jew it-terminal tal-browser
Prezzijiet u fatturazzjoni tal-GPU cloud
- Kull siegħa: il-GPU tiġi ċċarġjata kull siegħa sakemm is-server ikun qed jaħdem. Server ta' kull siegħa li jkun imwaqqaf jiġi ċċarġjat biss għad-disk tiegħu.
- Ċikli ta' fatturazzjoni: kull xahar, kull tliet xhur, kull sitt xhur jew kull sena, bi kwalunkwe skont muri qabel ma tħallas.
- Il-ħażna tad-disk tiġi ċċarġjata għal kull GB flimkien mal-prezz tal-GPU.
- Il-prezzijiet huma f'USD; tista' tarahom f'VND bir-rata tal-kambju kurrenti.