Néal GPU: cíos freastalaithe NVIDIA GPU de réir na huaire
Cíos freastalaithe NVIDIA GPU le haghaidh infeireas LLM, mionchoigeartú agus giniúint íomhánna. Praghsanna beo, billeáil in aghaidh na huaire, teimpléid PyTorch, vLLM agus ComfyUI, rochtain SSH.
- Fardal beo, praghas deimhnithe sula ndéanann tú ordú
- Teimpléid do PyTorch, vLLM, Ollama agus ComfyUI
- Ní ghearrtar bille ach as diosca ar fhreastalaithe in aghaidh na huaire atá stoptha
Le Cloudj GPU Cloud tógann tú freastalaí GPU ar cíos ón bhfardal beo: roghnaigh an tsamhail GPU, VRAM agus diosca, tosaigh ó theimpléad amhail PyTorch, vLLM, Ollama nó ComfyUI, agus ansin ceangail trí SSH. Gearrtar freastalaithe uaire ó d'iarmhéid cuntais, agus ní bhilleáiltear freastalaí uaire a bhfuil stop curtha leis ach as a dhiosca amháin.
Praghsanna cíosa GPU agus infhaighteacht bheo
Fardal beo: athraíonn infhaighteacht agus praghsanna leis an margadh. Deimhníonn an consól an praghas deiridh sula n-ordaíonn tú.
| GPU | VRAM in aghaidh an GPU | Ailtireacht | Ríomh FP32 | Teorainn chumhachta | Ó | |
|---|---|---|---|---|---|---|
| NVIDIA RTX PRO 6000 Max-Q | 96 GB | Blackwell | ≈ 119 TFLOPS | 300 W | – | Sonraí → |
| NVIDIA RTX PRO 6000 WS | 96 GB | Blackwell | ≈ 119 TFLOPS | 600 W | – | Sonraí → |
| NVIDIA RTX 5090 | 32 GB | Blackwell | ≈ 107.6 TFLOPS | 575 W | – | Sonraí → |
| NVIDIA RTX 5090D | 32 GB | Blackwell | ≈ 107.6 TFLOPS | 575 W | – | Sonraí → |
| NVIDIA RTX PRO 6000 S | 96 GB | Blackwell | ≈ 93.6 TFLOPS | 600 W | – | Sonraí → |
| NVIDIA RTX 4090 | 24 GB | Ada Lovelace | ≈ 81.4 TFLOPS | 450 W | – | Sonraí → |
| NVIDIA RTX 6000Ada | 48 GB | Ada Lovelace | ≈ 81.4 TFLOPS | 300 W | – | Sonraí → |
| NVIDIA RTX 6000D | 84 GB | Blackwell | ≈ 77.6 TFLOPS | 600 W | – | Sonraí → |
| NVIDIA L40S datacenter | 45 GB | Ada Lovelace | ≈ 73.3 TFLOPS | 350 W | – | Sonraí → |
| NVIDIA RTX PRO 5000 | 48 GB | Blackwell | ≈ 69.6 TFLOPS | 300 W | – | Sonraí → |
| NVIDIA H100 SXM datacenter | 80 GB | Hopper | ≈ 53.5 TFLOPS | 700 W | – | Sonraí → |
| NVIDIA RTX 5080 | 16 GB | Blackwell | ≈ 53.4 TFLOPS | 360 W | – | Sonraí → |
| NVIDIA RTX 4080S | 16 GB | Ada Lovelace | ≈ 50.9 TFLOPS | 320 W | – | Sonraí → |
| NVIDIA RTX 4080 | 16 GB | Ada Lovelace | ≈ 48.6 TFLOPS | 285 W | – | Sonraí → |
| NVIDIA H100 NVL datacenter | 94 GB | Hopper | ≈ 48.3 TFLOPS | 400 W | – | Sonraí → |
| NVIDIA H200 NVL datacenter | 140 GB | Hopper | ≈ 48.3 TFLOPS | 600 W | – | Sonraí → |
| NVIDIA RTX 5070 Ti | 16 GB | Blackwell | ≈ 44.3 TFLOPS | 300 W | – | Sonraí → |
| NVIDIA RTX PRO 4000 | 24 GB | Blackwell | ≈ 44.3 TFLOPS | 145 W | – | Sonraí → |
| NVIDIA RTX 4070S Ti | 16 GB | Ada Lovelace | ≈ 42.6 TFLOPS | 285 W | – | Sonraí → |
| NVIDIA H100 PCIE datacenter | 80 GB | Hopper | ≈ 41 TFLOPS | 350 W | – | Sonraí → |
| NVIDIA RTX 4500Ada | 24 GB | Ada Lovelace | ≈ 38.2 TFLOPS | 210 W | – | Sonraí → |
| NVIDIA RTX 3090 Ti | 24 GB | Ampere | ≈ 36.4 TFLOPS | 450 W | – | Sonraí → |
| NVIDIA RTX A6000 | 48 GB | Ampere | ≈ 36.1 TFLOPS | 300 W | – | Sonraí → |
| NVIDIA RTX 4070S | 12 GB | Ada Lovelace | ≈ 35.8 TFLOPS | 220 W | – | Sonraí → |
| NVIDIA RTX 3090 | 24 GB | Ampere | ≈ 35.4 TFLOPS | 350 W | – | Sonraí → |
| NVIDIA RTX 3080 Ti | 12 GB | Ampere | ≈ 35.1 TFLOPS | 350 W | – | Sonraí → |
| NVIDIA A40 datacenter | 45 GB | Ampere | ≈ 29.9 TFLOPS | 300 W | – | Sonraí → |
| NVIDIA RTX 3080 | 10 GB | Ampere | ≈ 29.5 TFLOPS | 380 W | – | Sonraí → |
| NVIDIA RTX 4070 | 12 GB | Ada Lovelace | ≈ 29.3 TFLOPS | 200 W | – | Sonraí → |
| NVIDIA L4 datacenter | 22 GB | Ada Lovelace | ≈ 24.2 TFLOPS | 72 W | – | Sonraí → |
| NVIDIA RTX 5060 Ti | 16 GB | Blackwell | ≈ 22.8 TFLOPS | 180 W | – | Sonraí → |
| NVIDIA RTX 4060 Ti | 16 GB | Ada Lovelace | ≈ 21.6 TFLOPS | 165 W | – | Sonraí → |
| NVIDIA RTX A4000 | 16 GB | Ampere | ≈ 20.6 TFLOPS | 140 W | – | Sonraí → |
| NVIDIA RTX 3070 | 8 GB | Ampere | ≈ 19.8 TFLOPS | 220 W | – | Sonraí → |
| NVIDIA RTX 3060 Ti | 8 GB | Ampere | ≈ 15.7 TFLOPS | 200 W | – | Sonraí → |
| NVIDIA A100 PCIE datacenter | 40 GB | Ampere | ≈ 15.6 TFLOPS | 250 W | – | Sonraí → |
| NVIDIA A100 SXM4 datacenter | 40 GB | Ampere | ≈ 15.6 TFLOPS | 400 W | – | Sonraí → |
| NVIDIA A800 PCIE datacenter | 80 GB | Ampere | ≈ 15.6 TFLOPS | 300 W | – | Sonraí → |
| NVIDIA RTX 2080 Ti | 11 GB | Turing | ≈ 14.6 TFLOPS | 250 W | – | Sonraí → |
| NVIDIA RTX 3060 laptop | 12 GB | Ampere | ≈ 12.9 TFLOPS | 80 W | – | Sonraí → |
| NVIDIA Tesla V100 datacenter | 16 GB | Volta | ≈ 12.5 TFLOPS | 300 W | – | Sonraí → |
| NVIDIA CMP 170HX datacenter | 64 GB | Ampere | ≈ 12.1 TFLOPS | 250 W | – | Sonraí → |
| NVIDIA RTX 3060 | 12 GB | Ampere | ≈ 12 TFLOPS | 170 W | – | Sonraí → |
| NVIDIA Titan Xp | 12 GB | Pascal | ≈ 11.7 TFLOPS | 160 W | – | Sonraí → |
| NVIDIA RTX A2000 | 6 GB | Ampere | ≈ 11.2 TFLOPS | 70 W | – | Sonraí → |
| NVIDIA GTX 1080 Ti | 11 GB | Pascal | ≈ 11 TFLOPS | 250 W | – | Sonraí → |
| NVIDIA GTX 1080 | 8 GB | Pascal | ≈ 7.8 TFLOPS | 130 W | – | Sonraí → |
| NVIDIA GTX 1070 Ti | 8 GB | Pascal | ≈ 7.4 TFLOPS | 180 W | – | Sonraí → |
| NVIDIA GTX 1650 | 4 GB | ≈ undefined TFLOPS | W | – | Sonraí → | |
| NVIDIA Q RTX 8000 | 45 GB | Turing | ≈ 11.9 TFLOPS | 250 W | – | Sonraí → |
| NVIDIA Tesla P40 datacenter | 24 GB | Pascal | ≈ 9.4 TFLOPS | 250 W | – | Sonraí → |
| NVIDIA RTX 2070S | 8 GB | Turing | ≈ 8.8 TFLOPS | 210 W | – | Sonraí → |
| NVIDIA Q RTX 4000 | 8 GB | Turing | ≈ 7.7 TFLOPS | 125 W | – | Sonraí → |
| NVIDIA RTX 2070 | 8 GB | Turing | ≈ 7.7 TFLOPS | 150 W | – | Sonraí → |
| NVIDIA RTX 2060S | 8 GB | Turing | ≈ 7.5 TFLOPS | 175 W | – | Sonraí → |
| NVIDIA GTX TITAN X | 12 GB | Maxwell | ≈ 6.8 TFLOPS | 150 W | – | Sonraí → |
| NVIDIA RTX 2060 | 12 GB | Turing | ≈ 6.5 TFLOPS | 130 W | – | Sonraí → |
| NVIDIA GTX 1660 Ti | 6 GB | Turing | ≈ 5.3 TFLOPS | 120 W | – | Sonraí → |
| NVIDIA GTX 1660 S | 6 GB | Turing | ≈ 3.6 TFLOPS | 125 W | – | Sonraí → |
Teimpléid GPU d'ualaí oibre AI, LLM agus rindreála
Tátal LLM
vLLM, SGLang nó Ollama le Open WebUI chun samhlacha teanga a chur ar fáil agus a thástáil.
Míntiúnáil
Unsloth Studio le haghaidh míntiúnáil a dhéanamh ar shamhlacha teanga ar do chuid sonraí féin.
Giniúint íomhánna agus físeán
ComfyUI le haghaidh sreafaí oibre Flux, SDXL agus físeán.
Forbairt PyTorch agus CUDA
Íomhánna PyTorch agus Ubuntu 24.04 le NVIDIA CUDA, inrochtana trí SSH.
Deasc nó VM iomlán
Deasc Ubuntu sa bhrabhsálaí, nó meaisín fíorúil KVM iomlán nuair a theastaíonn sé uait.
Conas freastalaí GPU a fháil ar cíos i 4 chéim
- 01
Roghnaigh teimpléad agus méid diosca
- 02
Roghnaigh GPU comhoiriúnach ón bhfardal beo
- 03
Deimhnigh an praghas agus cruthaigh an freastalaí ó d'iarmhéid cuntais
- 04
Ceangail trí SSH nó trí theirminéal an bhrabhsálaí
Praghsáil agus billeáil néal GPU
- In aghaidh na huaire: gearrtar táille ar an GPU in aghaidh na huaire fad a bhíonn an freastalaí ag rith. Ní ghearrtar táille ach as a dhiosca ar fhreastalaí uaire a bhfuil stop curtha leis.
- Timthriallta billeála: míosúil, ráithiúil, leathbhliantúil nó bliantúil, agus taispeántar aon lascaine sula n-íocann tú.
- Gearrtar táille ar stóras diosca in aghaidh an GB in éineacht le praghas an GPU.
- Is i USD atá na praghsanna; is féidir iad a thaispeáint i VND ag an ráta malairte reatha.