Run serverless GPUs on
Run serverless GPUs on
your
Cloud.
Azure.
AWS.
GCP.
Cloud.
Azure.
AWS.
GCP.
Cloud.
Azure.
AWS.
GCP.
Cloud.
Azure.
AWS.
GCP.
Cloud.
Azure.
AWS.
GCP.
Cloud.
Azure.
AWS.
GCP.
Cloud.
Azure.
AWS.
GCP.
Cloud.
Azure.
AWS.
GCP.
Deploy and auto-scale generative AI models on your own infra. Pay for what you use, no idle costs.
Deploy and auto-scale generative AI models on your own infra. Pay for what you use, no idle costs.
Backed by
Trusted by
The Forecasting Company
T
F
C
Lumina
Haystack
The Forecasting Company
T
F
C
Lumina
Haystack
The Forecasting Company
T
F
C
Lumina
Haystack
Run serverless GPUs on
your
Cloud.
Azure.
AWS.
GCP.
Cloud.
Azure.
AWS.
GCP.
Cloud.
Azure.
AWS.
GCP.
Cloud.
Azure.
AWS.
GCP.
Cloud.
Azure.
AWS.
GCP.
Cloud.
Azure.
AWS.
GCP.
Cloud.
Azure.
AWS.
GCP.
Cloud.
Azure.
AWS.
GCP.
Deploy and auto-scale generative AI models on your own infra. Pay for what you use, no idle costs.
Backed by
Trusted by
The Forecasting Company
T
F
C
Lumina
Haystack
Ship fast.
Ship fast.
Leave the heavy lifting to us.
Leave the heavy lifting to us.
Connect
Connect your cloud account (AWS, GCP or Azure) and Tensorfuse will automatically provision the resources to manage your infra.
Connect
Connect your cloud account (AWS, GCP or Azure) and Tensorfuse will automatically provision the resources to manage your infra.
Deploy
Deploy ML models to your own cloud via the Tensorfuse SDK.
Data never leaves your cloud and you can start using an OpenAI compatible API.
import tensorkube
image = tensorkube.Image.from_registry(
"nvidia/cuda" ).add_python(version='3.9')
.apt_install([ 'git','git-lfs' ])
.pip_install([ 'transformers', 'torch', 'torchvision', 'tensorrt', ])
.env( { 'SOME-RANDOM-SECRET-KEY': 'xxx-xyz-1234-abc-5678', } )
.run_custom_function( download_and_quantize_model, )
@tensorkube.entrypoint(image, gpu = 'A10G')
def load_model_on_gpu():
import transformers
model = transformers.BertModel.from_pretrained('bert-base-uncased')
model.to('cuda')
tensorkube.pass_reference(model, 'model')
@tensorkube.function(image)
def infer(input: str):
model = tensorkube.get_reference('model')
# test the model on input
response = model(input)
return response
Deploy
Deploy ML models to your own cloud via the Tensorfuse SDK.
Data never leaves your cloud and you can start using an OpenAI compatible API.
import tensorkube
image = tensorkube.Image.from_registry(
"nvidia/cuda" ).add_python(version='3.9')
.apt_install([ 'git','git-lfs' ])
.pip_install([ 'transformers', 'torch', 'torchvision', 'tensorrt', ])
.env( { 'SOME-RANDOM-SECRET-KEY': 'xxx-xyz-1234-abc-5678', } )
.run_custom_function( download_and_quantize_model, )
@tensorkube.entrypoint(image, gpu = 'A10G')
def load_model_on_gpu():
import transformers
model = transformers.BertModel.from_pretrained('bert-base-uncased')
model.to('cuda')
tensorkube.pass_reference(model, 'model')
@tensorkube.function(image)
def infer(input: str):
model = tensorkube.get_reference('model')
# test the model on input
response = model(input)
return response
Scale
Tensorfuse automatically scales in response to the amount of traffic your app receives.
Fast cold boots with our optimized container system
Scale
Tensorfuse automatically scales in response to the amount of traffic your app receives.
Fast cold boots with our optimized container system
Ease and speed of serverless.
Flexibility and control of your own infra.
Ease and speed of serverless.
Flexibility and control of your own infra.
Customize your environment
Describe container images and hardware specifications in simple Python. No YAML.
import tensorkube
image = tensorkube.Image.from_registry(
"nvidia/cuda" ).add_python(version='3.9')
.apt_install([ 'git','git-lfs' ])
.pip_install([ 'transformers', 'torch', 'torchvision', 'tensorrt', ])
.env( { 'SOME-RANDOM-SECRET-KEY': 'xxx-xyz-1234-abc-5678', } )
.run_custom_function( download_and_quantize_model, )
@tensorkube.use_image(image)
def infer():
print('Your inference code goes Here!')
Customize your environment
Describe container images and hardware specifications in simple Python. No YAML.
import tensorkube
image = tensorkube.Image.from_registry(
"nvidia/cuda" ).add_python(version='3.9')
.apt_install([ 'git','git-lfs' ])
.pip_install([ 'transformers', 'torch', 'torchvision', 'tensorrt', ])
.env( { 'SOME-RANDOM-SECRET-KEY': 'xxx-xyz-1234-abc-5678', } )
.run_custom_function( download_and_quantize_model, )
@tensorkube.use_image(image)
def infer():
print('Your inference code goes Here!')
Customize your environment
Describe container images and hardware specifications in simple Python. No YAML.
import tensorkube
image = tensorkube.Image.from_registry(
"nvidia/cuda" ).add_python(version='3.9')
.apt_install([ 'git','git-lfs' ])
.pip_install([ 'transformers', 'torch', 'torchvision', 'tensorrt', ])
.env( { 'SOME-RANDOM-SECRET-KEY': 'xxx-xyz-1234-abc-5678', } )
.run_custom_function( download_and_quantize_model, )
@tensorkube.use_image(image)
def infer():
print('Your inference code goes Here!')
Customize your environment
Describe container images and hardware specifications in simple Python. No YAML.
import tensorkube
image = tensorkube.Image.from_registry(
"nvidia/cuda" ).add_python(version='3.9')
.apt_install([ 'git','git-lfs' ])
.pip_install([ 'transformers', 'torch', 'torchvision', 'tensorrt', ])
.env( { 'SOME-RANDOM-SECRET-KEY': 'xxx-xyz-1234-abc-5678', } )
.run_custom_function( download_and_quantize_model, )
@tensorkube.use_image(image)
def infer():
print('Your inference code goes Here!')
Private by default
Your model and data live within your private cloud.
Private by default
Your model and data live within your private cloud.
Private by default
Your model and data live within your private cloud.
Private by default
Your model and data live within your private cloud.
Scale at will
Meet user demand in real time by scaling GPU workers from zero to hundreds in seconds.
Scale at will
Meet user demand in real time by scaling GPU workers from zero to hundreds in seconds.
Scale at will
Meet user demand in real time by scaling GPU workers from zero to hundreds in seconds.
Scale at will
Meet user demand in real time by scaling GPU workers from zero to hundreds in seconds.
Cost effective
Reduce egress charges by using model inference within your cloud environment.
Cost effective
Reduce egress charges by using model inference within your cloud environment.
Cost effective
Reduce egress charges by using model inference within your cloud environment.
Cost effective
Reduce egress charges by using model inference within your cloud environment.
OpenAI compatible
Start using your deployment on an OpenAI compatible endpoint.
OpenAI compatible
Start using your deployment on an OpenAI compatible endpoint.
OpenAI compatible
Start using your deployment on an OpenAI compatible endpoint.
OpenAI compatible
Start using your deployment on an OpenAI compatible endpoint.
Compute utilization
Easily utilize compute resources across multiple cloud providers.
Compute utilization
Easily utilize compute resources across multiple cloud providers.
Compute utilization
Easily utilize compute resources across multiple cloud providers.
Compute utilization
Easily utilize compute resources across multiple cloud providers.
Blog
Blog
Pricing for every team's size.
Pricing for every team's size.
Compute Management Costs
We charge compute management costs to make it possible for us to manage your infrastructure in a way that scales and is fair for various team sizes.
GPUs
$0.1 / GPU / hour
vCPUs
$0.007 / vCPU / hour
Compute Management Costs
We charge compute management costs to make it possible for us to manage your infrastructure in a way that scales and is fair for various team sizes.
GPUs
$0.1 / GPU / hour
vCPUs
$0.007 / vCPU / hour
Compute Management Costs
We charge compute management costs to make it possible for us to manage your infrastructure in a way that scales and is fair for various team sizes.
GPUs
$0.1 / GPU / hour
vCPUs
$0.007 / vCPU / hour
Free tier
$0
+ Compute Management Cost
1 Seat included
10 GPU hours / month free
Community Support
Free tier
$0
+ Compute Management Cost
1 Seat included
10 GPU hours / month free
Community Support
Free tier
$0
+ Compute Management Cost
1 Seat included
10 GPU hours / month free
Community Support
Free tier
$0
+ Compute Management Cost
1 Seat included
10 GPU hours / month free
Community Support
Team
$150
+ Compute Management Cost
10 Seats included
10 GPU hours / month free
Support via Private Slack
Team
$150
+ Compute Management Cost
10 Seats included
10 GPU hours / month free
Support via Private Slack
Team
$150
+ Compute Management Cost
10 Seats included
10 GPU hours / month free
Support via Private Slack
Team
$150
+ Compute Management Cost
10 Seats included
10 GPU hours / month free
Support via Private Slack
Enterprise
Custom
+ Compute Management Cost
Everything in the Team plan
Custom requirements
Tailored to your requirements
Enterprise
Custom
+ Compute Management Cost
Everything in the Team plan
Custom requirements
Tailored to your requirements
Enterprise
Custom
+ Compute Management Cost
Everything in the Team plan
Custom requirements
Tailored to your requirements
Enterprise
Custom
+ Compute Management Cost
Everything in the Team plan
Custom requirements
Tailored to your requirements
Get started with Tensorfuse today.
Get started with Tensorfuse today.
Deploy in minutes, scale in seconds.
import tensorkube
image = tensorkube.Image.from_registry(
"nvidia/cuda" ).add_python(version='3.9')
.apt_install([ 'git','git-lfs' ])
.pip_install([ 'transformers', 'torch', 'torchvision', 'tensorrt', ])
.env( { 'SOME-RANDOM-SECRET-KEY': 'xxx-xyz-1234-abc-5678', } )
.run_custom_function( download_and_quantize_model, )
@tensorkube.entrypoint(image, gpu = 'A10G')
def load_model_on_gpu():
import transformers
model = transformers.BertModel.from_pretrained('bert-base-uncased')
model.to('cuda')
tensorkube.pass_reference(model, 'model')
@tensorkube.function(image)
def infer(input: str):
model = tensorkube.get_reference('model')
# test the model on input
response = model(input)
return response
Get started with Tensorfuse today.
Deploy in minutes, scale in seconds.
import tensorkube
image = tensorkube.Image.from_registry(
"nvidia/cuda" ).add_python(version='3.9')
.apt_install([ 'git','git-lfs' ])
.pip_install([ 'transformers', 'torch', 'torchvision', 'tensorrt', ])
.env( { 'SOME-RANDOM-SECRET-KEY': 'xxx-xyz-1234-abc-5678', } )
.run_custom_function( download_and_quantize_model, )
@tensorkube.entrypoint(image, gpu = 'A10G')
def load_model_on_gpu():
import transformers
model = transformers.BertModel.from_pretrained('bert-base-uncased')
model.to('cuda')
tensorkube.pass_reference(model, 'model')
@tensorkube.function(image)
def infer(input: str):
model = tensorkube.get_reference('model')
# test the model on input
response = model(input)
return response
© 2024. All rights reserved.
Privacy Policy