Rent an NVIDIA CMP 170HX GPU with 64 GB VRAM
Rent CMP 170HX cloud GPU servers by the hour on Cloudj: 64 GB VRAM listed, Ampere architecture and about 1516 GB/s memory bandwidth for memory-bound compute. It is a headless compute card for developers who care about bandwidth and capacity more than FP32 speed (12.1 TFLOPS, 250 W, compute capability 8.0). Pick a live configuration, start from the Ubuntu 22.04 + CUDA or PyTorch + JupyterLab template and connect over SSH with your key.
CMP 170HX specifications
VRAM per GPU
64 GB
Architecture
NVIDIA Ampere
Compute capability
8
FP32 compute
≈ 12.1 TFLOPS
Memory bandwidth
≈ 1516 GB/s
Power limit
250 W
Values reported by the infrastructure provider for machines currently listed; real performance varies by machine.
Live CMP 170HX configurations and prices
From $0.43/h
incl. 32 GB disk · Live price, updated every 15 minutes
What you can run on the CMP 170HX cloud GPU
Bandwidth-bound inference
LLM token generation is limited mainly by memory bandwidth, and 1516 GB/s is in the same range as A100-class cards.
Quantized LLM hosting
Load quantized models in Ollama or llama.cpp and serve them over an API for testing or internal tools.
CUDA kernel experiments
Profile memory-heavy kernels on an Ampere card with compute capability 8.0 before moving them to larger hardware.
Batch data processing
Stream large arrays through GPU memory for transforms, hashing or analytics jobs that are bound by memory access.
Is the CMP 170HX the right GPU for your workload?
Choose the CMP 170HX when your workload is bottlenecked by memory capacity and bandwidth rather than raw single-precision compute. Opt for a GPU with higher compute throughput if you require faster training iterations, or select a smaller consumer card if your models do not need 64 GB of VRAM.
Guides for CMP 170HX
CMP 170HX cloud GPU: frequently asked questions
Rent the CMP 170HX cloud GPU by the hour
Pick a live configuration, choose a template and connect over SSH with your key. Billing stops when you destroy the instance.