An RTX 4090 alternative for LLMs — rent ASUS GX10
If you are looking for RTX 4090 for an LLM, start with the model’s memory requirements. RTX 4090 has 24 GB of dedicated VRAM; ASUS Ascent GX10 has 128 GB shared by CPU and GPU. Request GX10 when you want to evaluate a larger model on one node and compare the cost of the result.
When memory capacity is the priority
A rough weights-only estimate for a 70B model at 4 bits is about 35 GB. This already exceeds one RTX 4090’s VRAM. A smaller model can also reach a memory limit with longer contexts or concurrent requests. Compare weights, KV cache and buffers against the actual system’s memory budget.
What changes when moving to GB10
GX10 is a Grace Blackwell system with an ARM64 processor, rather than an RTX card with more VRAM. Moving a workload requires compatible containers, frameworks and extensions. Greater capacity does not establish higher speed: the engine and workload determine latency and the number of requests the system can serve.
Compare using one test set
Fix the model, weight format, context and concurrency. Check response quality first, then latency, memory use and the cost of the run. For image generation or rendering, use your actual pipeline: an LLM comparison does not establish results for another workload.
Rent ASUS for your selected model
At rentGPU you can request one GX10 or two nodes. One system supports evaluating an environment; a pair can run independent experiments or a model with multi-node runtime support. Share the model and duration requirements, then choose the configuration and rate on the ASUS page.
ASUS GX10 rental rate
One hourly rate applies to the selected rental duration. The calculator multiplies it by the full rental period.
The rate is for one ASUS Ascent GX10 with 128 GB of memory shared by the CPU and GPU.
Choose ASUS for your model
Share the model, quantization, context and dates. Choose one node or a pair and agree on a configuration for your workload.
Models for ASUS GX10ASUS GX10 rental
Before you rent
Is GX10 faster than RTX 4090?
That needs measuring on your workload. GX10 is presented here as an option with a larger shared memory budget. Speed depends on the model, engine and concurrency.
Is the GX10’s 128 GB dedicated VRAM?
It is unified CPU and GPU memory. The operating system and applications also use it. Account for the model’s complete working set.
Can I move a Docker image from an RTX system?
Check for an ARM64 image and GB10 support in its dependencies. Custom CUDA extensions may need building for the new environment.
What should I choose for the first test?
Start with one model and one GX10 in most evaluations. Specify peak context and target latency so the test represents the intended service.