Try an uncensored LLM before a longer server rental
A trial run helps you choose a text model before committing to a longer server rental. Prepare two model candidates and prompts from your application, then agree a short test on ASUS Ascent GX10. This evaluates the selected builds on GB10 and your workload’s memory requirements.
Choose two models to compare
Consider Dolphin 3.0 Llama 3.1 8B for an initial run. To evaluate an abliterated version specifically, plan a comparison of Qwen/Qwen3-32B with huihui-ai/Qwen3-32B-abliterated. Dolphin’s card lists Llama 3.1, while both Qwen cards list Apache 2.0. Read the repository conditions and record revisions before downloading.
Uncensored describes modified model behavior, while abliterated identifies a weight modification intended to reduce refusals. Evaluate how well each candidate performs your task, such as answering from a document or returning valid JSON. The weights and runtime for this comparison still need validation on GX10.
Prepare a reproducible environment
ASUS GX10 needs ARM64-compatible dependencies and an engine build that supports GB10. Record the driver, runtime and web-application versions. Check the selected quantization, chat template and context limit. An Ollama ARM64 package or a llama.cpp HTTP server alone does not validate the complete application stack.
You can plan sequential runs of two models on one node. For concurrent independent tests, consider a GX10 pair if both systems are available. Each node has its own 128 GB of unified memory, with a separate budget for weights, KV cache and supporting processes.
Run prompts from your application
Use the same prompt set for both models: a short conversation, an answer grounded in your document, a code example or structured output. Apply each model’s correct chat template and record the system prompt and generation settings. Save responses alongside their inputs so you can inspect the differences.
Check factual errors, format compliance and the language your users need. In the web application, test streaming, cancellation and error handling. Measure time to first token and memory use in your own test; the result applies to that build and configuration rather than every model on GX10.
Allow time for the complete trial
Include downloading both models, setup, warm-up and saving results in the short rental period. Enter the planned duration in the ASUS GX10 calculator, since the hourly rate depends on rental length. Trial dates and availability of one node or a pair require separate confirmation.
After testing, retain the weight identifiers, runtime configuration and evaluation results. Use them to choose a model and budget for a longer rental, or adjust the next test. The site’s forms are currently demonstrations: submitting one neither starts a model nor confirms a reservation.
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 there a free uncensored chat to try?
This page helps you plan a short hardware rental for your own test. It does not provide a free public chat. Time-based costs are calculated on the ASUS GX10 page, and the actual trial needs to be agreed separately.
Do I need two GX10 systems to compare two models?
Start with sequential tests on one node if the chosen models and runtime are compatible with it. A pair supports independent concurrent environments or a separately configured distributed deployment; it does not provide a default shared 256 GB memory pool.
What should I prepare for the trial?
Provide two model links with revisions, the weight format, intended runtime and a prompt set. Specify context length, quality criteria and planned duration. For a web interface, add the API requirements and application version.
Will an uncensored model outperform the original?
Evaluate that on your prompt set. Modified refusal behavior does not establish accuracy, code quality or document handling. Compare useful responses and errors from both models under recorded conditions.