Set Up llama.cpp with GPU (RTX 5080) and Optimize for Mixtral – Test Q5_K_M vs Q4_K_M
Budget: $30 – $250 AUD
I need a skilled developer to remotely configure my Windows PC for optimal use with the Mixtral model via llama.cpp.
The goal is to install everything needed, build llama.cpp with full CUDA GPU acceleration for my RTX 5080, and optimize the setup so it runs future Mixtral .gguf models with maximum speed and stability.
Key Tasks:
Install all required dependencies (build tools, Python, latest CUDA)
Build llama.cpp with LLAMA_CUBLAS=1 for GPU support
Optimize runtime settings for my RTX 5080 (context size, threads, GPU layers)
Test two quant formats: Q4_K_M and Q5_K_M
Compare performance of both (speed, GPU load, stability)
Show nvidia-smi during test to confirm GPU is working
Leave the system ready for me to drop in Mixtral later and run instantly
Requirements:
You must use AnyDesk or RustDesk only — no long-term access
You’ll be supervised during the session
Please explain what you’re doing as you go
Clean, efficient install — no extra bloat or services
Deliverables:
llama.cpp built and ready with GPU
My system fully prepared for future Mixtral usage
Performance test results and confirmation of GPU use
Looking for someone with experience in CUDA, GPU optimization, and LLM model deployment. Please be ready to recommend ideal Mixtral quant + config.
The goal is to install everything needed, build llama.cpp with full CUDA GPU acceleration for my RTX 5080, and optimize the setup so it runs future Mixtral .gguf models with maximum speed and stability.
Key Tasks:
Install all required dependencies (build tools, Python, latest CUDA)
Build llama.cpp with LLAMA_CUBLAS=1 for GPU support
Optimize runtime settings for my RTX 5080 (context size, threads, GPU layers)
Test two quant formats: Q4_K_M and Q5_K_M
Compare performance of both (speed, GPU load, stability)
Show nvidia-smi during test to confirm GPU is working
Leave the system ready for me to drop in Mixtral later and run instantly
Requirements:
You must use AnyDesk or RustDesk only — no long-term access
You’ll be supervised during the session
Please explain what you’re doing as you go
Clean, efficient install — no extra bloat or services
Deliverables:
llama.cpp built and ready with GPU
My system fully prepared for future Mixtral usage
Performance test results and confirmation of GPU use
Looking for someone with experience in CUDA, GPU optimization, and LLM model deployment. Please be ready to recommend ideal Mixtral quant + config.