Local AI Advisor

Can Desktop PC · Radeon AI PRO R9700 · 32 GB RAM run Llama 3.1 8B Instruct?

32 GB VRAM + 32 GB RAM · 640 GB/s memory bandwidth · Llama 3.1 8B Instruct has 8B parameters.

Short answer

Yes, excellent for chat; borderline for coding in a repository.

Best quantization for chat: Q6_K (6.3 GB of weights) on Lemonade. Generation: 65–90 tok/s, leaving about 23 GB free for your other apps.

How it rates for each workload

Same model and machine, different jobs. Agents and long documents send far bigger prompts than chat, so they need much more speed and memory.

WorkloadVerdictQuantGenerationFirst replyContext
Chatwith Open WebUIExcellentQ6_K65–90 tok/sunder 1 s8KDetails
Coding questionswith ContinueAcceptableQ6_K65–85 tok/s≈1 s16KDetails
Coding in a repositorywith AiderBorderlineQ8_045–60 tok/s≈4 s32KDetails
Agentic codingwith OpenCodeBorderlineQ8_040–55 tok/s≈5 s32KDetails
Long documents (64K)with Open WebUIComfortableQ8_030–40 tok/s≈20 s64KDetails

Speeds are estimates calibrated against measured benchmarks on similar hardware. “First reply” is the time to read the workload’s opening prompt. How we calculate

Get it running

Copy-paste commands for the Q8_0 build with a 64K context. Ollama and llama.cpp download the file straight from Hugging Face.

ollama run hf.co/unsloth/Llama-3.1-8B-Instruct-GGUF:UD-Q8_K_XL

Downloads 10.58 GB (UD-Q8_K_XL). Ollama's default context is small: set OLLAMA_CONTEXT_LENGTH=65536 before `ollama serve` for long prompts.

Different tool, context size or workload?

These pages use typical settings. The full check models your exact setup.

Open weights (restricted)Check my exact setup