Local AI Advisor

Can Desktop PC · GeForce RTX 5090 · 64 GB RAM run Llama 3.2 3B Instruct?

32 GB VRAM + 64 GB RAM · 1792 GB/s memory bandwidth · Llama 3.2 3B Instruct has 3.2B parameters.

Short answer

Yes, excellent for chat; not recommended for coding in a repository.

Best quantization for chat: Q6_K (2.5 GB of weights) on Ollama. Generation: 320–430 tok/s, leaving about 27 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_K320–430 tok/sunder 1 s8KDetails
Coding questionswith ContinueAcceptableQ8_0230–310 tok/sunder 1 s16KDetails
Coding in a repositorywith AiderTechnically RunsQ6_K210–280 tok/sunder 1 s32KDetails
Agentic codingwith OpenCodeTechnically RunsQ8_0160–220 tok/s≈1 s32KDetails
Long documents (64K)with Open WebUIComfortableQ8_0100–140 tok/s≈3 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-3.2-3B-Instruct-GGUF:Q8_0

Downloads 3.42 GB (Q8_0). 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