Local AI Advisor

Can Desktop PC · Radeon RX 9070 XT · 32 GB RAM run Qwen3-Next 80B-A3B Instruct?

16 GB VRAM + 32 GB RAM · 640 GB/s memory bandwidth · Qwen3-Next 80B-A3B Instruct has 81.3B parameters (3B active per token).

Short answer

It loads, but it's borderline even for chat.

Best quantization for chat: Q3_K_M (37 GB of weights) on Lemonade. Generation: 35–45 tok/s, leaving about 0.0 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 WebUIBorderlineQ3_K_M35–45 tok/s≈2 s8KDetails
Coding questionswith ContinueDoes Not Fit————Details
Coding in a repositorywith AiderDoes Not Fit————Details
Agentic codingwith OpenCodeDoes Not Fit————Details
Long documents (64K)with Open WebUIDoes Not Fit————Details

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 Q3_K_M build with a 8K context. Ollama and llama.cpp download the file straight from Hugging Face.

ollama run hf.co/unsloth/Qwen3-Next-80B-A3B-Instruct-GGUF:Q3_K_M

Downloads 38.3 GB (Q3_K_M). Ollama's default context is small: set OLLAMA_CONTEXT_LENGTH=8192 before `ollama serve` for long prompts.

Different tool, context size or workload?

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