Local AI Advisor

Can Desktop PC · Ryzen 9 9950X (no GPU) · 64 GB DDR5 run Qwen3-Next 80B-A3B Instruct?

64 GB RAM, no GPU · Qwen3-Next 80B-A3B Instruct has 81.3B parameters (3B active per token).

Short answer

Yes, comfortable for chat and acceptable or better for every workload we test.

Best quantization for chat: Q4_K_M (45 GB of weights) on llama.cpp (llama-server). Generation: 15–25 tok/s, leaving about 6.4 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 WebUIComfortableQ4_K_M15–25 tok/s≈3 s8KDetails
Coding questionswith ContinueAcceptableQ4_K_M14–25 tok/s≈15 s16KDetails
Coding in a repositorywith AiderAcceptableQ4_K_M13–20 tok/s≈50 s32KDetails
Agentic codingwith OpenCodeAcceptableQ3_K_M14–25 tok/s≈80 s32KDetails
Long documents (64K)with Open WebUIAcceptableQ4_K_M9–15 tok/s≈3 min64KDetails

Speeds are bandwidth-based estimates; no direct benchmark exists for this pair. “First reply” is the time to read the workload’s opening prompt. How we calculate

Get it running

Copy-paste commands for the Q4_K_M build with a 64K context. Ollama and llama.cpp download the file straight from Hugging Face.

ollama run hf.co/unsloth/Qwen3-Next-80B-A3B-Instruct-GGUF:Q4_K_M

Downloads 48.51 GB (Q4_K_M). 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.