Local AI Advisor

Can Desktop PC · Radeon AI PRO R9700 · 32 GB RAM run Qwen3.5 35B-A3B?

32 GB VRAM + 32 GB RAM · 640 GB/s memory bandwidth · Qwen3.5 35B-A3B has 36B parameters (3B active per token).

Short answer

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

Best quantization for chat: Q5_K_M (24 GB of weights) on Lemonade. Generation: 160–220 tok/s, leaving about 6.0 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 WebUIExcellentQ5_K_M160–220 tok/sunder 1 s8KDetails
Coding questionswith ContinueExcellentQ5_K_M160–210 tok/s≈1 s16KDetails
Coding in a repositorywith AiderComfortableQ6_K130–170 tok/s≈3 s32KDetails
Agentic codingwith OpenCodeComfortableQ6_K120–170 tok/s≈3 s32KDetails
Long documents (64K)with Open WebUIExcellentQ5_K_M110–150 tok/s≈10 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 Q5_K_M build with a 64K context. Ollama and llama.cpp download the file straight from Hugging Face.

ollama run hf.co/unsloth/Qwen3.5-35B-A3B-GGUF:Q5_K_M

Downloads 26.25 GB (Q5_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.