Local AI Advisor

Can Desktop PC · 2× GeForce RTX 3090 (48 GB) · 128 GB RAM run Qwen3.6 35B-A3B?

48 GB VRAM + 128 GB RAM · 936 GB/s memory bandwidth · Qwen3.6 35B-A3B has 36B parameters (3B active per token).

Short answer

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

Best quantization for chat: Q6_K (28 GB of weights) on Ollama. Generation: 140–230 tok/s, leaving about 18 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_K140–230 tok/sunder 1 s8KDetails
Coding questionswith ContinueExcellentQ6_K130–220 tok/sunder 1 s16KDetails
Coding in a repositorywith AiderExcellentQ6_K120–200 tok/s≈2 s32KDetails
Agentic codingwith OpenCodeExcellentQ6_K120–200 tok/s≈3 s32KDetails
Long documents (64K)with Open WebUIExcellentQ6_K95–160 tok/s≈8 s64KDetails

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 Q6_K build with a 64K context. Ollama and llama.cpp download the file straight from Hugging Face.

ollama run hf.co/unsloth/Qwen3.6-35B-A3B-GGUF:UD-Q6_K_XL

Downloads 31.84 GB (UD-Q6_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.