Can I Run Qwen 3.6 35B-A3B MoE on NVIDIA GeForce RTX 4080 Super (16GB GDDR6X)?
Full VRAM footprint analysis, tokens/sec generation benchmark, and copy-paste Ollama & vLLM runtime commands for running Qwen 3.6 35B-A3B MoE (35B) on NVIDIA GeForce RTX 4080 Super (16GB GDDR6X) (16 GB).
✕ Out of Memory — Exceeds VRAM Capacity
OLLAMA_FLASH_ATTENTION=1 OLLAMA_KV_CACHE_TYPE=q8_0 ollama run qwen3.6:35b-a3bLocal Hardware Configurations & Pricing
⚠️ Out of VRAM: High-Capacity Hardware UpgradesNVIDIA GeForce RTX 5090 (32GB GDDR7)
Maximum tokens/sec on 27B–35B models & Blackwell FP4 quantization
NVIDIA GeForce RTX 4090 (24GB GDDR6X)
The 24GB CUDA enthusiast standard for 14B–32B Q4_K_M models
2x NVIDIA RTX 3090 Dual Rig (48GB Total VRAM)
Best price-to-VRAM homelab rig for 70B Q4_K_M & Qwen 3.8 27B Q8
NVIDIA GeForce RTX 4080 Super (16GB GDDR6X)
736 GB/s memory bandwidth for fast 14B and MoE 35B models
⚠️ Out of VRAM? Don't downgrade model quality.
Out of VRAMSpin up an on-demand cloud GPU instance in 60 seconds with pre-installed Ollama and vLLM. (Qwen 3.6 35B-A3B MoE @ 24.1 GB VRAM)
Quantization Precision Matrix
32K ctx • KV: Q8_0| Quantization | Weights | KV Cache | Total VRAM | Est. Speed | Fit Status |
|---|---|---|---|---|---|
| FP1616 bpw | 70 GB | 1.61 GB | 74.81 GB (468%) | 0 t/s | OOM |
| Q8_08.5 bpw | 37.1 GB | 1.61 GB | 40.76 GB (255%) | 0 t/s | OOM |
| Q6_K6.56 bpw | 28.7 GB | 1.61 GB | 32.06 GB (200%) | 0 t/s | OOM |
| Q5_K_M5.68 bpw | 24.85 GB | 1.61 GB | 28.08 GB (176%) | 0 t/s | OOM |
| Q4_K_M4.8 bpw | 21 GB | 1.61 GB | 24.1 GB (151%) | 0 t/s | OOM |
| FP44.25 bpw | 18.55 GB | 1.61 GB | 21.56 GB (135%) | ~55 t/s | PARTIAL |
| IQ3_XS3.3 bpw | 14.35 GB | 1.61 GB | 17.21 GB (108%) | ~67 t/s | PARTIAL |
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Frequently Asked Questions: Qwen 3.6 35B-A3B MoE on NVIDIA GeForce RTX 4080 Super (16GB GDDR6X)
How much VRAM does Qwen 3.6 35B-A3B MoE need on NVIDIA GeForce RTX 4080 Super (16GB GDDR6X)?
At standard 4-bit quantization (Q4_K_M) with 32K context, Qwen 3.6 35B-A3B MoE requires 24.1 GB of total VRAM (weights + KV cache + CUDA runtime overhead).
What inference speed will Qwen 3.6 35B-A3B MoE achieve on NVIDIA GeForce RTX 4080 Super (16GB GDDR6X)?
Based on NVIDIA GeForce RTX 4080 Super (16GB GDDR6X)'s 736 GB/s memory bandwidth, expected decode speed is approximately 0 tokens per second.
Can I expand context beyond 32K tokens?
Each additional 32K tokens of context requires additional KV cache memory. For longer context windows, consider 8-bit or 4-bit KV cache quantization.