Alibaba
Qwen3 235B-A22B
Flagship MoE — 235 B total parameters, 22 B active. Frontier quality but needs 80 GB+ VRAM to run.
About This Model
Qwen3 235B-A22B is Alibaba flagship open-weights model. Real frontier quality, but the 144 GB VRAM bar at Q4 puts it firmly in the dual-H100 / multi-A100 territory. Not consumer-runnable yet, but listed for cloud-GPU planning.
Check Your Hardware
See which quantizations of Qwen3 235B-A22B your hardware can run.
Quantization Options
| Quantization | Bits | File Size | VRAM Needed | RAM Needed | Quality |
|---|---|---|---|---|---|
| Q4_K_M | 4.5 | 140 GB | 144 GB | 200 GB | 85% |
Context window & KV cache
Adds 3.00 GB to VRAMLong chats and RAG inputs cost real memory. Drag to see how 32K vs 128K context shifts your grade.
Model native max: 32K tokens. KV-cache estimate is approximate (±30 %); real usage depends on attention layout.
How to run Qwen3 235B-A22B
Pick a runtime — copy & paste. Commands are pre-filled with this model’s repo.
GUI. Browse → download → chat. MLX on Apple Silicon.
LM Studio home →- 1
Open LM Studio
Go to the 🔍 Search tab.
- 2
Search for
unsloth/Qwen3-235B-A22B-GGUF - 3
Download
Pick the Q4_K_M quant — best balance of size vs. quality.
- 4
Chat
Hit ▶ Load Model and start chatting. Toggle 'Local Server' to expose an OpenAI-compatible API on :1234.
Community benchmarks
Real tokens/sec reports from people running Qwen3 235B-A22B on actual hardware.
No community runs yet for this model. Be the first to submit your numbers.
See It In Action
Real model outputs generated via RunThisModel.com — watch responses stream in real time.
Outputs generated by real AI models via RunThisModel.com. Generation speed shown is from cloud inference. Local speeds vary by hardware — check your device.
Frequently Asked Questions
How much VRAM do I need to run Qwen3 235B-A22B?
Qwen3 235B-A22B requires 144GB VRAM minimum with Q4_K_M quantization. For full precision, you need 144GB VRAM.
What is the best quantization for Qwen3 235B-A22B?
Q4_K_M offers the best balance of quality and VRAM usage. Q8_0 is near-lossless if you have enough VRAM.