Qwen3 235B A22B Thinking 2507 — Hardware Requirements & GPU Compatibility
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Qwen3 235B A22B Thinking 2507 is the reasoning and chain-of-thought variant of Alibaba's largest Qwen3 mixture-of-experts model, updated in July 2025. With 235 billion total parameters and about 22 billion active per forward pass, it represents the pinnacle of Qwen3's reasoning capabilities. This model excels at complex multi-step problems, mathematical reasoning, code analysis, and tasks requiring deep logical thinking. It demands serious hardware to run locally, but for users with multi-GPU setups, it offers reasoning performance that rivals the best proprietary models while keeping all computation on your own machines.
53.5K downloads
399 likesSpecifications
- Publisher
- Alibaba
- Family
- Qwen
- Parameters
- 235B
- Architecture
- Qwen3MoeForCausalLM
- Context Length
- 262,144 tokens
- Vocabulary Size
- 151,936
- Release Date
- 2025-08-17
- License
- Apache 2.0
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HuggingFace
How Much VRAM Does Qwen3 235B A22B Thinking 2507 Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| BF16 | 16.00 | 470.5 GB | 495.5 GB | 470.00 GB | Brain floating point 16 — preferred for training |
Which GPUs Can Run Qwen3 235B A22B Thinking 2507?
BF16 · 470.5 GBQwen3 235B A22B Thinking 2507 (BF16) requires 470.5 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 612+ GB is recommended. Using the full 262K context window can add up to 25.0 GB, bringing total usage to 495.5 GB. No single GPU has enough memory — multi-GPU or cluster setups are needed.
Which Devices Can Run Qwen3 235B A22B Thinking 2507?
BF16 · 470.5 GB2 devices with unified memory can run Qwen3 235B A22B Thinking 2507, including NVIDIA DGX H100.
Decent
— Enough memory, may be tightRelated Models
Derivatives (9)
Frequently Asked Questions
- How much VRAM does Qwen3 235B A22B Thinking 2507 need?
Qwen3 235B A22B Thinking 2507 requires 470.5 GB of VRAM at BF16. Full 262K context adds up to 25.0 GB (495.5 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 235B × 16 bits ÷ 8 = 470 GB
KV Cache + Overhead ≈ 0.5 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 25.5 GB (at full 262K context)
VRAM usage by quantization
BF16470.5 GBBF16 + full context495.5 GB- Can NVIDIA GeForce RTX 5090 run Qwen3 235B A22B Thinking 2507?
No — Qwen3 235B A22B Thinking 2507 requires at least 470.5 GB at BF16, which exceeds the NVIDIA GeForce RTX 5090's 32 GB of VRAM.
- Can I run Qwen3 235B A22B Thinking 2507 on a Mac?
Qwen3 235B A22B Thinking 2507 requires at least 470.5 GB at BF16, which exceeds the unified memory of most consumer Macs. You would need a Mac Studio or Mac Pro with a high-memory configuration.
- Can I run Qwen3 235B A22B Thinking 2507 locally?
Yes — Qwen3 235B A22B Thinking 2507 can run locally on consumer hardware. At BF16 quantization it needs 470.5 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- What's the download size of Qwen3 235B A22B Thinking 2507?
At BF16, the download is about 470.00 GB.