Qwen3 235B A22B Thinking 2507 GPTQ Int4 Int8Mix — Hardware Requirements & GPU Compatibility
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30 downloads
4 likesSpecifications
- Publisher
- QuantTrio
- Family
- Qwen
- Parameters
- 252.5B
- Architecture
- Qwen3MoeForCausalLM
- Context Length
- 262,144 tokens
- Vocabulary Size
- 151,936
- Release Date
- 2025-09-05
- License
- Apache 2.0
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How Much VRAM Does Qwen3 235B A22B Thinking 2507 GPTQ Int4 Int8Mix Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| FP16 | 16.00 | 505.5 GB | 530.5 GB | 505.01 GB | Full half-precision — baseline for inference |
Which GPUs Can Run Qwen3 235B A22B Thinking 2507 GPTQ Int4 Int8Mix?
FP16 · 505.5 GBQwen3 235B A22B Thinking 2507 GPTQ Int4 Int8Mix (FP16) requires 505.5 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 658+ GB is recommended. Using the full 262K context window can add up to 25.0 GB, bringing total usage to 530.5 GB. No single GPU has enough memory — multi-GPU or cluster setups are needed.
Which Devices Can Run Qwen3 235B A22B Thinking 2507 GPTQ Int4 Int8Mix?
FP16 · 505.5 GB2 devices with unified memory can run Qwen3 235B A22B Thinking 2507 GPTQ Int4 Int8Mix, including NVIDIA DGX H100.
Decent
— Enough memory, may be tightRelated Models
Frequently Asked Questions
- How much VRAM does Qwen3 235B A22B Thinking 2507 GPTQ Int4 Int8Mix need?
Qwen3 235B A22B Thinking 2507 GPTQ Int4 Int8Mix requires 505.5 GB of VRAM at FP16. Full 262K context adds up to 25.0 GB (530.5 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 252.5B × 16 bits ÷ 8 = 505 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
FP16505.5 GBFP16 + full context530.5 GB- Can NVIDIA GeForce RTX 5090 run Qwen3 235B A22B Thinking 2507 GPTQ Int4 Int8Mix?
No — Qwen3 235B A22B Thinking 2507 GPTQ Int4 Int8Mix requires at least 505.5 GB at FP16, which exceeds the NVIDIA GeForce RTX 5090's 32 GB of VRAM.
- Can I run Qwen3 235B A22B Thinking 2507 GPTQ Int4 Int8Mix on a Mac?
Qwen3 235B A22B Thinking 2507 GPTQ Int4 Int8Mix requires at least 505.5 GB at FP16, 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 GPTQ Int4 Int8Mix locally?
Yes — Qwen3 235B A22B Thinking 2507 GPTQ Int4 Int8Mix can run locally on consumer hardware. At FP16 quantization it needs 505.5 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- What's the download size of Qwen3 235B A22B Thinking 2507 GPTQ Int4 Int8Mix?
At FP16, the download is about 505.01 GB.
- Which GPUs can run Qwen3 235B A22B Thinking 2507 GPTQ Int4 Int8Mix?
No single consumer GPU has enough VRAM to run Qwen3 235B A22B Thinking 2507 GPTQ Int4 Int8Mix at FP16 (505.5 GB). Multi-GPU or professional hardware is required.
- Which devices can run Qwen3 235B A22B Thinking 2507 GPTQ Int4 Int8Mix?
2 devices with unified memory can run Qwen3 235B A22B Thinking 2507 GPTQ Int4 Int8Mix at FP16 (505.5 GB), including NVIDIA DGX A100 640GB, NVIDIA DGX H100. Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.