Qwen3 235B A22B Instruct 2507 GGUF — Hardware Requirements & GPU Compatibility
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4.3K downloads
113 likesSpecifications
- Publisher
- Unsloth
- Family
- Qwen
- Parameters
- 235B
- License
- Apache 2.0
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HuggingFace
How Much VRAM Does Qwen3 235B A22B Instruct 2507 GGUF Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 109.9 GB | — | 99.88 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 113.1 GB | — | 102.81 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 126.0 GB | — | 114.56 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 129.3 GB | — | 117.50 GB | 4-bit legacy quantization |
| Q4_K_M | 4.80 | 155.1 GB | — | 141.00 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 184.2 GB | — | 167.44 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 213.3 GB | — | 193.88 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 258.5 GB | — | 235.00 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run Qwen3 235B A22B Instruct 2507 GGUF?
Q4_K_M · 155.1 GBQwen3 235B A22B Instruct 2507 GGUF (Q4_K_M) requires 155.1 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 202+ GB is recommended. No single GPU has enough memory — multi-GPU or cluster setups are needed.
Which Devices Can Run Qwen3 235B A22B Instruct 2507 GGUF?
Q4_K_M · 155.1 GB4 devices with unified memory can run Qwen3 235B A22B Instruct 2507 GGUF, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Pro M2 Ultra (192 GB).
Runs great
— Plenty of headroom
Decent
— Enough memory, may be tightRelated Models
Frequently Asked Questions
- How much VRAM does Qwen3 235B A22B Instruct 2507 GGUF need?
Qwen3 235B A22B Instruct 2507 GGUF requires 155.1 GB of VRAM at Q4_K_M, or 258.5 GB at Q8_0.
VRAM = Weights + KV Cache + Overhead
Weights = 235B × 4.8 bits ÷ 8 = 141 GB
KV Cache + Overhead ≈ 14.1 GB (at 2K context + ~0.3 GB framework)
VRAM usage by quantization
Q4_K_M155.1 GB- Can NVIDIA GeForce RTX 5090 run Qwen3 235B A22B Instruct 2507 GGUF?
No — Qwen3 235B A22B Instruct 2507 GGUF requires at least 109.9 GB at Q2_K, which exceeds the NVIDIA GeForce RTX 5090's 32 GB of VRAM.
- What's the best quantization for Qwen3 235B A22B Instruct 2507 GGUF?
For Qwen3 235B A22B Instruct 2507 GGUF, Q4_K_M (155.1 GB) offers the best balance of quality and VRAM usage. Q5_K_S (177.7 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 109.9 GB.
VRAM requirement by quantization
Q2_K109.9 GB~75%Q4_0129.3 GB~85%Q4_K_S145.4 GB~88%Q4_K_M ★155.1 GB~89%Q5_K_S177.7 GB~92%Q8_0258.5 GB~99%★ Recommended — best balance of quality and VRAM usage.
- Can I run Qwen3 235B A22B Instruct 2507 GGUF on a Mac?
Qwen3 235B A22B Instruct 2507 GGUF requires at least 109.9 GB at Q2_K, 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 Instruct 2507 GGUF locally?
Yes — Qwen3 235B A22B Instruct 2507 GGUF can run locally on consumer hardware. At Q4_K_M quantization it needs 155.1 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Qwen3 235B A22B Instruct 2507 GGUF?
At Q4_K_M, Qwen3 235B A22B Instruct 2507 GGUF can reach ~19 tok/s on AMD Instinct MI300X. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: AMD Instinct MI300X → 5300 ÷ 155.1 × 0.55 = ~19 tok/s
Estimated speed at Q4_K_M (155.1 GB)
AMD Instinct MI300X~19 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Qwen3 235B A22B Instruct 2507 GGUF?
At Q4_K_M, the download is about 141.00 GB. The full-precision Q8_0 version is 235.00 GB. The smallest option (Q2_K) is 99.88 GB.
- Which GPUs can run Qwen3 235B A22B Instruct 2507 GGUF?
No single consumer GPU has enough VRAM to run Qwen3 235B A22B Instruct 2507 GGUF at Q4_K_M (155.1 GB). Multi-GPU or professional hardware is required.
- Which devices can run Qwen3 235B A22B Instruct 2507 GGUF?
4 devices with unified memory can run Qwen3 235B A22B Instruct 2507 GGUF at Q4_K_M (155.1 GB), including Mac Pro M2 Ultra (192 GB), Mac Studio M2 Ultra (192 GB), 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.