Qwen3 32B MLX 4bit — Hardware Requirements & GPU Compatibility
Chat
24.2K downloads
5 likesSpecifications
- Publisher
- LM Studio Community
- Family
- Qwen
- Parameters
- 5.1B
- Architecture
- Qwen3ForCausalLM
- Context Length
- 40,960 tokens
- Vocabulary Size
- 151,936
- Release Date
- 2025-04-28
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does Qwen3 32B MLX 4bit Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q4_K_M | 4.80 | 3.7 GB | 10.1 GB | 3.07 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_0 | 5.00 | 3.8 GB | 10.2 GB | 3.20 GB | 5-bit legacy quantization |
| Q5_K_M | 5.70 | 4.3 GB | 10.7 GB | 3.65 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 4.9 GB | 11.2 GB | 4.22 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 5.8 GB | 12.1 GB | 5.12 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run Qwen3 32B MLX 4bit?
Q4_K_M · 3.7 GBQwen3 32B MLX 4bit (Q4_K_M) requires 3.7 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 5+ GB is recommended. Using the full 41K context window can add up to 6.4 GB, bringing total usage to 10.1 GB. 35 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroomWhich Devices Can Run Qwen3 32B MLX 4bit?
Q4_K_M · 3.7 GB33 devices with unified memory can run Qwen3 32B MLX 4bit, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomRelated Models
Frequently Asked Questions
- How much VRAM does Qwen3 32B MLX 4bit need?
Qwen3 32B MLX 4bit requires 3.7 GB of VRAM at Q4_K_M, or 5.8 GB at Q8_0. Full 41K context adds up to 6.4 GB (10.1 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 5.1B × 4.8 bits ÷ 8 = 3.1 GB
KV Cache + Overhead ≈ 0.6 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 7 GB (at full 41K context)
VRAM usage by quantization
Q4_K_M3.7 GBQ4_K_M + full context10.1 GB- What's the best quantization for Qwen3 32B MLX 4bit?
For Qwen3 32B MLX 4bit, Q4_K_M (3.7 GB) offers the best balance of quality and VRAM usage. Q5_0 (3.8 GB) provides better quality if you have the VRAM.
VRAM requirement by quantization
Q4_K_M ★3.7 GB~89%Q5_03.8 GB~90%Q5_K_M4.3 GB~92%Q6_K4.9 GB~95%Q8_05.8 GB~99%★ Recommended — best balance of quality and VRAM usage.
- Can I run Qwen3 32B MLX 4bit on a Mac?
Qwen3 32B MLX 4bit requires at least 3.7 GB at Q4_K_M, 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 32B MLX 4bit locally?
Yes — Qwen3 32B MLX 4bit can run locally on consumer hardware. At Q4_K_M quantization it needs 3.7 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Qwen3 32B MLX 4bit?
At Q4_K_M, Qwen3 32B MLX 4bit can reach ~786 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~177 tok/s. 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 ÷ 3.7 × 0.55 = ~786 tok/s
Estimated speed at Q4_K_M (3.7 GB)
AMD Instinct MI300X~786 tok/sNVIDIA GeForce RTX 4090~177 tok/sNVIDIA H100 SXM~587 tok/sAMD Instinct MI250X~486 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Qwen3 32B MLX 4bit?
At Q4_K_M, the download is about 3.07 GB. The full-precision Q8_0 version is 5.12 GB.
- Which GPUs can run Qwen3 32B MLX 4bit?
35 consumer GPUs can run Qwen3 32B MLX 4bit at Q4_K_M (3.7 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT. 35 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Qwen3 32B MLX 4bit?
33 devices with unified memory can run Qwen3 32B MLX 4bit at Q4_K_M (3.7 GB), including Mac Mini M4 (16 GB), Mac Mini M4 (32 GB), Mac Mini M4 Pro (24 GB), Mac Mini M4 Pro (48 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.