Qwen3 4B GGUF — Hardware Requirements & GPU Compatibility
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This is a GGUF-quantized version of Alibaba's Qwen3 4B, repackaged by Unsloth. Qwen3 4B is a compact yet capable model from the latest generation of the Qwen series, offering strong multilingual performance and solid reasoning abilities in a small footprint. At 4 billion parameters in GGUF format, this model is lightweight enough to run comfortably on most consumer hardware, including laptops and systems with modest GPUs. Unsloth's conversion ensures compatibility with llama.cpp and its ecosystem of tools, making it an accessible option for users who want a responsive local model for everyday tasks without heavy resource demands.
80.2K downloads
201 likesSpecifications
- Publisher
- Unsloth
- Family
- Qwen
- Parameters
- 4B
- Architecture
- Qwen3ForCausalLM
- Context Length
- 40,960 tokens
- Vocabulary Size
- 151,936
- Release Date
- 2025-06-08
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does Qwen3 4B GGUF Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| IQ2_XXS | 2.20 | 1.6 GB | 5.2 GB | 1.10 GB | Importance-weighted 2-bit, extreme compression — significant quality loss |
| IQ2_M | 2.70 | 1.8 GB | 5.4 GB | 1.35 GB | Importance-weighted 2-bit, medium |
| IQ3_XXS | 3.10 | 2.0 GB | 5.6 GB | 1.55 GB | Importance-weighted 3-bit |
| Q2_K | 3.40 | 2.2 GB | 5.8 GB | 1.70 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 2.2 GB | 5.8 GB | 1.75 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 2.4 GB | 6.0 GB | 1.95 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 2.5 GB | 6.1 GB | 2.00 GB | 4-bit legacy quantization |
| IQ4_XS | 4.30 | 2.6 GB | 6.2 GB | 2.15 GB | Importance-weighted 4-bit, compact |
| Q4_1 | 4.50 | 2.7 GB | 6.3 GB | 2.25 GB | 4-bit legacy quantization with offset |
| Q4_K_S | 4.50 | 2.7 GB | 6.3 GB | 2.25 GB | 4-bit small quantization |
| IQ4_NL | 4.50 | 2.7 GB | 6.3 GB | 2.25 GB | Importance-weighted 4-bit, non-linear |
| Q4_K_M | 4.80 | 2.9 GB | 6.5 GB | 2.40 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_S | 5.50 | 3.2 GB | 6.8 GB | 2.75 GB | 5-bit small quantization |
| Q5_K_M | 5.70 | 3.3 GB | 6.9 GB | 2.85 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 3.8 GB | 7.4 GB | 3.30 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 4.5 GB | 8.1 GB | 4.00 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run Qwen3 4B GGUF?
Q4_K_M · 2.9 GBQwen3 4B GGUF (Q4_K_M) requires 2.9 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 4+ GB is recommended. Using the full 41K context window can add up to 3.6 GB, bringing total usage to 6.5 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 4B GGUF?
Q4_K_M · 2.9 GB33 devices with unified memory can run Qwen3 4B GGUF, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomRelated Models
Frequently Asked Questions
- How much VRAM does Qwen3 4B GGUF need?
Qwen3 4B GGUF requires 2.9 GB of VRAM at Q4_K_M, or 4.5 GB at Q8_0. Full 41K context adds up to 3.6 GB (6.5 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 4B × 4.8 bits ÷ 8 = 2.4 GB
KV Cache + Overhead ≈ 0.5 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 4.1 GB (at full 41K context)
VRAM usage by quantization
Q4_K_M2.9 GBQ4_K_M + full context6.5 GB- What's the best quantization for Qwen3 4B GGUF?
For Qwen3 4B GGUF, Q4_K_M (2.9 GB) offers the best balance of quality and VRAM usage. Q5_K_S (3.2 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 1.6 GB.
VRAM requirement by quantization
IQ2_XXS1.6 GB~53%Q3_K_S2.2 GB~77%Q4_12.7 GB~88%Q4_K_M ★2.9 GB~89%Q5_K_S3.2 GB~92%Q8_04.5 GB~99%★ Recommended — best balance of quality and VRAM usage.
- Can I run Qwen3 4B GGUF on a Mac?
Qwen3 4B GGUF requires at least 1.6 GB at IQ2_XXS, 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 4B GGUF locally?
Yes — Qwen3 4B GGUF can run locally on consumer hardware. At Q4_K_M quantization it needs 2.9 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Qwen3 4B GGUF?
At Q4_K_M, Qwen3 4B GGUF can reach ~1009 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~227 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 ÷ 2.9 × 0.55 = ~1009 tok/s
Estimated speed at Q4_K_M (2.9 GB)
AMD Instinct MI300X~1009 tok/sNVIDIA GeForce RTX 4090~227 tok/sNVIDIA H100 SXM~754 tok/sAMD Instinct MI250X~624 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Qwen3 4B GGUF?
At Q4_K_M, the download is about 2.40 GB. The full-precision Q8_0 version is 4.00 GB. The smallest option (IQ2_XXS) is 1.10 GB.