Qwen3 4B Instruct 2507 GGUF — Hardware Requirements & GPU Compatibility
Chat
A GGUF-quantized version of Alibaba's Qwen3 4B Instruct 2507 (July 2025 release), repackaged by MaziyarPanahi. At 4 billion parameters, this model hits a sweet spot for users who want noticeably better output quality than sub-2B models while still running efficiently on modest hardware, including many integrated GPUs and older discrete cards. The 2507 revision reflects updated training and tuning from Alibaba, and the GGUF format ensures broad compatibility with llama.cpp, Ollama, LM Studio, and other popular local inference tools. A well-rounded small model for chat, writing assistance, and light reasoning tasks.
118.4K downloads
2 likesSpecifications
- Publisher
- MaziyarPanahi
- Family
- Qwen
- Parameters
- 4B
- Release Date
- 2025-08-27
Get Started
HuggingFace
How Much VRAM Does Qwen3 4B Instruct 2507 GGUF Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| IQ2_M | 2.70 | 1.5 GB | — | 1.35 GB | Importance-weighted 2-bit, medium |
| IQ3_XXS | 3.10 | 1.7 GB | — | 1.55 GB | Importance-weighted 3-bit |
| IQ3_XS | 3.30 | 1.8 GB | — | 1.65 GB | Importance-weighted 3-bit, extra small |
| Q2_K | 3.40 | 1.9 GB | — | 1.70 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 1.9 GB | — | 1.75 GB | 3-bit small quantization |
| IQ3_M | 3.60 | 2.0 GB | — | 1.80 GB | Importance-weighted 3-bit, medium |
| Q3_K_M | 3.90 | 2.1 GB | — | 1.95 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 2.2 GB | — | 2.00 GB | 4-bit legacy quantization |
| Q3_K_L | 4.10 | 2.3 GB | — | 2.05 GB | 3-bit large quantization |
| IQ4_XS | 4.30 | 2.4 GB | — | 2.15 GB | Importance-weighted 4-bit, compact |
| Q4_1 | 4.50 | 2.5 GB | — | 2.25 GB | 4-bit legacy quantization with offset |
| Q4_K_S | 4.50 | 2.5 GB | — | 2.25 GB | 4-bit small quantization |
| IQ4_NL | 4.50 | 2.5 GB | — | 2.25 GB | Importance-weighted 4-bit, non-linear |
| Q4_K_M | 4.80 | 2.6 GB | — | 2.40 GB | 4-bit medium quantization — most popular sweet spot |
| Q4_K_L | 4.90 | 2.7 GB | — | 2.45 GB | 4-bit large quantization |
| Q5_K_S | 5.50 | 3.0 GB | — | 2.75 GB | 5-bit small quantization |
| Q5_K_M | 5.70 | 3.1 GB | — | 2.85 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q5_K_L | 5.80 | 3.2 GB | — | 2.90 GB | 5-bit large quantization |
| Q6_K | 6.60 | 3.6 GB | — | 3.30 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 4.4 GB | — | 4.00 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run Qwen3 4B Instruct 2507 GGUF?
Q4_K_M · 2.6 GBQwen3 4B Instruct 2507 GGUF (Q4_K_M) requires 2.6 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 4+ GB is recommended. 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 Instruct 2507 GGUF?
Q4_K_M · 2.6 GB33 devices with unified memory can run Qwen3 4B Instruct 2507 GGUF, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomRelated Models
Frequently Asked Questions
- How much VRAM does Qwen3 4B Instruct 2507 GGUF need?
Qwen3 4B Instruct 2507 GGUF requires 2.6 GB of VRAM at Q4_K_M, or 4.4 GB at Q8_0.
VRAM = Weights + KV Cache + Overhead
Weights = 4B × 4.8 bits ÷ 8 = 2.4 GB
KV Cache + Overhead ≈ 0.2 GB (at 2K context + ~0.3 GB framework)
VRAM usage by quantization
Q4_K_M2.6 GB- What's the best quantization for Qwen3 4B Instruct 2507 GGUF?
For Qwen3 4B Instruct 2507 GGUF, Q4_K_M (2.6 GB) offers the best balance of quality and VRAM usage. Q4_K_L (2.7 GB) provides better quality if you have the VRAM. The smallest option is IQ2_M at 1.5 GB.
VRAM requirement by quantization
IQ2_M1.5 GB~62%IQ3_M2.0 GB~78%Q4_12.5 GB~88%Q4_K_M ★2.6 GB~89%Q4_K_L2.7 GB~90%Q8_04.4 GB~99%★ Recommended — best balance of quality and VRAM usage.
- Can I run Qwen3 4B Instruct 2507 GGUF on a Mac?
Qwen3 4B Instruct 2507 GGUF requires at least 1.5 GB at IQ2_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 4B Instruct 2507 GGUF locally?
Yes — Qwen3 4B Instruct 2507 GGUF can run locally on consumer hardware. At Q4_K_M quantization it needs 2.6 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Qwen3 4B Instruct 2507 GGUF?
At Q4_K_M, Qwen3 4B Instruct 2507 GGUF can reach ~1104 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~248 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 ÷ 2.6 × 0.55 = ~1104 tok/s
Estimated speed at Q4_K_M (2.6 GB)
AMD Instinct MI300X~1104 tok/sNVIDIA GeForce RTX 4090~248 tok/sNVIDIA H100 SXM~825 tok/sAMD Instinct MI250X~683 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 Instruct 2507 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_M) is 1.35 GB.