Qwen3 4B Thinking 2507 — Hardware Requirements & GPU Compatibility
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Qwen3 4B Thinking 2507 is the reasoning-optimized variant of Alibaba's compact 4-billion-parameter Qwen3 model, released in the July 2025 update cycle. Despite its small size, this thinking variant is tuned to produce chain-of-thought reasoning and step-by-step problem solving, making it a surprisingly capable lightweight reasoner. This model is ideal for users who want basic reasoning and analytical capabilities on very modest hardware. It can run on most consumer GPUs and even some CPU-only setups when quantized, providing an accessible entry point for experimenting with reasoning-style models without any significant hardware investment.
527.0K downloads
598 likesSpecifications
- Publisher
- Alibaba
- Family
- Qwen 3
- Parameters
- 4.0B
- Architecture
- Qwen3ForCausalLM
- Context Length
- 262,144 tokens
- Vocabulary Size
- 151,936
- Release Date
- 2025-08-05
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does Qwen3 4B Thinking 2507 Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 2.2 GB | 26.2 GB | 1.71 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 2.3 GB | 26.2 GB | 1.76 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 2.5 GB | 26.4 GB | 1.96 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 2.5 GB | 26.5 GB | 2.01 GB | 4-bit legacy quantization |
| Q4_K_M | 4.80 | 2.9 GB | 26.9 GB | 2.41 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 3.4 GB | 27.3 GB | 2.87 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 3.8 GB | 27.8 GB | 3.32 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 4.5 GB | 28.5 GB | 4.02 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run Qwen3 4B Thinking 2507?
Q4_K_M · 2.9 GBQwen3 4B Thinking 2507 (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 262K context window can add up to 24.0 GB, bringing total usage to 26.9 GB. 50 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroomWhich Devices Can Run Qwen3 4B Thinking 2507?
Q4_K_M · 2.9 GB59 devices with unified memory can run Qwen3 4B Thinking 2507, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomWhere to Download Qwen3 4B Thinking 2507
Community quantizations of this model — GGUF for llama.cpp, Ollama, and LM Studio, plus AWQ/MLX variants where available.
Related Models
Frequently Asked Questions
- How much VRAM does Qwen3 4B Thinking 2507 need?
Qwen3 4B Thinking 2507 requires 2.9 GB of VRAM at Q4_K_M, or 8.5 GB at BF16. Full 262K context adds up to 24.0 GB (26.9 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 4.0B × 4.8 bits ÷ 8 = 2.4 GB
KV Cache + Overhead ≈ 0.5 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 24.5 GB (at full 262K context)
VRAM usage by quantization
Q4_K_M2.9 GBQ4_K_M + full context26.9 GB- What's the best quantization for Qwen3 4B Thinking 2507?
For Qwen3 4B Thinking 2507, Q4_K_M (2.9 GB) offers the best balance of quality and VRAM usage. Q4_K_L (3.0 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 GBQ2_K2.2 GBQ3_K_L2.5 GBQ4_K_M ★2.9 GBQ5_03.0 GBBF168.5 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Qwen3 4B Thinking 2507 on a Mac?
Qwen3 4B Thinking 2507 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 Thinking 2507 locally?
Yes — Qwen3 4B Thinking 2507 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 Thinking 2507?
At Q4_K_M, Qwen3 4B Thinking 2507 can reach ~1517 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~226 tok/s. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: NVIDIA B200 → 8000 ÷ 2.9 × 0.65 = ~1793 tok/s
Estimated speed at Q4_K_M (2.9 GB)
~1793 tok/s~226 tok/s~1793 tok/s~1517 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 Thinking 2507?
At Q4_K_M, the download is about 2.41 GB. The full-precision BF16 version is 8.04 GB. The smallest option (IQ2_XXS) is 1.11 GB.
- Which GPUs can run Qwen3 4B Thinking 2507?
50 consumer GPUs can run Qwen3 4B Thinking 2507 at Q4_K_M (2.9 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT. 50 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Qwen3 4B Thinking 2507?
59 devices with unified memory can run Qwen3 4B Thinking 2507 at Q4_K_M (2.9 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Apple iPhone 17 Pro, Asus ROG Flow Z13 (2025, Ryzen AI Max+ 395, 128 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.