Qwen3 4B — Hardware Requirements & GPU Compatibility
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Qwen3 4B is a compact 4-billion parameter instruction-tuned model from Alibaba Cloud's Qwen 3 family. It is designed for efficient local inference on consumer hardware, supporting chat and general assistant tasks while fitting comfortably on GPUs with 6GB or more of VRAM in quantized formats. The model supports hybrid thinking mode, allowing it to balance reasoning depth and response speed. Despite its small footprint, Qwen3 4B delivers quality competitive with larger models from previous generations, making it a practical choice for lightweight local deployments and resource-constrained environments. Released under the Apache 2.0 license.
6.2M downloads
570 likesSpecifications
- Publisher
- Alibaba
- Family
- Qwen
- Parameters
- 4B
- Architecture
- Qwen3ForCausalLM
- Context Length
- 40,960 tokens
- Vocabulary Size
- 151,936
- Release Date
- 2025-07-26
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does Qwen3 4B 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 |
| IQ4_NL | 4.50 | 2.7 GB | 6.3 GB | 2.25 GB | Importance-weighted 4-bit, non-linear |
| Q4_K_S | 4.50 | 2.7 GB | 6.3 GB | 2.25 GB | 4-bit small quantization |
| Q4_1 | 4.50 | 2.7 GB | 6.3 GB | 2.25 GB | 4-bit legacy quantization with offset |
| Q4_K_M | 4.80 | 2.9 GB | 6.5 GB | 2.40 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_0 | 5.00 | 3.0 GB | 6.6 GB | 2.50 GB | 5-bit legacy quantization |
| 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?
Q4_K_M · 2.9 GBQwen3 4B (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?
Q4_K_M · 2.9 GB33 devices with unified memory can run Qwen3 4B, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomRelated Models
Derivatives (7)
Frequently Asked Questions
- How much VRAM does Qwen3 4B need?
Qwen3 4B 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?
For Qwen3 4B, Q4_K_M (2.9 GB) offers the best balance of quality and VRAM usage. Q5_0 (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 GB~53%Q3_K_S2.2 GB~77%IQ4_NL2.7 GB~88%Q4_K_M ★2.9 GB~89%Q5_03.0 GB~90%Q8_04.5 GB~99%★ Recommended — best balance of quality and VRAM usage.
- Can I run Qwen3 4B on a Mac?
Qwen3 4B 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 locally?
Yes — Qwen3 4B 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?
At Q4_K_M, Qwen3 4B 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?
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.