Qwen3 4B NWC — Hardware Requirements & GPU Compatibility
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Qwen3 4B NWC is a 5.5B-parameter open language model from Parda21 in the Qwen 3 family. It supports a context window of up to 40,960 tokens. At Q4_K_M it needs about 3.81 GB of VRAM — see which GPUs and Macs can run it below.
211 downloads
2 likesSpecifications
- Publisher
- Parda21
- Family
- Qwen 3
- Parameters
- 5.5B
- Architecture
- Qwen3ForCausalLM
- Context Length
- 40,960 tokens
- Vocabulary Size
- 151,936
- Release Date
- 2026-09-19
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does Qwen3 4B NWC Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_Kest. | 3.40 | 2.8 GB | 6.4 GB | 2.35 GB | 2-bit quantization with K-quant improvements |
| Q3_K_Mest. | 3.90 | 3.2 GB | 6.8 GB | 2.70 GB | 3-bit medium quantization |
| Q4_K_Mest. | 4.80 | 3.8 GB | 7.4 GB | 3.32 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_Mest. | 5.70 | 4.4 GB | 8.0 GB | 3.95 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_Kest. | 6.60 | 5.1 GB | 8.7 GB | 4.57 GB | 6-bit quantization, very good quality |
| Q8_0est. | 8.00 | 6.0 GB | 9.6 GB | 5.54 GB | 8-bit quantization, near-lossless |
| BF16est. | 16.00 | 11.6 GB | 15.2 GB | 11.08 GB | Brain floating point 16 — preferred for training |
est.= calculated VRAM estimate; no published GGUF file found for that quantization yet. Other rows are verified against real community uploads.
Which GPUs Can Run Qwen3 4B NWC?
Q4_K_M · 3.8 GBQwen3 4B NWC (Q4_K_M) requires 3.8 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 3.6 GB, bringing total usage to 7.4 GB. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroomWhich Devices Can Run Qwen3 4B NWC?
Q4_K_M · 3.8 GB59 devices with unified memory can run Qwen3 4B NWC, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, iPhone 17.
Runs great
— Plenty of headroom
Decent
— Enough memory, may be tightRelated Models
Frequently Asked Questions
- How much VRAM does Qwen3 4B NWC need?
Qwen3 4B NWC requires 3.8 GB of VRAM at Q4_K_M, or 11.6 GB at BF16. Full 41K context adds up to 3.6 GB (7.4 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 5.5B × 4.8 bits ÷ 8 = 3.3 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_M3.8 GBQ4_K_M + full context7.4 GB- What's the best quantization for Qwen3 4B NWC?
For Qwen3 4B NWC, Q4_K_M (3.8 GB) offers the best balance of quality and VRAM usage. Q5_K_M (4.4 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 2.8 GB.
VRAM requirement by quantization
Q2_K2.8 GBQ4_K_M ★3.8 GBQ5_K_M4.4 GBQ6_K5.1 GBQ8_06.0 GBBF1611.6 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Qwen3 4B NWC on a Mac?
Qwen3 4B NWC requires at least 2.8 GB at Q2_K, 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 NWC locally?
Yes — Qwen3 4B NWC can run locally on consumer hardware. At Q4_K_M quantization it needs 3.8 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Qwen3 4B NWC?
At Q4_K_M, Qwen3 4B NWC can reach ~1260 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~172 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 ÷ 3.8 × 0.65 = ~1365 tok/s
Estimated speed at Q4_K_M (3.8 GB)
~1365 tok/s~172 tok/s~1365 tok/s~1260 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 NWC?
At Q4_K_M, the download is about 3.32 GB. The full-precision BF16 version is 11.08 GB. The smallest option (Q2_K) is 2.35 GB.
- Which GPUs can run Qwen3 4B NWC?
52 consumer GPUs can run Qwen3 4B NWC at Q4_K_M (3.8 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT. 52 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Qwen3 4B NWC?
59 devices with unified memory can run Qwen3 4B NWC at Q4_K_M (3.8 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.