Qwen3 1.7B GGUF — Hardware Requirements & GPU Compatibility
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A GGUF-quantized version of Alibaba's Qwen3 1.7B, repackaged by MaziyarPanahi. At 1.7 billion parameters, this lightweight model can run on virtually any modern hardware and offers solid general-purpose text generation for its size class. Qwen3 brings meaningful improvements in reasoning and instruction following over its predecessors. The GGUF format makes it easy to load in popular inference tools like llama.cpp and Ollama, with multiple quantization levels typically available to let you choose your preferred balance of quality and speed. A good option for users who want a fast, responsive small model for simple tasks and experimentation.
176.3K downloads
7 likesSpecifications
- Publisher
- MaziyarPanahi
- Family
- Qwen
- Parameters
- 1.7B
- Release Date
- 2025-04-28
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HuggingFace
How Much VRAM Does Qwen3 1.7B GGUF Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 0.8 GB | — | 0.72 GB | 2-bit quantization with K-quant improvements |
| Q3_K_M | 3.90 | 0.9 GB | — | 0.83 GB | 3-bit medium quantization |
| Q3_K_L | 4.10 | 1.0 GB | — | 0.87 GB | 3-bit large quantization |
| Q4_K_M | 4.80 | 1.1 GB | — | 1.02 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 1.3 GB | — | 1.21 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 1.5 GB | — | 1.40 GB | 6-bit quantization, very good quality |
Which GPUs Can Run Qwen3 1.7B GGUF?
Q4_K_M · 1.1 GBQwen3 1.7B GGUF (Q4_K_M) requires 1.1 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 2+ 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 1.7B GGUF?
Q4_K_M · 1.1 GB33 devices with unified memory can run Qwen3 1.7B GGUF, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomRelated Models
Frequently Asked Questions
- How much VRAM does Qwen3 1.7B GGUF need?
Qwen3 1.7B GGUF requires 1.1 GB of VRAM at Q4_K_M, or 1.5 GB at Q6_K.
VRAM = Weights + KV Cache + Overhead
Weights = 1.7B × 4.8 bits ÷ 8 = 1 GB
KV Cache + Overhead ≈ 0.1 GB (at 2K context + ~0.3 GB framework)
VRAM usage by quantization
Q4_K_M1.1 GB- What's the best quantization for Qwen3 1.7B GGUF?
For Qwen3 1.7B GGUF, Q4_K_M (1.1 GB) offers the best balance of quality and VRAM usage. Q5_K_M (1.3 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 0.8 GB.
VRAM requirement by quantization
Q2_K0.8 GB~75%Q3_K_M0.9 GB~83%Q3_K_L1.0 GB~86%Q4_K_M ★1.1 GB~89%Q5_K_M1.3 GB~92%Q6_K1.5 GB~95%★ Recommended — best balance of quality and VRAM usage.
- Can I run Qwen3 1.7B GGUF on a Mac?
Qwen3 1.7B GGUF requires at least 0.8 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 1.7B GGUF locally?
Yes — Qwen3 1.7B GGUF can run locally on consumer hardware. At Q4_K_M quantization it needs 1.1 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Qwen3 1.7B GGUF?
At Q4_K_M, Qwen3 1.7B GGUF can reach ~2603 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~585 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 ÷ 1.1 × 0.55 = ~2603 tok/s
Estimated speed at Q4_K_M (1.1 GB)
AMD Instinct MI300X~2603 tok/sNVIDIA GeForce RTX 4090~585 tok/sNVIDIA H100 SXM~1945 tok/sAMD Instinct MI250X~1609 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Qwen3 1.7B GGUF?
At Q4_K_M, the download is about 1.02 GB. The full-precision Q6_K version is 1.40 GB. The smallest option (Q2_K) is 0.72 GB.