Qwen3 8B GGUF — Hardware Requirements & GPU Compatibility
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Qwen3 8B GGUF is the official GGUF-format release of Alibaba's 8-billion-parameter Qwen3 model. The GGUF format is optimized for llama.cpp and compatible inference engines, making this one of the easiest Qwen3 models to get running locally with tools like Ollama, LM Studio, or Jan. At 8 billion parameters, this model offers a solid middle ground in the Qwen3 lineup, delivering capable chat and general-purpose performance while remaining runnable on most consumer GPUs with 6 GB or more of VRAM. The GGUF packaging supports flexible quantization levels, letting users choose their own quality-versus-memory tradeoff.
74.3K downloads
161 likesSpecifications
- Publisher
- Alibaba
- Family
- Qwen
- Parameters
- 8B
- Release Date
- 2025-05-21
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does Qwen3 8B GGUF Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q4_K_M | 4.80 | 5.3 GB | — | 4.80 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_0 | 5.00 | 5.5 GB | — | 5.00 GB | 5-bit legacy quantization |
| Q5_K_M | 5.70 | 6.3 GB | — | 5.70 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 7.3 GB | — | 6.60 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 8.8 GB | — | 8.00 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run Qwen3 8B GGUF?
Q4_K_M · 5.3 GBQwen3 8B GGUF (Q4_K_M) requires 5.3 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 7+ 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 8B GGUF?
Q4_K_M · 5.3 GB33 devices with unified memory can run Qwen3 8B GGUF, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomRelated Models
Frequently Asked Questions
- How much VRAM does Qwen3 8B GGUF need?
Qwen3 8B GGUF requires 5.3 GB of VRAM at Q4_K_M, or 8.8 GB at Q8_0.
VRAM = Weights + KV Cache + Overhead
Weights = 8B × 4.8 bits ÷ 8 = 4.8 GB
KV Cache + Overhead ≈ 0.5 GB (at 2K context + ~0.3 GB framework)
VRAM usage by quantization
Q4_K_M5.3 GB- What's the best quantization for Qwen3 8B GGUF?
For Qwen3 8B GGUF, Q4_K_M (5.3 GB) offers the best balance of quality and VRAM usage. Q5_0 (5.5 GB) provides better quality if you have the VRAM.
VRAM requirement by quantization
Q4_K_M ★5.3 GB~89%Q5_05.5 GB~90%Q5_K_M6.3 GB~92%Q6_K7.3 GB~95%Q8_08.8 GB~99%★ Recommended — best balance of quality and VRAM usage.
- Can I run Qwen3 8B GGUF on a Mac?
Qwen3 8B GGUF requires at least 5.3 GB at Q4_K_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 8B GGUF locally?
Yes — Qwen3 8B GGUF can run locally on consumer hardware. At Q4_K_M quantization it needs 5.3 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Qwen3 8B GGUF?
At Q4_K_M, Qwen3 8B GGUF can reach ~552 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~124 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 ÷ 5.3 × 0.55 = ~552 tok/s
Estimated speed at Q4_K_M (5.3 GB)
AMD Instinct MI300X~552 tok/sNVIDIA GeForce RTX 4090~124 tok/sNVIDIA H100 SXM~413 tok/sAMD Instinct MI250X~341 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Qwen3 8B GGUF?
At Q4_K_M, the download is about 4.80 GB. The full-precision Q8_0 version is 8.00 GB.