Qwen3 8B Base — Hardware Requirements & GPU Compatibility
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Qwen3 8B Base is an 8.2-billion parameter pretrained foundation model from Alibaba Cloud's Qwen 3 series. As a base model, it is not instruction-tuned and is intended for fine-tuning, research, and as a starting point for custom downstream applications. It was trained on a large multilingual corpus with improved data quality and training methodology compared to the Qwen 2.5 generation. The model runs efficiently on consumer GPUs with 8GB or more of VRAM and serves as the foundation for the Qwen3 8B instruction-tuned variant and community fine-tunes. It is a strong choice for practitioners building specialized models through further training. Released under the Apache 2.0 license.
453.7K downloads
107 likesSpecifications
- Publisher
- Alibaba
- Family
- Qwen 3
- Parameters
- 8.2B
- Architecture
- Qwen3ForCausalLM
- Context Length
- 32,768 tokens
- Vocabulary Size
- 151,936
- Release Date
- 2025-04-28
- License
- Apache 2.0
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HuggingFace
How Much VRAM Does Qwen3 8B Base Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 4.1 GB | 8.6 GB | 3.48 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 4.2 GB | 8.7 GB | 3.58 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 4.6 GB | 9.1 GB | 3.99 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 4.7 GB | 9.2 GB | 4.10 GB | 4-bit legacy quantization |
| Q4_K_M | 4.80 | 5.5 GB | 10.1 GB | 4.91 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 6.4 GB | 11.0 GB | 5.84 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 7.4 GB | 11.9 GB | 6.76 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 8.8 GB | 13.3 GB | 8.19 GB | 8-bit quantization, near-lossless |
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 8B Base?
Q4_K_M · 5.5 GBQwen3 8B Base (Q4_K_M) requires 5.5 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 8+ GB is recommended. Using the full 33K context window can add up to 4.5 GB, bringing total usage to 10.1 GB. 50 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 3070 Ti.
Runs great
— Plenty of headroom
Decent
— Enough VRAM, may be tightWhich Devices Can Run Qwen3 8B Base?
Q4_K_M · 5.5 GB58 devices with unified memory can run Qwen3 8B Base, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, MacBook Air 13" M3 (8 GB).
Runs great
— Plenty of headroomWhere to Download Qwen3 8B Base
Community quantizations of this model — GGUF for llama.cpp, Ollama, and LM Studio, plus AWQ/MLX variants where available.
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Frequently Asked Questions
- How much VRAM does Qwen3 8B Base need?
Qwen3 8B Base requires 5.5 GB of VRAM at Q4_K_M, or 17.0 GB at BF16. Full 33K context adds up to 4.5 GB (10.1 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 8.2B × 4.8 bits ÷ 8 = 4.9 GB
KV Cache + Overhead ≈ 0.6 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 5.2 GB (at full 33K context)
VRAM usage by quantization
Q4_K_M5.5 GBQ4_K_M + full context10.1 GB- What's the best quantization for Qwen3 8B Base?
For Qwen3 8B Base, Q4_K_M (5.5 GB) offers the best balance of quality and VRAM usage. Q5_0 (5.7 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 4.1 GB.
VRAM requirement by quantization
Q2_K4.1 GBQ3_K_L4.8 GBQ4_K_M ★5.5 GBQ5_05.7 GBQ5_K_M6.4 GBBF1617.0 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Qwen3 8B Base on a Mac?
Qwen3 8B Base requires at least 4.1 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 8B Base locally?
Yes — Qwen3 8B Base can run locally on consumer hardware. At Q4_K_M quantization it needs 5.5 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Qwen3 8B Base?
At Q4_K_M, Qwen3 8B Base can reach ~797 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~119 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 ÷ 5.5 × 0.65 = ~942 tok/s
Estimated speed at Q4_K_M (5.5 GB)
~942 tok/s~119 tok/s~942 tok/s~797 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 Base?
At Q4_K_M, the download is about 4.91 GB. The full-precision BF16 version is 16.38 GB. The smallest option (Q2_K) is 3.48 GB.
- Which GPUs can run Qwen3 8B Base?
50 consumer GPUs can run Qwen3 8B Base at Q4_K_M (5.5 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT, AMD Radeon RX 7600. 39 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Qwen3 8B Base?
59 devices with unified memory can run Qwen3 8B Base at Q4_K_M (5.5 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.