Qwen3 8B — Hardware Requirements & GPU Compatibility
Chat
Qwen3 8B is an 8.2-billion parameter instruction-tuned model from Alibaba Cloud's Qwen 3 series. It is a general-purpose chat model that delivers strong performance across reasoning, multilingual understanding, and coding tasks while remaining efficient enough to run on consumer GPUs with 8GB or more of VRAM. Like other Qwen 3 models, it supports hybrid thinking mode for flexible reasoning depth. The model benefits from the improved pretraining data and training methodology of the Qwen 3 generation, offering notable quality gains over Qwen 2.5 at the same parameter count. It is widely supported by inference frameworks including llama.cpp, vLLM, and Ollama. Released under the Apache 2.0 license.
10.9M downloads
1.1K likesSpecifications
- Publisher
- Alibaba
- Family
- Qwen 3
- Parameters
- 8.2B
- Architecture
- Qwen3ForCausalLM
- Context Length
- 40,960 tokens
- Vocabulary Size
- 151,936
- Release Date
- 2025-04-27
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does Qwen3 8B Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 4.1 GB | 9.8 GB | 3.48 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 4.2 GB | 9.9 GB | 3.58 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 4.6 GB | 10.3 GB | 3.99 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 4.7 GB | 10.4 GB | 4.10 GB | 4-bit legacy quantization |
| Q4_K_M | 4.80 | 5.5 GB | 11.3 GB | 4.91 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 6.4 GB | 12.2 GB | 5.84 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 7.4 GB | 13.1 GB | 6.76 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 8.8 GB | 14.5 GB | 8.19 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run Qwen3 8B?
Q4_K_M · 5.5 GBQwen3 8B (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 41K context window can add up to 5.7 GB, bringing total usage to 11.3 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?
Q4_K_M · 5.5 GB58 devices with unified memory can run Qwen3 8B, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, MacBook Air 13" M3 (8 GB).
Runs great
— Plenty of headroomWhere to Download Qwen3 8B
Community quantizations of this model — GGUF for llama.cpp, Ollama, and LM Studio, plus AWQ/MLX variants where available.
Related Models
Frequently Asked Questions
- How much VRAM does Qwen3 8B need?
Qwen3 8B requires 5.5 GB of VRAM at Q4_K_M, or 17.0 GB at BF16. Full 41K context adds up to 5.7 GB (11.3 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 ≈ 6.4 GB (at full 41K context)
VRAM usage by quantization
Q4_K_M5.5 GBQ4_K_M + full context11.3 GB- What's the best quantization for Qwen3 8B?
For Qwen3 8B, Q4_K_M (5.5 GB) offers the best balance of quality and VRAM usage. Q4_K_L (5.6 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 2.9 GB.
VRAM requirement by quantization
IQ2_XXS2.9 GBIQ3_M4.3 GBIQ4_NL5.2 GBQ4_K_M ★5.5 GBQ5_K_S6.2 GBBF1617.0 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Qwen3 8B on a Mac?
Qwen3 8B requires at least 2.9 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 8B locally?
Yes — Qwen3 8B 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?
At Q4_K_M, Qwen3 8B 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?
At Q4_K_M, the download is about 4.91 GB. The full-precision BF16 version is 16.38 GB. The smallest option (IQ2_XXS) is 2.25 GB.
- Which GPUs can run Qwen3 8B?
50 consumer GPUs can run Qwen3 8B 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?
59 devices with unified memory can run Qwen3 8B 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.