Qwen 1 8B — Hardware Requirements & GPU Compatibility
Chat
Qwen-1.8B is Alibaba's first-generation 1.8-billion-parameter base language model, pretrained from scratch on over 2.2 trillion tokens of Chinese, English, multilingual, code, and math data, with the same roughly 150,000-token vocabulary used across the Qwen family. It is a raw pretrained model rather than a chat assistant; Alibaba's aligned Qwen-1.8B-Chat is built on top of it. Its main selling point is low-cost deployment: the card reports int4/int8 quantized versions needing under 2GB of memory for inference and as little as 6GB for fine-tuning, so it runs comfortably on almost any consumer GPU or even a laptop. Context length is 8,192 tokens. It is released under a custom Tongyi Qianwen Research License, free for academic research, with commercial use requiring direct contact with Alibaba. It was published in November 2023.
2.3K downloads
74 likesSpecifications
- Publisher
- Alibaba
- Family
- Qwen
- Parameters
- 1.8B
- Architecture
- QWenLMHeadModel
- Context Length
- 8,192 tokens
- Vocabulary Size
- 151,936
- Release Date
- 2023-11-30
Get Started
HuggingFace
How Much VRAM Does Qwen 1 8B Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 0.9 GB | — | 0.78 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 0.9 GB | — | 0.80 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 1.0 GB | — | 0.90 GB | 3-bit medium quantization |
| Q4_K_M | 4.80 | 1.2 GB | — | 1.10 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 1.4 GB | — | 1.31 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 1.7 GB | — | 1.52 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 2.0 GB | — | 1.84 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 Qwen 1 8B?
Q4_K_M · 1.2 GBQwen 1 8B (Q4_K_M) requires 1.2 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 2+ GB is recommended. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroomWhich Devices Can Run Qwen 1 8B?
Q4_K_M · 1.2 GB59 devices with unified memory can run Qwen 1 8B, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomWhere to Download Qwen 1 8B
Community quantizations of this model — GGUF for llama.cpp, Ollama, and LM Studio, plus AWQ/MLX variants where available.
Benchmarks
Benchmark details →Related Models
Frequently Asked Questions
- How much VRAM does Qwen 1 8B need?
Qwen 1 8B requires 1.2 GB of VRAM at Q4_K_M, or 4.0 GB at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 1.8B × 4.8 bits ÷ 8 = 1.1 GB
KV Cache + Overhead ≈ 0.1 GB (at 2K context + ~0.3 GB framework)
VRAM usage by quantization
Q4_K_M1.2 GB- What's the best quantization for Qwen 1 8B?
For Qwen 1 8B, Q4_K_M (1.2 GB) offers the best balance of quality and VRAM usage. Q5_K_S (1.4 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 0.9 GB.
VRAM requirement by quantization
Q2_K0.9 GBQ3_K_L1.0 GBQ4_K_M ★1.2 GBQ5_K_S1.4 GBQ5_K_M1.4 GBBF164.0 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Qwen 1 8B on a Mac?
Qwen 1 8B requires at least 0.9 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 Qwen 1 8B locally?
Yes — Qwen 1 8B can run locally on consumer hardware. At Q4_K_M quantization it needs 1.2 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Qwen 1 8B?
At Q4_K_M, Qwen 1 8B can reach ~3967 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~542 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 ÷ 1.2 × 0.65 = ~4298 tok/s
Estimated speed at Q4_K_M (1.2 GB)
~4298 tok/s~542 tok/s~4298 tok/s~3967 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Qwen 1 8B?
At Q4_K_M, the download is about 1.10 GB. The full-precision BF16 version is 3.67 GB. The smallest option (Q2_K) is 0.78 GB.
- Which GPUs can run Qwen 1 8B?
52 consumer GPUs can run Qwen 1 8B at Q4_K_M (1.2 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 Qwen 1 8B?
59 devices with unified memory can run Qwen 1 8B at Q4_K_M (1.2 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.