Qwen2 0.5B — Hardware Requirements & GPU Compatibility
Chat
Qwen2-0.5B is Alibaba's roughly 494-million-parameter dense base language model, the smallest member of the Qwen2 family that scales up to 72B and includes one Mixture-of-Experts variant. This is a pretrained base model, not an instruction-tuned chat model; Alibaba explicitly recommends applying supervised fine-tuning, RLHF, or further pretraining before using it for open-ended generation. Given its size, it runs easily on nearly any GPU and even on CPUs. Its configuration specifies a context window of up to 131,072 tokens. It is released under the Apache 2.0 license, permitting unrestricted commercial and research use, and was published in May 2024. It was superseded within the same year by Qwen2.5-0.5B, part of Alibaba's fast release cadence for its small-model line.
Runs great on a 24 GB GPU
Runs from 8 GB (Q4_K_M · 128K)Best on 24 GB (Q4_K_M · ~1057 tok/s · 128K)Macs from 8 GB (Q4_K_M · ~116 tok/s)
Apache 2.0
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Qwen2 0.5B
One recommended way. Every other method, OS and quant is in the step-by-step guide.Each way opens a step-by-step guide for your OS and quant.
Recommended
ollama run hf.co/mradermacher/Qwen2-0.5B-GGUF:Q4_K_MQ4_K_M fits fully on the RTX 4090 · 23.2 GB free · 16K context
Download: Qwen2-0.5B.Q4_K_M.gguf0.40 GBmradermacher
Ways to runOther ways
each opens step-by-stepRun in cloud
Your own private copy · Q4_K_M · 24 GB GPU
- The easiest way to try this model
- Running in a few minutes, easy setup
- Chat right here or connect any app
$5+ bonus for new users
Runs on RTX 3060 12GB (11 GB headroom) · Q4_K_M
- Generation speed
- ~377 tok/s
- generation speed
- Cost per 1M output tokens
- $0.044
- per 1M output tokens
How Much VRAM Does Qwen2 0.5B Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 0.5 GB | 0.9 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 128K context · ~1213 tok/s | |
| 0.5 GB | 0.9 GB | 3-bit small — 3-bit small quantization | Fits: 128K context · ~1213 tok/s | |
| 0.6 GB | 1 GB | 4-bit · legacy — 4-bit legacy quantization | Fits: 128K context · ~1150 tok/s | |
| 0.6 GB | 1 GB | 3-bit medium — 3-bit medium quantization | Fits: 128K context · ~1150 tok/s | |
| 0.6 GB | 1 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 128K context · ~1057 tok/s | |
| 0.7 GB | 1.1 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 128K context · ~964 tok/s | |
| 0.7 GB | 1.1 GB | 6-bit, very good — 6-bit quantization, very good quality | Fits: 128K context · ~898 tok/s | |
| 0.8 GB | 1.2 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: 128K context · ~799 tok/s |
Selected Q4_K_M
4-bit medium quantization0.8GBat 16K context
- Weights
- 0.3 GB
- KV cache
- 0.2 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 23.2 GB free · ~1057 tok/s
≥50 tok/s needs ≥48 GB/s · RTX 5090 ~1879, RTX 3090 Ti ~1057
Which Hardware Can Run Qwen2 0.5B?
Q4_K_M · 0.6 GBQwen2 0.5B (Q4_K_M) requires 0.6 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 1+ GB is recommended. Using the full 131K context window can add up to 1.6 GB, bringing total usage to 2.2 GB. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti. 163 devices with unified memory can run Qwen2 0.5B, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB).
≤ 11 GB
≤ 11 GB tier →fits · 7.2 GB free · up to 128K ctx
16 GPUs · 1 Mac class · 2 device families
RTX 3080~797Simulate output on RTX 3080RTX 2080 Ti~646Simulate output on RTX 2080 TiRTX 3070 Ti~638Simulate output on RTX 3070 TiM3*8 GB~116Simulate output on iMac 24" M3 (8 GB)Orin Nano~107Simulate output on NVIDIA Jetson Orin Nano 8GB (Super)iPhone~77Simulate output on iPhone 1720 GB
fits · 19.2 GB free · up to 128K ctx
1 GPU
RX 7900 XT~774Simulate output on RX 7900 XT32–40 GB
fits · 31.2 GB free · up to 128K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~1879Simulate output on RTX 5090M5 Max36 GB~519Simulate output on Mac Studio M5 Max (36 GB)M532 GB~173Simulate output on MacBook Air 13" M5 (32 GB)Orin~215Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~142Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
How good is Qwen2 0.5B?
llmrun Score blends public benchmark boards into one number; 100 = the average open model.
Not enough data yet for an llmrun Score.
A Score needs results on enough independent boards. How the Composite Score is built
Benchmarks
This is the single board showing results for this model; badges show who ran the test.
- MMLU-Pro
TIGER-LabIndependent
Independent15.0%
#137/146 open
Alternatives to Qwen2 0.5B
Qwen 2 family · 13 models →Similar models
6| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| SmolVLM2 500M Video InstructHugging Face | — | 0.9 GB | ~950 | same size |
| SmolVLM 500M InstructHugging Face | — | 0.9 GB | ~950 | same size |
| Falcon H1 0.5B BaseTII UAE | — | 1.2 GB | ~950 | same size |
| TIPO 500M FtKBlueLeaf | — | 0.8 GB | ~809 | same size |
| H2o Danube3 500M Chath2oai | — | 1 GB | ~923 | same size |
| Pythia 410MEleutherAI | — | 0.3 GB | ~1986 | same size |
Fine-tunes & variants(1)
Specifications
- Publisher
- Alibaba
- Family
- Qwen 2
- Parameters
- 494M
- Architecture
- Qwen2ForCausalLM
- Context Length
- 131,072 tokens
- Vocabulary Size
- 151,936
- Release Date
- 2024-05-31
- License
- Apache 2.0
Frequently Asked Questions
How much VRAM does Qwen2 0.5B need?
Qwen2 0.5B requires 0.6 GB of VRAM at Q4_K_M, or 1.3 GB at BF16. Full 131K context adds up to 1.6 GB (2.2 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 494M × 4.8 bits ÷ 8 = 0.3 GB
KV Cache + Overhead ≈ 0.3 GB (at 2K context + ~0.3 GB framework)
Fit ratings and hardware model lists check this model with room for a 16K-token context, which needs a little more memory.
KV Cache + Overhead ≈ 1.9 GB (at full 131K context)
VRAM usage by quantization
What's the best quantization for Qwen2 0.5B?
For Qwen2 0.5B, Q4_K_M (0.6 GB) offers the best balance of quality and VRAM usage. Q5_0 (0.6 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 0.5 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run Qwen2 0.5B on a Mac?
Yes — MacBook Air 13" M3 (8 GB) and 145 other Macs can run Qwen2 0.5B. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 0.6 GB of usable unified memory (RAM minus macOS overhead).
Can I run Qwen2 0.5B locally?
Yes — Qwen2 0.5B can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Qwen2 0.5B?
At Q4_K_M, Qwen2 0.5B can reach ~7742 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~1057 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 ÷ 0.62 × 0.65 = ~8387 tok/s
Estimated speed at Q4_K_M (0.6 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of Qwen2 0.5B?
At Q4_K_M, the download is about 0.30 GB. The full-precision BF16 version is 0.99 GB. The smallest option (IQ2_XXS) is 0.14 GB.
Which GPUs can run Qwen2 0.5B?
52 consumer GPUs can run Qwen2 0.5B at Q4_K_M. 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 Qwen2 0.5B?
163 devices with unified memory can run Qwen2 0.5B at Q4_K_M (0.6 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Beelink GTR9 Pro (Ryzen AI Max+ 395, 128 GB), Framework Desktop (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.