Qwen2.5 0.5B Instruct ONNX — Hardware Requirements & GPU Compatibility
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Qwen2.5 0.5B Instruct ONNX is a 0.5B-parameter open language model from JasonYANG170 in the Qwen 2.5 family. It supports a context window of up to 32,768 tokens. At Q4_K_M it needs about 0.63 GB of VRAM — see which GPUs and Macs can run it below.
Runs great on a 24 GB GPU
Runs from 8 GB (Q4_K_M · 32K)Best on 24 GB (Q4_K_M · ~1040 tok/s · 32K)Macs from 8 GB (Q4_K_M · ~114 tok/s)
Apache 2.0
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Qwen2.5 0.5B Instruct ONNX
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
vllm serve JasonYANG170/Qwen2.5-0.5B-Instruct-ONNX --max-model-len 16384vLLM repo with its own quantisation
Download: Weights on Hugging Face
Ways to runOther ways
each opens step-by-stepRun in cloud
Runs on RTX 3080 Ti (12 GB) (11 GB headroom) · Q4_K_M
- Generation speed
- ~941 tok/s
- generation speed
- Cost per 1M output tokens
- $0.036
- per 1M output tokens
How Much VRAM Does Qwen2.5 0.5B Instruct ONNX 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: 32K context · ~1213 tok/s | |
| 0.6 GB | 1 GB | 3-bit medium — 3-bit medium quantization | Fits: 32K context · ~1150 tok/s | |
| 0.6 GB | 1 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 32K context · ~1040 tok/s | |
| 0.7 GB | 1.1 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 32K context · ~964 tok/s | |
| 0.7 GB | 1.1 GB | 6-bit, very good — 6-bit quantization, very good quality | Fits: 32K context · ~885 tok/s | |
| 0.8 GB | 1.2 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: 32K context · ~789 tok/s | |
| 1.3 GB | 1.7 GB | 16-bit · bfloat — Brain floating point 16 — preferred for training | Fits: 32K context · ~493 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 · ~1040 tok/s
≥50 tok/s needs ≥48 GB/s · RTX 5090 ~1849, RTX 3090 Ti ~1040
Which Hardware Can Run Qwen2.5 0.5B Instruct ONNX?
Q4_K_M · 0.6 GBQwen2.5 0.5B Instruct ONNX (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 33K context window can add up to 0.4 GB, bringing total usage to 1 GB. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti. 163 devices with unified memory can run Qwen2.5 0.5B Instruct ONNX, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB).
≤ 11 GB
≤ 11 GB tier →fits · 7.2 GB free · up to 32K ctx
16 GPUs · 1 Mac class · 2 device families
RTX 3080~784Simulate output on RTX 3080RTX 2080 Ti~636Simulate output on RTX 2080 TiRTX 3070 Ti~628Simulate output on RTX 3070 TiM3*8 GB~114Simulate output on iMac 24" M3 (8 GB)Orin Nano~105Simulate output on NVIDIA Jetson Orin Nano 8GB (Super)iPhone~76Simulate output on iPhone 1720 GB
fits · 19.2 GB free · up to 32K ctx
1 GPU
RX 7900 XT~762Simulate output on RX 7900 XT32–40 GB
fits · 31.2 GB free · up to 32K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~1849Simulate output on RTX 5090M5 Max36 GB~511Simulate output on Mac Studio M5 Max (36 GB)M532 GB~171Simulate output on MacBook Air 13" M5 (32 GB)Orin~211Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~139Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
Alternatives to Qwen2.5 0.5B Instruct ONNX
Qwen 2.5 family · 44 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 |
| Qwen2 0.5B InstructAlibaba | — | 0.8 GB | ~1057 | same size |
Specifications
- Publisher
- JasonYANG170
- Family
- Qwen 2.5
- Parameters
- 0.5B
- Architecture
- Qwen2ForCausalLM
- Context Length
- 32,768 tokens
- Vocabulary Size
- 151,936
- Release Date
- 2026-09-14
- License
- Apache 2.0
Frequently Asked Questions
How much VRAM does Qwen2.5 0.5B Instruct ONNX need?
Qwen2.5 0.5B Instruct ONNX requires 0.6 GB of VRAM at Q4_K_M, or 1.3 GB at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 0.5B × 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 ≈ 0.7 GB (at full 33K context)
VRAM usage by quantization
What's the best quantization for Qwen2.5 0.5B Instruct ONNX?
For Qwen2.5 0.5B Instruct ONNX, Q4_K_M (0.6 GB) offers the best balance of quality and VRAM usage. Q5_K_M (0.7 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 0.5 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run Qwen2.5 0.5B Instruct ONNX on a Mac?
Yes — MacBook Air 13" M3 (8 GB) and 145 other Macs can run Qwen2.5 0.5B Instruct ONNX. 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 Qwen2.5 0.5B Instruct ONNX locally?
Yes — Qwen2.5 0.5B Instruct ONNX can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Qwen2.5 0.5B Instruct ONNX?
At Q4_K_M, Qwen2.5 0.5B Instruct ONNX can reach ~7619 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~1040 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 ÷ 0.63 × 0.65 = ~8254 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 Qwen2.5 0.5B Instruct ONNX?
At Q4_K_M, the download is about 0.30 GB. The full-precision BF16 version is 1.00 GB. The smallest option (Q2_K) is 0.21 GB.
Which GPUs can run Qwen2.5 0.5B Instruct ONNX?
52 consumer GPUs can run Qwen2.5 0.5B Instruct ONNX 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 Qwen2.5 0.5B Instruct ONNX?
163 devices with unified memory can run Qwen2.5 0.5B Instruct ONNX 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.