Qwen1.5 0.5B Chat — Hardware Requirements & GPU Compatibility
Chat
Qwen1.5 0.5B Chat is an early-generation small language model from Alibaba's Qwen series with just 620 million parameters. As one of the smallest models in the Qwen family, it was designed to demonstrate that useful conversational ability is possible even at sub-billion parameter scales. This model runs easily on virtually any hardware including CPUs, older GPUs, and even mobile devices. While its capabilities are limited compared to larger Qwen models, it remains a useful option for embedded applications, rapid prototyping, or situations where minimal resource consumption is the top priority.
Runs great on a 24 GB GPU
Runs from 8 GB (Q4_K_M · 32K)Best on 24 GB (Q4_K_M · ~753 tok/s · 32K)Macs from 8 GB (Q4_K_M · ~82 tok/s)
Other
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Qwen1.5 0.5B Chat
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/second-state/Qwen1.5-0.5B-Chat-GGUF:Q4_K_MQ4_K_M fits fully on the RTX 4090 · 21.7 GB free · 16K context
Download: Qwen1.5-0.5B-Chat-Q4_K_M.gguf0.41 GBsecond-state
Ways to runOther ways
each opens step-by-stepRun in cloud
Runs on RTX 3080 Ti (12 GB) (9 GB headroom) · Q4_K_M
- Generation speed
- ~682 tok/s
- generation speed
- Cost per 1M output tokens
- $0.050
- per 1M output tokens
How Much VRAM Does Qwen1.5 0.5B Chat Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 0.8 GB | 3.8 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 32K context · ~862 tok/s | |
| 0.8 GB | 3.8 GB | 3-bit small — 3-bit small quantization | Fits: 32K context · ~851 tok/s | |
| 0.8 GB | 3.8 GB | 3-bit medium — 3-bit medium quantization | Fits: 32K context · ~819 tok/s | |
| 0.8 GB | 3.8 GB | 4-bit · legacy — 4-bit legacy quantization | Fits: 32K context · ~809 tok/s | |
| 0.9 GB | 3.9 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 32K context · ~753 tok/s | |
| 0.9 GB | 4 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 32K context · ~697 tok/s | |
| 1.0 GB | 4 GB | 6-bit, very good — 6-bit quantization, very good quality | Fits: 32K context · ~649 tok/s | |
| 1.1 GB | 4.1 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: 32K context · ~585 tok/s |
Selected Q4_K_M
4-bit medium quantization2.3GBat 16K context
- Weights
- 0.4 GB
- KV cache
- 1.6 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 21.7 GB free · ~753 tok/s
≥50 tok/s needs ≥67 GB/s · RTX 5090 ~1339, RTX 3090 Ti ~753
Which Hardware Can Run Qwen1.5 0.5B Chat?
Q4_K_M · 0.9 GBQwen1.5 0.5B Chat (Q4_K_M) requires 0.9 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 2+ GB is recommended. Using the full 33K context window can add up to 3.0 GB, bringing total usage to 3.9 GB. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti. 163 devices with unified memory can run Qwen1.5 0.5B Chat, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB).
≤ 11 GB
≤ 11 GB tier →fits · 5.7 GB free · up to 32K ctx
16 GPUs · 1 Mac class · 2 device families
RTX 3080~568Simulate output on RTX 3080RTX 2080 Ti~460Simulate output on RTX 2080 TiRTX 3070 Ti~455Simulate output on RTX 3070 TiM3*8 GB~82Simulate output on iMac 24" M3 (8 GB)Orin Nano~76Simulate output on NVIDIA Jetson Orin Nano 8GB (Super)iPhone~55Simulate output on iPhone 1720 GB
fits · 17.7 GB free · up to 32K ctx
1 GPU
RX 7900 XT~552Simulate output on RX 7900 XT32–40 GB
fits · 29.7 GB free · up to 32K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~1339Simulate output on RTX 5090M5 Max36 GB~370Simulate output on Mac Studio M5 Max (36 GB)M532 GB~124Simulate output on MacBook Air 13" M5 (32 GB)Orin~153Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~101Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
Alternatives to Qwen1.5 0.5B Chat
Qwen family · 28 models →Similar models
4| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| D1 Omni 600MLiquid AI | — | 1.2 GB | ~910 | same size |
| Malaysian TTS 0.6B V1mesolitica | — | 1.6 GB | ~819 | same size |
| T5gemma B B Ul2 ITGoogle | — | 2.5 GB | ~504 | same size |
| Qwen3 0.6B BaseAlibaba | — | 1.6 GB | ~840 | same size |
Fine-tunes & variants(1)
Specifications
- Publisher
- Alibaba
- Family
- Qwen
- Parameters
- 620M
- Architecture
- Qwen2ForCausalLM
- Context Length
- 32,768 tokens
- Vocabulary Size
- 151,936
- Release Date
- 2024-01-31
- License
- Other
Frequently Asked Questions
How much VRAM does Qwen1.5 0.5B Chat need?
Qwen1.5 0.5B Chat requires 0.9 GB of VRAM at Q4_K_M, or 1.7 GB at BF16. Full 33K context adds up to 3.0 GB (3.9 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 620M × 4.8 bits ÷ 8 = 0.4 GB
KV Cache + Overhead ≈ 0.5 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 ≈ 3.5 GB (at full 33K context)
VRAM usage by quantization
What's the best quantization for Qwen1.5 0.5B Chat?
For Qwen1.5 0.5B Chat, Q4_K_M (0.9 GB) offers the best balance of quality and VRAM usage. Q5_0 (0.9 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 0.7 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run Qwen1.5 0.5B Chat on a Mac?
Yes — MacBook Air 13" M3 (8 GB) and 145 other Macs can run Qwen1.5 0.5B Chat. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 0.9 GB of usable unified memory (RAM minus macOS overhead).
Can I run Qwen1.5 0.5B Chat locally?
Yes — Qwen1.5 0.5B Chat can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Qwen1.5 0.5B Chat?
At Q4_K_M, Qwen1.5 0.5B Chat can reach ~5517 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~753 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 ÷ 0.87 × 0.65 = ~5977 tok/s
Estimated speed at Q4_K_M (0.9 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of Qwen1.5 0.5B Chat?
At Q4_K_M, the download is about 0.37 GB. The full-precision BF16 version is 1.24 GB. The smallest option (IQ2_XXS) is 0.17 GB.
Which GPUs can run Qwen1.5 0.5B Chat?
52 consumer GPUs can run Qwen1.5 0.5B Chat 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 Qwen1.5 0.5B Chat?
163 devices with unified memory can run Qwen1.5 0.5B Chat at Q4_K_M (0.9 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.