Qwen3 4B Domino B16 — Hardware Requirements & GPU Compatibility
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Qwen3 4B Domino B16 is a 588M-parameter open language model from Huang2020 in the Qwen 3 family. It supports a context window of up to 40,960 tokens. At Q4_K_M it needs about 0.68 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 · 40K)Best on 24 GB (Q4_K_M · ~964 tok/s · 40K)Macs from 8 GB (Q4_K_M · ~105 tok/s)
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Qwen3 4B Domino B16
One recommended way. Every other method, OS and quant is in the step-by-step guide.
Recommended
vllm serve Huang2020/Qwen3-4B-Domino-b16 --max-model-len 16384vLLM repo with its own quantisation
Download: Weights on Hugging Face
Other ways
each opens step-by-stepRun in cloud
Fits on RTX 3060 12GB (11 GB headroom) · Q4_K_M
- Generation speed
- ~344 tok/s
- generation speed
- Cost per 1M output tokens
- $0.067
- per 1M output tokens
How Much VRAM Does Qwen3 4B Domino B16 Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 0.6 GB | 1 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 40K context · ~1130 tok/s | |
| 0.6 GB | 1 GB | 3-bit medium — 3-bit medium quantization | Fits: 40K context · ~1074 tok/s | |
| 0.7 GB | 1.1 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 40K context · ~964 tok/s | |
| 0.8 GB | 1.1 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 40K context · ~874 tok/s | |
| 0.8 GB | 1.2 GB | 6-bit, very good — 6-bit quantization, very good quality | Fits: 40K context · ~809 tok/s | |
| 0.9 GB | 1.3 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: 40K context · ~720 tok/s | |
| 1.5 GB | 1.9 GB | 16-bit · bfloat — Brain floating point 16 — preferred for training | Fits: 40K context · ~437 tok/s |
Selected Q4_K_M
4-bit medium quantization0.9GBat 16K context
- Weights
- 0.4 GB
- KV cache
- 0.2 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 23.1 GB free · ~964 tok/s
≥50 tok/s needs ≥52 GB/s · RTX 5090 ~1713, RTX 3090 Ti ~964
Which Hardware Can Run Qwen3 4B Domino B16?
Q4_K_M · 0.7 GBQwen3 4B Domino B16 (Q4_K_M) requires 0.7 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 41K context window can add up to 0.5 GB, bringing total usage to 1.2 GB. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti. 163 devices with unified memory can run Qwen3 4B Domino B16, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB).
≤ 11 GB
≤ 11 GB tier →fits · 7.1 GB free · up to 40K ctx
16 GPUs · 1 Mac class · 2 device families
RTX 3080~727Simulate output on RTX 3080RTX 2080 Ti~589Simulate output on RTX 2080 TiRTX 3070 Ti~582Simulate output on RTX 3070 TiM3*8 GB~105Simulate output on iMac 24" M3 (8 GB)Orin Nano~98Simulate output on NVIDIA Jetson Orin Nano 8GB (Super)iPhone~70Simulate output on iPhone 1720 GB
fits · 19.1 GB free · up to 40K ctx
1 GPU
RX 7900 XT~706Simulate output on RX 7900 XT32–40 GB
fits · 31.1 GB free · up to 40K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~1713Simulate output on RTX 5090M5 Max36 GB~474Simulate output on Mac Studio M5 Max (36 GB)M532 GB~158Simulate output on MacBook Air 13" M5 (32 GB)Orin~196Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~129Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
Alternatives to Qwen3 4B Domino B16
Qwen 3 family · 77 models →Similar models
6| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| D1 Omni 600MLiquid AI | — | 1.2 GB | ~910 | same size |
| T5gemma B B Ul2 ITGoogle | — | 2.5 GB | ~504 | same size |
| Qwen1.5 0.5B ChatAlibaba | — | 2.3 GB | ~753 | same size |
| Bloomz 560MBigScience | — | 0.4 GB | ~1771 | same size |
| Bloom 560MBigScience | — | 1.6 GB | ~1771 | same size |
| Xglm 564MMeta | — | 0.4 GB | ~1771 | same size |
Specifications
- Publisher
- Huang2020
- Family
- Qwen 3
- Parameters
- 588M
- Architecture
- DFlashDraftModel
- Context Length
- 40,960 tokens
- Vocabulary Size
- 151,936
- Release Date
- 2026-05-27
Frequently Asked Questions
How much VRAM does Qwen3 4B Domino B16 need?
Qwen3 4B Domino B16 requires 0.7 GB of VRAM at Q4_K_M, or 1.5 GB at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 588M × 4.8 bits ÷ 8 = 0.4 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 ≈ 0.8 GB (at full 41K context)
VRAM usage by quantization
What's the best quantization for Qwen3 4B Domino B16?
For Qwen3 4B Domino B16, Q4_K_M (0.7 GB) offers the best balance of quality and VRAM usage. Q5_K_M (0.8 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 0.6 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run Qwen3 4B Domino B16 on a Mac?
Yes — MacBook Air 13" M3 (8 GB) and 145 other Macs can run Qwen3 4B Domino B16. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 0.7 GB of usable unified memory (RAM minus macOS overhead).
Can I run Qwen3 4B Domino B16 locally?
Yes — Qwen3 4B Domino B16 can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Qwen3 4B Domino B16?
At Q4_K_M, Qwen3 4B Domino B16 can reach ~7059 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~964 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 ÷ 0.68 × 0.65 = ~7647 tok/s
Estimated speed at Q4_K_M (0.7 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of Qwen3 4B Domino B16?
At Q4_K_M, the download is about 0.35 GB. The full-precision BF16 version is 1.18 GB. The smallest option (Q2_K) is 0.25 GB.
Which GPUs can run Qwen3 4B Domino B16?
52 consumer GPUs can run Qwen3 4B Domino B16 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 Qwen3 4B Domino B16?
163 devices with unified memory can run Qwen3 4B Domino B16 at Q4_K_M (0.7 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.