Qwen1.5 4B — Hardware Requirements & GPU Compatibility
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Qwen1.5 4B is a 4.0B-parameter open language model from Alibaba in the Qwen family. It supports a context window of up to 32,768 tokens. At Q4_K_M it needs about 3.51 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 · 8K)Best on 24 GB (Q4_K_M · ~187 tok/s · 32K)Macs from 8 GB (Q4_K_M · ~20 tok/s)
Other
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Qwen1.5 4B
One recommended way. Every other method, OS and quant is in the step-by-step guide.
Recommended
vllm serve Qwen/Qwen1.5-4B --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 (2 GB headroom) · Q4_K_M
- Generation speed
- ~67 tok/s
- generation speed
- Cost per 1M output tokens
- $0.29
- per 1M output tokens
How Much VRAM Does Qwen1.5 4B Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 2.8 GB | 15.4 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 32K context · ~232 tok/s | |
| 3.1 GB | 15.6 GB | 3-bit medium — 3-bit medium quantization | Fits: 32K context · ~214 tok/s | |
| 3.5 GB | 16.1 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 32K context · ~187 tok/s | |
| 4.0 GB | 16.5 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 32K context · ~166 tok/s | |
| 4.4 GB | 17 GB | 6-bit, very good — 6-bit quantization, very good quality | Fits: 32K context · ~149 tok/s | |
| 5.1 GB | 17.7 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: 32K context · ~129 tok/s | |
| 9.0 GB | 21.6 GB | 16-bit · bfloat — Brain floating point 16 — preferred for training | Fits: 32K context · ~73 tok/s |
Selected Q4_K_M
4-bit medium quantization9.4GBat 16K context
- Weights
- 2.4 GB
- KV cache
- 6.7 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 14.6 GB free · ~187 tok/s
≥50 tok/s needs ≥270 GB/s · RTX 5090 ~332, RTX 3090 Ti ~187
Which Hardware Can Run Qwen1.5 4B?
Q4_K_M · 3.5 GBQwen1.5 4B (Q4_K_M) requires 3.5 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 5+ GB is recommended. Using the full 33K context window can add up to 12.6 GB, bringing total usage to 16.1 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 4B, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB), MacBook Air 13" M3 (8 GB).
12 GB
12 GB tier →fits · 2.6 GB free · up to 16K ctx
10 GPUs · 1 device family
RTX 3080 Ti~169Simulate output on RTX 3080 TiRTX 5070~124Simulate output on RTX 5070RTX 4070~93Simulate output on RTX 4070iPhone~15Simulate output on Apple iPhone 17 Pro20 GB
fits · 10.6 GB free · up to 32K ctx
1 GPU
RX 7900 XT~137Simulate output on RX 7900 XT32–40 GB
fits · 22.6 GB free · up to 32K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~332Simulate output on RTX 5090M5 Max36 GB~92Simulate output on Mac Studio M5 Max (36 GB)M532 GB~31Simulate output on MacBook Air 13" M5 (32 GB)Orin~38Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~25Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
Alternatives to Qwen1.5 4B
Qwen family · 28 models →Similar models
6| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| Granite Vision 4.1 4BIBM | — | 4 GB | ~228 | same size |
| MiniCPM V 4OpenBMB | — | 3.1 GB | ~236 | same size |
| Phi 4 Mini ReasoningMicrosoft | — | 4.8 GB | ~228 | same size |
| Phi 3.5 Vision InstructMicrosoft | — | 9.2 GB | ~183 | same size |
| Qwen3 4B Base Dapo V4ReliquaryForge | — | 4.2 GB | ~226 | same size |
| Nanbeige4.2 3BNanbeige | — | 3.5 GB | ~227 | same size |
Specifications
- Publisher
- Alibaba
- Family
- Qwen
- Parameters
- 4.0B
- Architecture
- Qwen2ForCausalLM
- Context Length
- 32,768 tokens
- Vocabulary Size
- 151,936
- Release Date
- 2024-01-22
- License
- Other
Frequently Asked Questions
How much VRAM does Qwen1.5 4B need?
Qwen1.5 4B requires 3.5 GB of VRAM at Q4_K_M, or 9.0 GB at BF16. Full 33K context adds up to 12.6 GB (16.1 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 4.0B × 4.8 bits ÷ 8 = 2.4 GB
KV Cache + Overhead ≈ 1.1 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 ≈ 13.7 GB (at full 33K context)
VRAM usage by quantization
What's the best quantization for Qwen1.5 4B?
For Qwen1.5 4B, Q4_K_M (3.5 GB) offers the best balance of quality and VRAM usage. Q5_K_M (4.0 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 2.8 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run Qwen1.5 4B on a Mac?
Yes — Mac Mini M2 (8 GB) and 145 other Macs can run Qwen1.5 4B. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 3.5 GB of usable unified memory (RAM minus macOS overhead).
Can I run Qwen1.5 4B locally?
Yes — Qwen1.5 4B can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Qwen1.5 4B?
At Q4_K_M, Qwen1.5 4B can reach ~1368 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~187 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 ÷ 3.5100 × 0.65 = ~1481 tok/s
Estimated speed at Q4_K_M (3.5 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 4B?
At Q4_K_M, the download is about 2.37 GB. The full-precision BF16 version is 7.90 GB. The smallest option (Q2_K) is 1.68 GB.
Which GPUs can run Qwen1.5 4B?
52 consumer GPUs can run Qwen1.5 4B 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 4B?
163 devices with unified memory can run Qwen1.5 4B at Q4_K_M (3.5 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.