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Can I run Qwen 3 4B?

Qwen 3 4B by Alibaba needs around 6 GB of RAM at the recommended 4-bit quantization (2.4 GB download). Your hardware is checked below β€” instantly, nothing leaves your browser. Expect roughly ~126 tok/s on a NVIDIA RTX 3060 12GB.

Reading your hardware signals…

Real-world notes

Qwen 3 4B is the model to reach for when you want real reasoning out of something small. At a 4-bit quant it lands around 2.4 GB, so it slips onto almost anything: a 6 GB GPU has headroom, any Apple Silicon Mac swallows it whole, and even an older laptop with an integrated card can host it. The minimum you want for comfortable use is about 6 GB of RAM. If you are running on a CPU-only box or a Raspberry-class device, this is one of the few models that stays genuinely useful at this footprint rather than just technically loadable.

In daily use it feels quick. On an RTX 3060 it pushes roughly 126 tokens per second at 4-bit, and an M-series Max sits near 144, both well past reading speed, so replies stream instantly for chat and short reasoning chains. A 4090 hits around 353 if you have one. Pure CPU on DDR5 drops to about 21 tokens per second, slow but workable for background tasks. The context window is 32K, plenty for most work, but filling it pushes total memory to roughly 5.5 GB, so on a tight 6 GB machine keep your working context modest.

Against its own family, Qwen 3 4B is the obvious step up from Qwen 3 1.7B when the smaller model starts fumbling multi-step prompts, while staying far lighter than the 7-8B tier. Phi-4 Mini 3.8B is a reasonable alternative at a similar size and generally trades blows on chat, but Qwen 3 4B's standout trait is punching above its parameter count on reasoning tasks for how little it costs to run. It ships under Apache 2.0, so you can use it commercially and in production without license worries, which is rare at this quality-to-size ratio.

Specifications

Parameters4B
Context window32K tokens
ProviderAlibaba
LicenseApache 2.0
Released2025-04
Best forChat, Reasoning

Size by quantization

QuantizationBits/weightDownloadMin RAMQuality
Q2_K3.351.7 GB4 GBNoticeable loss
Q4_K_MRecommended4.852.4 GB6 GBRecommended
Q5_K_M5.652.8 GB6 GBHigh
Q8_08.54.3 GB8 GBNear-original
F16168.0 GB12 GBOriginal

Sizes are estimates from parameter count Γ— bits per weight; real GGUF builds vary slightly. Β· Data updated: 2026-06-11 Β· How we calculate these numbers β†’

Memory needed by context length

ContextKV cache (est.)Total memory (Q4)
4K tokens~0.4 GB~2.8 GB
8K tokens~0.8 GB~3.2 GB
32K tokens~3.1 GB~5.5 GB

The KV cache grows with context length β€” a model that fits at 4K can run out of memory at 32K. Estimates assume an FP16 cache with grouped-query attention; actual usage varies by runtime.

Estimated speed by hardware

HardwareBandwidth~Speed
NVIDIA RTX 3060 12GB360 GB/s~126 tok/s
NVIDIA RTX 4090 24GB1008 GB/s~353 tok/s
Apple M-series (base)100 GB/s~35 tok/s
Apple M-series Pro270 GB/s~95 tok/s
Apple M-series Max410 GB/s~144 tok/s
CPU only (dual-channel DDR5)60 GB/s~21 tok/s

Token generation is memory-bandwidth bound: tok/s β‰ˆ bandwidth Γ— 0.85 Γ· model size at Q4. Real-world numbers vary by runtime and context length.

Run it locally

The easiest path is Ollama β€” one command and you're chatting:

ollama run qwen3:4b

Frequently asked questions

Qwen 3 4B System Requirements β€” Can I Run It Locally?