Can You Run Qwen3-4B? VRAM Requirements
Qwen3-4B has 4B total parameters. Running it at Q4_K_M (GGUF) with 16K context takes roughly 6.0 GB of memory (2.3 GB weights + 2.3 GB KV cache + runtime overhead). The minimum viable hardware is about RTX 3060 12GB. All numbers are planning estimates, verified 2026-07-27.
VRAM by quantization (16K context)
| Quantization | Weights | Total needed | Minimum hardware |
|---|---|---|---|
| FP16 / BF16 | 7.5 GB | 11.2 GB | RTX 4060 Ti 16GB |
| FP8 | 3.7 GB | 7.5 GB | RTX 3060 12GB |
| Q8_0 (GGUF) | 3.9 GB | 7.7 GB | RTX 3060 12GB |
| Q6_K (GGUF) | 3.1 GB | 6.8 GB | RTX 3060 12GB |
| Q5_K_M (GGUF) | 2.6 GB | 6.4 GB | RTX 3060 12GB |
| MXFP4 | 2.0 GB | 5.7 GB | RTX 3060 12GB |
| Q4_K_M (GGUF)recommended | 2.3 GB | 6.0 GB | RTX 3060 12GB |
| Q3_K_M (GGUF) | 1.8 GB | 5.5 GB | RTX 3060 12GB |
| Q2_K (GGUF) | 1.3 GB | 5.1 GB | RTX 3060 12GB |
How context length changes memory (Q4_K_M (GGUF))
| Context | KV cache | Total needed |
|---|---|---|
| 4K | 0.6 GB | 4.3 GB |
| 16K | 2.3 GB | 6.0 GB |
| 64K | 9.0 GB | 12.8 GB |
| 128K | 18.0 GB | 21.8 GB |
KV cache grows linearly with context; FP8 KV cache halves it again (try it in the calculator below).
Customize the estimate
LLM VRAM Calculator — Can I Run It?
Estimate how much GPU memory (VRAM) you need to run open-weight LLMs like Kimi K3, DeepSeek R1, Qwen3, or Llama locally. Pick a model, quantization, and context length — the calculator adds up weights, KV cache, and runtime overhead, then shows which GPUs or Macs can fit it. Runs entirely in your browser: no upload, no signup.
4B total params · max 128K context
Recommended balance · ~0.61 bytes/param
Estimated memory needed
21.8 GB
Will it fit?
Assumes ~92% of device memory is usable. Multi-GPU counts are for tensor/pipeline parallel serving (vLLM, SGLang); Apple Silicon uses unified memory via llama.cpp or MLX.
FAQ
How much VRAM does Qwen3-4B need?
About 6.0 GB at the recommended Q4_K_M (GGUF) quantization with 16K context: 2.3 GB for weights, 2.3 GB for KV cache, plus runtime overhead. Longer context grows the KV cache.
What is the minimum hardware for Qwen3-4B?
Roughly RTX 3060 12GB, assuming 92% of device memory is usable. Lower-end setups can try more aggressive quantization (Q3/Q2) at a noticeable quality cost.
Can I run Qwen3-4B on a Mac?
Yes. At Q4_K_M (GGUF) with 16K context it needs about 6.0 GB, which fits the unified memory of a Mac M3 Ultra 512GB via llama.cpp or MLX.
Which quantization should I use for Qwen3-4B?
Q4_K_M is the recommended balance of size and quality; with headroom, Q6_K or Q8_0 reduce quality loss further.