Can I Run Qwen3-Coder on RTX 4060 Laptop (8 GB VRAM, 16 GB RAM)?
Written by Jakub Rusinowski · Last updated September 29, 2026
Yes, but it is tight
Yes, but it is tight — Qwen3-Coder 8B at Q5_K_M needs about 7.6 GB of the 8 GB usable on RTX 4060 Laptop (8 GB VRAM, 16 GB RAM), leaving only ~0.4 GB before the runtime starts swapping. Expect ~28.8 tok/s (estimated), with room for about 8,192 tokens of context.
Confidence: low · Recommended quantization: Q5_K_M · Estimated speed: ~28.8 tok/s
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RTX 4060 Laptop (8 GB VRAM, 16 GB RAM) — what it gives a model
| Usable memory for models | 8 GB |
| Memory bandwidth | 256 GB/s |
| Form factor | Laptop |
| Operating system | Windows or Linux |
| Memory upgradeable | Yes |
| Price | $1,099 (lib/data/laptops.ts (street price), checked 2026-07-06) |
Qwen3-Coder on RTX 4060 Laptop (8 GB VRAM, 16 GB RAM): memory by quantization
| Quant | Memory needed | Fits 8 GB? | Max context | Est. speed | Download |
|---|---|---|---|---|---|
| F16 | 17.9 GB | ✗ No | — | — | 16 GB |
| Q8_0 | 10.4 GB | ✗ No | — | — | 8.5 GB |
| Q6_K | 8.5 GB | ✗ No | — | — | 6.6 GB |
| Q5_K_M | 7.6 GB | ✓ Yes | 8K | ~28.8 tok/s | 5.7 GB |
| Q4_K_M | 6.8 GB | ✓ Yes | 16K | ~32.9 tok/s | 4.8 GB |
| Q3_K_M | 5.4 GB | ✓ Yes | 16K | ~43.5 tok/s | 3.4 GB |
| Q2_K | 4.6 GB | ✓ Yes | 32K | ~52.9 tok/s | 2.6 GB |
Which Qwen3-Coder sizes fit
| Variant | Needs at Q4_K_M | Fits? | Est. speed |
|---|---|---|---|
| Qwen3-Coder 480B-A35B (MoE) | 291.6 GB | ✗ Too large | — |
| Qwen3-Coder-Next (80B-A3B MoE) | 51.6 GB | ✗ Too large | — |
| Qwen3-Coder 30B-A3B (MoE) | 20 GB | ✗ Too large | — |
| Qwen3-Coder 8B | 6.8 GB | ✓ Fits | ~32.9 tok/s |
What to watch out for
- Only ~0.4 GB of headroom at Q5_K_M: a longer context or a second application can push this into swapping.
- 3 larger variants of Qwen3-Coder do not fit and would need CPU offload or different hardware.
- This model does not publish its full attention configuration, so the KV-cache share of these figures is inferred from its parameter count rather than computed exactly.
RTX 4060 laptop limitations
- 8 GB VRAM limits you to 7–8B models at Q4 with a short context.
- System RAM is usually upgradeable on this class of laptop even though VRAM is not.
Recommended setup
Ollama or llama.cpp (CUDA/ROCm) — vLLM if you need concurrent serving
How these numbers are calculated
- Memory need = quantized weights + KV cache at 8,192 tokens (f16) + 0.8 GB runtime overhead.
- 8 GB of VRAM on the NVIDIA GeForce RTX 4060 Laptop GPU at 256 GB/s.
- 16 GB of system RAM available for CPU offload when a model exceeds VRAM.
- Throughput is a memory-bandwidth roofline estimate, not a measurement. It is labelled "estimated" everywhere it appears.
- KV cache is inferred from the parameter count because this model does not publish its full attention configuration.
FAQ
Can I run Qwen3-Coder on RTX 4060 Laptop (8 GB VRAM, 16 GB RAM)?
Yes, but it is tight — Qwen3-Coder 8B at Q5_K_M needs about 7.6 GB of the 8 GB usable on RTX 4060 Laptop (8 GB VRAM, 16 GB RAM), leaving only ~0.4 GB before the runtime starts swapping. Expect ~28.8 tok/s (estimated), with room for about 8,192 tokens of context.
Which quantization of Qwen3-Coder should I use on RTX 4060 Laptop (8 GB VRAM, 16 GB RAM)?
Q5_K_M — it needs about 7.6 GB of the 8 GB available, downloads as roughly 5.7 GB, and runs at an estimated 28.8 tokens/sec with up to 8K of context.
What limits Qwen3-Coder on RTX 4060 Laptop (8 GB VRAM, 16 GB RAM)?
Nothing binding — the model fits with headroom and generates at a usable speed on this hardware.
Which runtime should I use?
Ollama or llama.cpp (CUDA/ROCm) — vLLM if you need concurrent serving
Other Computers
- Qwen3-Coder on MacBook Pro M4 Max 128 GB
- Qwen3-Coder on MacBook Pro M4 Max 48 GB
- Qwen3-Coder on MacBook Pro M4 Pro 24 GB
- Qwen3-Coder on MacBook Air M4 16 GB
Other Models on RTX 4060 Laptop (8 GB VRAM, 16 GB RAM)
- SmolLM2 on RTX 4060 Laptop (8 GB VRAM, 16 GB RAM)
- SmolLM3 on RTX 4060 Laptop (8 GB VRAM, 16 GB RAM)
- StarCoder 2 on RTX 4060 Laptop (8 GB VRAM, 16 GB RAM)
- Aya 3B (Tiny Aya) on RTX 4060 Laptop (8 GB VRAM, 16 GB RAM)
- VibeThinker on RTX 4060 Laptop (8 GB VRAM, 16 GB RAM)
Qwen3-Coder on GPUs
- Qwen3-Coder on NVIDIA GeForce RTX 5070
- Qwen3-Coder on NVIDIA GeForce RTX 5060 Ti 8GB
- Qwen3-Coder on NVIDIA GeForce RTX 5060
- Qwen3-Coder on NVIDIA GeForce RTX 4070 Ti
What This Model Is Good At
Model & Tools
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