Can I Run Qwen3-Coder on 8 GB system RAM?
Autor: Jakub Rusinowski · Ostatnia aktualizacja: 29 września 2026
No
No — Qwen3-Coder does not fit in 8 GB system RAM. Even at Q2_K it needs about 11.6 GB against 6.4 GB usable, a shortfall of roughly 5.2 GB.
Confidence: high
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8 GB system RAM — what it gives a model
| Usable memory for models | 6.4 GB |
| Memory bandwidth | 90 GB/s |
Qwen3-Coder on 8 GB system RAM: memory by quantization
| Quant | Memory needed | Fits 6.4 GB? | Max context | Est. speed | Download |
|---|---|---|---|---|---|
| F16 | 62.6 GB | ✗ No | — | — | 61 GB |
| Q8_0 | 34 GB | ✗ No | — | — | 32.4 GB |
| Q6_K | 26.6 GB | ✗ No | — | — | 25 GB |
| Q5_K_M | 23.2 GB | ✗ No | — | — | 21.6 GB |
| Q4_K_M | 20 GB | ✗ No | — | — | 18.4 GB |
| Q3_K_M | 14.6 GB | ✗ No | — | — | 13 GB |
| Q2_K | 11.6 GB | ✗ No | — | — | 10 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) | 49.4 GB | ✗ Too large | — |
| Qwen3-Coder 30B-A3B (MoE) | 20 GB | ✗ Too large | — |
What to watch out for
- These figures assume CPU-only inference. Any discrete GPU, even an 8 GB one, will be several times faster for models that fit in its VRAM.
Recommended setup
llama.cpp (CPU build) or Ollama — both run without a GPU
How these numbers are calculated
- Memory need = quantized weights + KV cache at 8,192 tokens (f16) + 0.8 GB runtime overhead.
- 6.4 GB of the 8 GB is treated as usable for model weights (80% — the rest is the OS and running applications).
- DDR5-5600 dual channel at 89.6 GB/s peak. CPU decode is assumed to sustain 35% of that peak, because CPU inference is not purely bandwidth-bound — it also spends real time in compute and thread synchronisation. This figure is an assumption, not a fitted constant: no CPU measurement is in the calibration set.
- CPU-only inference: no GPU is assumed. A GPU of any size will beat these figures substantially.
- Throughput is a memory-bandwidth roofline estimate, not a measurement. It is labelled "estimated" everywhere it appears.
- KV cache is computed from this model's published attention configuration.
FAQ
Can I run Qwen3-Coder on 8 GB system RAM?
No — Qwen3-Coder does not fit in 8 GB system RAM. Even at Q2_K it needs about 11.6 GB against 6.4 GB usable, a shortfall of roughly 5.2 GB.
What limits Qwen3-Coder on 8 GB system RAM?
Memory. 8 GB system RAM provides about 6.4 GB usable, and the smallest quantization of this model needs 11.6 GB.
Which runtime should I use?
llama.cpp (CPU build) or Ollama — both run without a GPU
Other RAM Capacities
Other Models on 8 GB system RAM
- SmolLM2 on 8 GB system RAM
- SmolLM3 on 8 GB system RAM
- StarCoder 2 on 8 GB system RAM
- Aya 3B (Tiny Aya) on 8 GB system RAM
- VibeThinker on 8 GB system 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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