Can I Run Qwen3-Coder on 32 GB system RAM?
Autor: Jakub Rusinowski · Ostatnia aktualizacja: 29 września 2026
Yes, but it is tight
Yes, but it is tight — Qwen3-Coder 30B-A3B (MoE) at Q5_K_M needs about 23.2 GB of the 25.6 GB usable on 32 GB system RAM, leaving only ~2.4 GB before the runtime starts swapping. Expect ~23.2 tok/s (estimated), with room for about 16,384 tokens of context.
Confidence: medium · Recommended quantization: Q5_K_M · Estimated speed: ~23.2 tok/s
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32 GB system RAM — what it gives a model
| Usable memory for models | 25.6 GB |
| Memory bandwidth | 90 GB/s |
Qwen3-Coder on 32 GB system RAM: memory by quantization
| Quant | Memory needed | Fits 25.6 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 | ✓ Yes | 16K | ~23.2 tok/s | 21.6 GB |
| Q4_K_M | 20 GB | ✓ Yes | 32K | ~26.4 tok/s | 18.4 GB |
| Q3_K_M | 14.6 GB | ✓ Yes | 64K | ~34.3 tok/s | 13 GB |
| Q2_K | 11.6 GB | ✓ Yes | 128K | ~41 tok/s | 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 | ✓ Fits | ~26.4 tok/s |
What to watch out for
- 2 larger variants of Qwen3-Coder do not fit and would need CPU offload or different hardware.
- 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.
- 25.6 GB of the 32 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 32 GB system RAM?
Yes, but it is tight — Qwen3-Coder 30B-A3B (MoE) at Q5_K_M needs about 23.2 GB of the 25.6 GB usable on 32 GB system RAM, leaving only ~2.4 GB before the runtime starts swapping. Expect ~23.2 tok/s (estimated), with room for about 16,384 tokens of context.
Which quantization of Qwen3-Coder should I use on 32 GB system RAM?
Q5_K_M — it needs about 23.2 GB of the 25.6 GB available, downloads as roughly 21.6 GB, and runs at an estimated 23.2 tokens/sec with up to 16K of context.
What limits Qwen3-Coder on 32 GB system RAM?
Nothing binding — the model fits with headroom and generates at a usable speed on this hardware.
Which runtime should I use?
llama.cpp (CPU build) or Ollama — both run without a GPU
Other RAM Capacities
Other Models on 32 GB system RAM
- SmolLM2 on 32 GB system RAM
- SmolLM3 on 32 GB system RAM
- StarCoder 2 on 32 GB system RAM
- Aya 3B (Tiny Aya) on 32 GB system RAM
- VibeThinker on 32 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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