Can I Run StarCoder 2 on 16 GB system RAM?
Written by Jakub Rusinowski · Last updated February 28, 2024
Yes, but it is tight
Yes, but it is tight — StarCoder 2 15B at Q5_K_M needs about 12.5 GB of the 12.8 GB usable on 16 GB system RAM, leaving only ~0.3 GB before the runtime starts swapping. Expect ~5.9 tok/s (estimated), with room for about 8,192 tokens of context.
Confidence: medium · Recommended quantization: Q5_K_M · Estimated speed: ~5.9 tok/s
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16 GB system RAM — what it gives a model
| Usable memory for models | 12.8 GB |
| Memory bandwidth | 90 GB/s |
StarCoder 2 on 16 GB system RAM: memory by quantization
| Quant | Memory needed | Fits 12.8 GB? | Max context | Est. speed | Download |
|---|---|---|---|---|---|
| F16 | 32.5 GB | ✗ No | — | — | 31 GB |
| Q8_0 | 17.9 GB | ✗ No | — | — | 16.5 GB |
| Q6_K | 14.2 GB | ✗ No | — | — | 12.7 GB |
| Q5_K_M | 12.5 GB | ✓ Yes | 8K | ~5.9 tok/s | 11 GB |
| Q4_K_M | 10.8 GB | ✓ Yes | 16K | ~6.8 tok/s | 9.4 GB |
| Q3_K_M | 8.1 GB | ✓ Yes | 16K | ~9.5 tok/s | 6.6 GB |
| Q2_K | 6.6 GB | ✓ Yes | 16K | ~12 tok/s | 5.1 GB |
Which StarCoder 2 sizes fit
| Variant | Needs at Q4_K_M | Fits? | Est. speed |
|---|---|---|---|
| StarCoder 2 15B | 10.8 GB | ✓ Fits | ~6.8 tok/s |
| StarCoder 2 7B | 5.7 GB | ✓ Fits | ~14.1 tok/s |
| StarCoder 2 3B | 2.9 GB | ✓ Fits | ~32.2 tok/s |
What to watch out for
- Only ~0.3 GB of headroom at Q5_K_M: a longer context or a second application can push this into swapping.
- 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.
- 12.8 GB of the 16 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 StarCoder 2 on 16 GB system RAM?
Yes, but it is tight — StarCoder 2 15B at Q5_K_M needs about 12.5 GB of the 12.8 GB usable on 16 GB system RAM, leaving only ~0.3 GB before the runtime starts swapping. Expect ~5.9 tok/s (estimated), with room for about 8,192 tokens of context.
Which quantization of StarCoder 2 should I use on 16 GB system RAM?
Q5_K_M — it needs about 12.5 GB of the 12.8 GB available, downloads as roughly 11 GB, and runs at an estimated 5.9 tokens/sec with up to 8K of context.
What limits StarCoder 2 on 16 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 16 GB system RAM
- Aya 3B (Tiny Aya) on 16 GB system RAM
- VibeThinker on 16 GB system RAM
- Yi 1.5 Family on 16 GB system RAM
- Aya Expanse on 16 GB system RAM
- BitNet b1.58 on 16 GB system RAM
StarCoder 2 on GPUs
- StarCoder 2 on NVIDIA GeForce RTX 5080
- StarCoder 2 on NVIDIA GeForce RTX 5070 Ti
- StarCoder 2 on NVIDIA GeForce RTX 5070
- StarCoder 2 on NVIDIA GeForce RTX 5060 Ti 16GB
What This Model Is Good At
Model & Tools
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