Can I Run StarCoder 2 on RTX 4090 Laptop (16 GB VRAM, 32 GB RAM)?
Written by Jakub Rusinowski · Last updated February 28, 2024
Yes
Yes — StarCoder 2 15B at Q6_K needs about 14.2 GB of the 16 GB usable on RTX 4090 Laptop (16 GB VRAM, 32 GB RAM) (~1.8 GB spare), at ~30.8 tok/s (estimated), with room for about 16,384 tokens of context.
Confidence: high · Recommended quantization: Q6_K · Estimated speed: ~30.8 tok/s
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RTX 4090 Laptop (16 GB VRAM, 32 GB RAM) — what it gives a model
| Usable memory for models | 16 GB |
| Memory bandwidth | 576 GB/s |
| Form factor | Laptop |
| Operating system | Windows or Linux |
| Memory upgradeable | Yes |
StarCoder 2 on RTX 4090 Laptop (16 GB VRAM, 32 GB RAM): memory by quantization
| Quant | Memory needed | Fits 16 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 | ✓ Yes | 16K | ~30.8 tok/s | 12.7 GB |
| Q5_K_M | 12.5 GB | ✓ Yes | 16K | ~35.1 tok/s | 11 GB |
| Q4_K_M | 10.8 GB | ✓ Yes | 16K | ~40.5 tok/s | 9.4 GB |
| Q3_K_M | 8.1 GB | ✓ Yes | 16K | ~54.6 tok/s | 6.6 GB |
| Q2_K | 6.6 GB | ✓ Yes | 16K | ~67.5 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 | ~40.5 tok/s |
| StarCoder 2 7B | 5.7 GB | ✓ Fits | ~77.3 tok/s |
| StarCoder 2 3B | 2.9 GB | ✓ Fits | ~148.6 tok/s |
RTX 4090 laptop limitations
- A mobile RTX 4090 carries 16 GB, not the desktop card's 24 GB, and is closer to a desktop 4080 in throughput — which is why it is modelled against that chip here.
- Sustained throughput depends on the chassis power limit; thin laptops throttle well below the quoted figures.
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.
- 16 GB of VRAM on the NVIDIA GeForce RTX 4090 Laptop GPU at 576 GB/s.
- 32 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 computed from this model's published attention configuration.
FAQ
Can I run StarCoder 2 on RTX 4090 Laptop (16 GB VRAM, 32 GB RAM)?
Yes — StarCoder 2 15B at Q6_K needs about 14.2 GB of the 16 GB usable on RTX 4090 Laptop (16 GB VRAM, 32 GB RAM) (~1.8 GB spare), at ~30.8 tok/s (estimated), with room for about 16,384 tokens of context.
Which quantization of StarCoder 2 should I use on RTX 4090 Laptop (16 GB VRAM, 32 GB RAM)?
Q6_K — it needs about 14.2 GB of the 16 GB available, downloads as roughly 12.7 GB, and runs at an estimated 30.8 tokens/sec with up to 16K of context.
What limits StarCoder 2 on RTX 4090 Laptop (16 GB VRAM, 32 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
- StarCoder 2 on MacBook Pro M4 Max 128 GB
- StarCoder 2 on MacBook Pro M4 Max 48 GB
- StarCoder 2 on MacBook Pro M4 Pro 24 GB
- StarCoder 2 on MacBook Air M4 16 GB
Other Models on RTX 4090 Laptop (16 GB VRAM, 32 GB RAM)
- Aya 3B (Tiny Aya) on RTX 4090 Laptop (16 GB VRAM, 32 GB RAM)
- VibeThinker on RTX 4090 Laptop (16 GB VRAM, 32 GB RAM)
- Yi 1.5 Family on RTX 4090 Laptop (16 GB VRAM, 32 GB RAM)
- Aya Expanse on RTX 4090 Laptop (16 GB VRAM, 32 GB RAM)
- BitNet b1.58 on RTX 4090 Laptop (16 GB VRAM, 32 GB 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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