Can I Run SmolLM3 on RTX 4090 Laptop (16 GB VRAM, 32 GB RAM)?
Written by Jakub Rusinowski · Last updated September 6, 2026
Yes — comfortably
Yes, comfortably — SmolLM3 3B at Q8_0 needs about 4.9 GB of the 16 GB usable on RTX 4090 Laptop (16 GB VRAM, 32 GB RAM), leaving ~11.1 GB spare and running at ~92.7 tok/s (estimated), with room for about 65,536 tokens of context.
Confidence: medium · Recommended quantization: Q8_0 · Estimated speed: ~92.7 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 |
SmolLM3 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 | 7.8 GB | ✓ Yes | 64K | ~57.2 tok/s | 6.2 GB |
| Q8_0 | 4.9 GB | ✓ Yes | 64K | ~92.7 tok/s | 3.3 GB |
| Q6_K | 4.2 GB | ✓ Yes | 64K | ~110.3 tok/s | 2.5 GB |
| Q5_K_M | 3.8 GB | ✓ Yes | 64K | ~120.9 tok/s | 2.2 GB |
| Q4_K_M | 3.5 GB | ✓ Yes | 64K | ~133 tok/s | 1.9 GB |
| Q3_K_M | 3 GB | ✓ Yes | 128K | ~159.8 tok/s | 1.3 GB |
| Q2_K | 2.7 GB | ✓ Yes | 128K | ~179.8 tok/s | 1 GB |
What to watch out for
- 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 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 inferred from the parameter count because this model does not publish its full attention configuration.
FAQ
Can I run SmolLM3 on RTX 4090 Laptop (16 GB VRAM, 32 GB RAM)?
Yes, comfortably — SmolLM3 3B at Q8_0 needs about 4.9 GB of the 16 GB usable on RTX 4090 Laptop (16 GB VRAM, 32 GB RAM), leaving ~11.1 GB spare and running at ~92.7 tok/s (estimated), with room for about 65,536 tokens of context.
Which quantization of SmolLM3 should I use on RTX 4090 Laptop (16 GB VRAM, 32 GB RAM)?
Q8_0 — it needs about 4.9 GB of the 16 GB available, downloads as roughly 3.3 GB, and runs at an estimated 92.7 tokens/sec with up to 64K of context.
What limits SmolLM3 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
- SmolLM3 on MacBook Pro M4 Max 128 GB
- SmolLM3 on MacBook Pro M4 Max 48 GB
- SmolLM3 on MacBook Pro M4 Pro 24 GB
- SmolLM3 on MacBook Air M4 16 GB
Other Models on RTX 4090 Laptop (16 GB VRAM, 32 GB RAM)
- StarCoder 2 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)
SmolLM3 on GPUs
- SmolLM3 on NVIDIA GeForce RTX 5060 Ti 8GB
- SmolLM3 on NVIDIA GeForce RTX 5060
- SmolLM3 on NVIDIA GeForce RTX 4060