Can I Run Yi 1.5 Family on RTX 4090 Laptop (16 GB VRAM, 32 GB RAM)?
Autor: Jakub Rusinowski · Ostatnia aktualizacja: 13 maja 2024
Yes — comfortably
Yes, comfortably — Yi 1.5 9B Chat at Q8_0 needs about 11 GB of the 16 GB usable on RTX 4090 Laptop (16 GB VRAM, 32 GB RAM), leaving ~5 GB spare and running at ~40.2 tok/s (estimated), with room for about 16,384 tokens of context.
Confidence: high · Recommended quantization: Q8_0 · Estimated speed: ~40.2 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 |
Yi 1.5 Family 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 | 19.3 GB | ✗ No | — | — | 17.7 GB |
| Q8_0 | 11 GB | ✓ Yes | 16K | ~40.2 tok/s | 9.4 GB |
| Q6_K | 8.8 GB | ✓ Yes | 16K | ~50.2 tok/s | 7.2 GB |
| Q5_K_M | 7.9 GB | ✓ Yes | 16K | ~56.6 tok/s | 6.3 GB |
| Q4_K_M | 6.9 GB | ✓ Yes | 16K | ~64.4 tok/s | 5.3 GB |
| Q3_K_M | 5.4 GB | ✓ Yes | 16K | ~84.1 tok/s | 3.8 GB |
| Q2_K | 4.5 GB | ✓ Yes | 16K | ~101 tok/s | 2.9 GB |
Which Yi 1.5 Family sizes fit
| Variant | Needs at Q4_K_M | Fits? | Est. speed |
|---|---|---|---|
| Yi 1.5 34B Chat | 23.6 GB | ✗ Too large | — |
| Yi 1.5 9B Chat | 6.9 GB | ✓ Fits | ~64.4 tok/s |
What to watch out for
- 1 larger variant of Yi 1.5 Family does not fit and would need CPU offload or different hardware.
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 Yi 1.5 Family on RTX 4090 Laptop (16 GB VRAM, 32 GB RAM)?
Yes, comfortably — Yi 1.5 9B Chat at Q8_0 needs about 11 GB of the 16 GB usable on RTX 4090 Laptop (16 GB VRAM, 32 GB RAM), leaving ~5 GB spare and running at ~40.2 tok/s (estimated), with room for about 16,384 tokens of context.
Which quantization of Yi 1.5 Family should I use on RTX 4090 Laptop (16 GB VRAM, 32 GB RAM)?
Q8_0 — it needs about 11 GB of the 16 GB available, downloads as roughly 9.4 GB, and runs at an estimated 40.2 tokens/sec with up to 16K of context.
What limits Yi 1.5 Family 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
- Yi 1.5 Family on MacBook Pro M4 Max 128 GB
- Yi 1.5 Family on MacBook Pro M4 Max 48 GB
- Yi 1.5 Family on MacBook Pro M4 Pro 24 GB
- Yi 1.5 Family on MacBook Air M4 16 GB
Other Models 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)
- Bonsai 27B on RTX 4090 Laptop (16 GB VRAM, 32 GB RAM)
- Codestral on RTX 4090 Laptop (16 GB VRAM, 32 GB RAM)
- Cogito v1 on RTX 4090 Laptop (16 GB VRAM, 32 GB RAM)
Yi 1.5 Family on GPUs
- Yi 1.5 Family on NVIDIA GeForce RTX 5090
- Yi 1.5 Family on NVIDIA GeForce RTX 5070
- Yi 1.5 Family on NVIDIA GeForce RTX 5060 Ti 8GB
- Yi 1.5 Family on NVIDIA GeForce RTX 5060
What This Model Is Good At
Model & Tools
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