Can I Run Phi 3.5 Family on RTX 4060 Laptop (8 GB VRAM, 16 GB RAM)?
Autor: Jakub Rusinowski · Ostatnia aktualizacja: 20 sierpnia 2024
Yes — comfortably
Yes, comfortably — Phi 3.5 Mini at Q8_0 needs about 5.6 GB of the 8 GB usable on RTX 4060 Laptop (8 GB VRAM, 16 GB RAM), leaving ~2.4 GB spare and running at ~39.4 tok/s (estimated), with room for about 16,384 tokens of context.
Confidence: high · Recommended quantization: Q8_0 · Estimated speed: ~39.4 tok/s
See what else this hardware can run →
lub porównaj w Vast.ai od $0.35/godz. (typical low · varies)
Jako partner Amazon zarabiamy na kwalifikujących się zakupach. Linki do chmurowych GPU są linkami polecającymi — możemy otrzymać prowizję bez dodatkowych kosztów dla Ciebie.
RTX 4060 Laptop (8 GB VRAM, 16 GB RAM) — what it gives a model
| Usable memory for models | 8 GB |
| Memory bandwidth | 256 GB/s |
| Form factor | Laptop |
| Operating system | Windows or Linux |
| Memory upgradeable | Yes |
| Price | $1,099 (lib/data/laptops.ts (street price), checked 2026-07-06) |
Phi 3.5 Family on RTX 4060 Laptop (8 GB VRAM, 16 GB RAM): memory by quantization
| Quant | Memory needed | Fits 8 GB? | Max context | Est. speed | Download |
|---|---|---|---|---|---|
| F16 | 9.2 GB | ✗ No | — | — | 7.6 GB |
| Q8_0 | 5.6 GB | ✓ Yes | 16K | ~39.4 tok/s | 4 GB |
| Q6_K | 4.7 GB | ✓ Yes | 32K | ~48.6 tok/s | 3.1 GB |
| Q5_K_M | 4.3 GB | ✓ Yes | 32K | ~54.4 tok/s | 2.7 GB |
| Q4_K_M | 3.9 GB | ✓ Yes | 32K | ~61.4 tok/s | 2.3 GB |
| Q3_K_M | 3.2 GB | ✓ Yes | 32K | ~78.3 tok/s | 1.6 GB |
| Q2_K | 2.9 GB | ✓ Yes | 32K | ~92.2 tok/s | 1.2 GB |
RTX 4060 laptop limitations
- 8 GB VRAM limits you to 7–8B models at Q4 with a short context.
- System RAM is usually upgradeable on this class of laptop even though VRAM is not.
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.
- 8 GB of VRAM on the NVIDIA GeForce RTX 4060 Laptop GPU at 256 GB/s.
- 16 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 Phi 3.5 Family on RTX 4060 Laptop (8 GB VRAM, 16 GB RAM)?
Yes, comfortably — Phi 3.5 Mini at Q8_0 needs about 5.6 GB of the 8 GB usable on RTX 4060 Laptop (8 GB VRAM, 16 GB RAM), leaving ~2.4 GB spare and running at ~39.4 tok/s (estimated), with room for about 16,384 tokens of context.
Which quantization of Phi 3.5 Family should I use on RTX 4060 Laptop (8 GB VRAM, 16 GB RAM)?
Q8_0 — it needs about 5.6 GB of the 8 GB available, downloads as roughly 4 GB, and runs at an estimated 39.4 tokens/sec with up to 16K of context.
What limits Phi 3.5 Family on RTX 4060 Laptop (8 GB VRAM, 16 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
- Phi 3.5 Family on MacBook Pro M4 Max 128 GB
- Phi 3.5 Family on MacBook Pro M4 Max 48 GB
- Phi 3.5 Family on MacBook Pro M4 Pro 24 GB
- Phi 3.5 Family on MacBook Air M4 16 GB
Other Models on RTX 4060 Laptop (8 GB VRAM, 16 GB RAM)
- Phi-4 Family on RTX 4060 Laptop (8 GB VRAM, 16 GB RAM)
- Phi-4 Mini on RTX 4060 Laptop (8 GB VRAM, 16 GB RAM)
- Qwen 2.5 Family on RTX 4060 Laptop (8 GB VRAM, 16 GB RAM)
- Qwen 2.5 VL on RTX 4060 Laptop (8 GB VRAM, 16 GB RAM)
- Qwen 3 on RTX 4060 Laptop (8 GB VRAM, 16 GB RAM)
Phi 3.5 Family on GPUs
- Phi 3.5 Family on NVIDIA GeForce RTX 5060 Ti 8GB
- Phi 3.5 Family on NVIDIA GeForce RTX 5060
- Phi 3.5 Family on NVIDIA GeForce RTX 4060
What This Model Is Good At
Model & Tools
← Can I Run It? | Phi 3.5 Family model page | Check your hardware