Can I Run Phi-4 Family on RTX 4090 Desktop (24 GB VRAM, 64 GB RAM)?
作者： Jakub Rusinowski · 最后更新： 2026年11月11日
Yes
Yes — Phi-4 Reasoning Vision 15B at Q8_0 needs about 18.5 GB of the 24 GB usable on RTX 4090 Desktop (24 GB VRAM, 64 GB RAM) (~5.5 GB spare), at ~40.6 tok/s (estimated), with room for about 16,384 tokens of context.
Confidence: high · Recommended quantization: Q8_0 · Estimated speed: ~40.6 tok/s
See what else this hardware can run →
或在 Vast.ai 比较，低至 $0.35/小时 (typical low · varies)
作为亚马逊联盟成员，我们从符合条件的购买中获得收入。云 GPU 链接为推荐链接——我们可能获得佣金，您无需额外付费。
RTX 4090 Desktop (24 GB VRAM, 64 GB RAM) — what it gives a model
| Usable memory for models | 24 GB |
| Memory bandwidth | 1008 GB/s |
| Form factor | Desktop |
| Operating system | Windows or Linux |
| Memory upgradeable | Yes |
Phi-4 Family on RTX 4090 Desktop (24 GB VRAM, 64 GB RAM): memory by quantization
| Quant | Memory needed | Fits 24 GB? | Max context | Est. speed | Download |
|---|---|---|---|---|---|
| F16 | 32.7 GB | ✗ No | — | — | 30.2 GB |
| Q8_0 | 18.5 GB | ✓ Yes | 16K | ~40.6 tok/s | 16.1 GB |
| Q6_K | 14.9 GB | ✓ Yes | 16K | ~50.6 tok/s | 12.4 GB |
| Q5_K_M | 13.2 GB | ✓ Yes | 16K | ~57 tok/s | 10.7 GB |
| Q4_K_M | 11.6 GB | ✓ Yes | 16K | ~64.8 tok/s | 9.1 GB |
| Q3_K_M | 8.9 GB | ✓ Yes | 16K | ~84.2 tok/s | 6.4 GB |
| Q2_K | 7.4 GB | ✓ Yes | 16K | ~100.7 tok/s | 5 GB |
Which Phi-4 Family sizes fit
| Variant | Needs at Q4_K_M | Fits? | Est. speed |
|---|---|---|---|
| Phi-4 Reasoning Vision 15B | 11.6 GB | ✓ Fits | ~64.8 tok/s |
| Phi-4 (14B) | 10.9 GB | ✓ Fits | ~68.8 tok/s |
RTX 4090 desktop limitations
- 24 GB is the sweet spot for 27–32B models at Q4; 70B needs offload or a second card.
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.
- 24 GB of VRAM on the NVIDIA GeForce RTX 4090 at 1008 GB/s.
- 64 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-4 Family on RTX 4090 Desktop (24 GB VRAM, 64 GB RAM)?
Yes — Phi-4 Reasoning Vision 15B at Q8_0 needs about 18.5 GB of the 24 GB usable on RTX 4090 Desktop (24 GB VRAM, 64 GB RAM) (~5.5 GB spare), at ~40.6 tok/s (estimated), with room for about 16,384 tokens of context.
Which quantization of Phi-4 Family should I use on RTX 4090 Desktop (24 GB VRAM, 64 GB RAM)?
Q8_0 — it needs about 18.5 GB of the 24 GB available, downloads as roughly 16.1 GB, and runs at an estimated 40.6 tokens/sec with up to 16K of context.
What limits Phi-4 Family on RTX 4090 Desktop (24 GB VRAM, 64 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
Other Models on RTX 4090 Desktop (24 GB VRAM, 64 GB RAM)
- Phi-4 Mini on RTX 4090 Desktop (24 GB VRAM, 64 GB RAM)
- Poolside Laguna XS 2.1 on RTX 4090 Desktop (24 GB VRAM, 64 GB RAM)
- Qwen 2.5 Family on RTX 4090 Desktop (24 GB VRAM, 64 GB RAM)
- Qwen 2.5 VL on RTX 4090 Desktop (24 GB VRAM, 64 GB RAM)
- Qwen 3 on RTX 4090 Desktop (24 GB VRAM, 64 GB RAM)
Phi-4 Family on GPUs
- Phi-4 Family on NVIDIA GeForce RTX 5080
- Phi-4 Family on NVIDIA GeForce RTX 5070 Ti
- Phi-4 Family on NVIDIA GeForce RTX 5070
- Phi-4 Family on NVIDIA GeForce RTX 5060 Ti 16GB
What This Model Is Good At
Model & Tools
← Can I Run It? | Phi-4 Family model page | Check your hardware