Can I Run Phi-4 Family on RTX 3090 Desktop (24 GB VRAM, 64 GB RAM)?
Written by Jakub Rusinowski · Last updated January 6, 2025
Yes — comfortably
Yes, comfortably — Phi-4 (14B) at Q8_0 needs about 17.4 GB of the 24 GB usable on RTX 3090 Desktop (24 GB VRAM, 64 GB RAM), leaving ~6.6 GB spare and running 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
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RTX 3090 Desktop (24 GB VRAM, 64 GB RAM) — what it gives a model
| Usable memory for models | 24 GB |
| Memory bandwidth | 936 GB/s |
| Form factor | Desktop |
| Operating system | Windows or Linux |
| Memory upgradeable | Yes |
Phi-4 Family on RTX 3090 Desktop (24 GB VRAM, 64 GB RAM): memory by quantization
| Quant | Memory needed | Fits 24 GB? | Max context | Est. speed | Download |
|---|---|---|---|---|---|
| F16 | 30.5 GB | ✗ No | — | — | 28 GB |
| Q8_0 | 17.4 GB | ✓ Yes | 16K | ~40.6 tok/s | 14.9 GB |
| Q6_K | 14 GB | ✓ Yes | 16K | ~50.5 tok/s | 11.5 GB |
| Q5_K_M | 12.4 GB | ✓ Yes | 16K | ~56.9 tok/s | 9.9 GB |
| Q4_K_M | 10.9 GB | ✓ Yes | 16K | ~64.6 tok/s | 8.5 GB |
| Q3_K_M | 8.4 GB | ✓ Yes | 16K | ~83.7 tok/s | 6 GB |
| Q2_K | 7.1 GB | ✓ Yes | 16K | ~100 tok/s | 4.6 GB |
RTX 3090 desktop limitations
- The cheapest route to 24 GB of VRAM, and the standard used-market recommendation for local LLMs.
- Older architecture: no FP8 acceleration, and higher idle power than a current 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 3090 at 936 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 3090 Desktop (24 GB VRAM, 64 GB RAM)?
Yes, comfortably — Phi-4 (14B) at Q8_0 needs about 17.4 GB of the 24 GB usable on RTX 3090 Desktop (24 GB VRAM, 64 GB RAM), leaving ~6.6 GB spare and running 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 3090 Desktop (24 GB VRAM, 64 GB RAM)?
Q8_0 — it needs about 17.4 GB of the 24 GB available, downloads as roughly 14.9 GB, and runs at an estimated 40.6 tokens/sec with up to 16K of context.
What limits Phi-4 Family on RTX 3090 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 3090 Desktop (24 GB VRAM, 64 GB RAM)
- Phi-4 Mini on RTX 3090 Desktop (24 GB VRAM, 64 GB RAM)
- Poolside Laguna XS 2.1 on RTX 3090 Desktop (24 GB VRAM, 64 GB RAM)
- Qwen 2.5 Family on RTX 3090 Desktop (24 GB VRAM, 64 GB RAM)
- Qwen 2.5 VL on RTX 3090 Desktop (24 GB VRAM, 64 GB RAM)
- Qwen 3 on RTX 3090 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
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