Can I Run Phi-4 Mini on RTX 3090 Desktop (24 GB VRAM, 64 GB RAM)?
Written by Jakub Rusinowski · Last updated February 4, 2025
Yes — comfortably
Yes, comfortably — Phi-4 Mini (3.8B) at Q8_0 needs about 5.9 GB of the 24 GB usable on RTX 3090 Desktop (24 GB VRAM, 64 GB RAM), leaving ~18.1 GB spare and running at ~114.1 tok/s (estimated), with room for about 65,536 tokens of context.
Confidence: high · Recommended quantization: Q8_0 · Estimated speed: ~114.1 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 Mini 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 | 9.5 GB | ✓ Yes | 64K | ~72.3 tok/s | 7.6 GB |
| Q8_0 | 5.9 GB | ✓ Yes | 64K | ~114.1 tok/s | 4 GB |
| Q6_K | 5 GB | ✓ Yes | 64K | ~134.2 tok/s | 3.1 GB |
| Q5_K_M | 4.6 GB | ✓ Yes | 64K | ~146 tok/s | 2.7 GB |
| Q4_K_M | 4.2 GB | ✓ Yes | 64K | ~159.3 tok/s | 2.3 GB |
| Q3_K_M | 3.5 GB | ✓ Yes | 64K | ~188 tok/s | 1.6 GB |
| Q2_K | 3.1 GB | ✓ Yes | 64K | ~208.8 tok/s | 1.2 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 Mini on RTX 3090 Desktop (24 GB VRAM, 64 GB RAM)?
Yes, comfortably — Phi-4 Mini (3.8B) at Q8_0 needs about 5.9 GB of the 24 GB usable on RTX 3090 Desktop (24 GB VRAM, 64 GB RAM), leaving ~18.1 GB spare and running at ~114.1 tok/s (estimated), with room for about 65,536 tokens of context.
Which quantization of Phi-4 Mini should I use on RTX 3090 Desktop (24 GB VRAM, 64 GB RAM)?
Q8_0 — it needs about 5.9 GB of the 24 GB available, downloads as roughly 4 GB, and runs at an estimated 114.1 tokens/sec with up to 64K of context.
What limits Phi-4 Mini 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)
- 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)
- Qwen 3.5 on RTX 3090 Desktop (24 GB VRAM, 64 GB RAM)
Phi-4 Mini on GPUs
- Phi-4 Mini on NVIDIA GeForce RTX 5060 Ti 8GB
- Phi-4 Mini on NVIDIA GeForce RTX 5060
- Phi-4 Mini on NVIDIA GeForce RTX 4060
What This Model Is Good At
Model & Tools
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