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Local AI

Can the RTX 3080 10GB Run Phi-4 14B? (✅ 8.5GB VRAM Needed, 2026)

Can the RTX 3080 10GB run Phi-4 14B locally? Yes — runs great at Q4 (recommended). VRAM requirements, expected tokens/sec, the right quantization, and the cheapest GPU that fits if it doesn’t.

← Can It Run? — GPU compatibility matrix · Local LLMs guide

✅ Yes — runs great at Q4 (recommended)
Phi-4 14B needs 8.5 GB VRAM at Q4. The RTX 3080 10GB has 10 GB.

Φ Phi-4 14B (Microsoft) is a 14B-parameter model for reasoning, math, structured tasks. Microsoft’s reasoning-focused model, punches above its size. The question is whether the RTX 3080 10GB’s 10 GB of VRAM can hold it — here’s the exact math.

VRAM requirements

QuantizationVRAM neededRTX 3080 10GB
Q4_K_M (recommended)8.5 GB
Q8_0 (near-lossless)15 GB

Expected performance

Running Phi-4 14B at Q4 on the RTX 3080 10GB, expect roughly ~35 tokens/sec in Ollama or llama.cpp — comfortably faster than reading speed, so chat feels responsive.

Headroom: 8.5 GB used of 10 GB leaves about 1.5 GB free for KV cache (context). That’s fine for 4-8K context; for 32K+ you may need a smaller batch or a lower quant.

About the RTX 3080 10GB

Architecture: Ampere · VRAM: 10 GB · Price: ~$400 used.

Good: Fast memory bandwidth, strong tokens/sec for its tier
Watch out: Only 10GB — awkward middle ground, limits model size

Check current RTX 3080 10GB price on Amazon →

Related checks

Other models on the RTX 3080 10GB

Phi-4 14B on other GPUs

🎯 LLM Hardware Checker

Enter your exact GPU + RAM, see every model you can run.

💾 VRAM Requirements

Exact VRAM per model at Q4/Q5/Q8/FP16 with context scaling.

Frequently asked questions

Can the RTX 3080 10GB run Phi-4 14B?

Yes — runs great at Q4 (recommended). Phi-4 14B needs 8.5 GB VRAM at Q4_K_M; the RTX 3080 10GB has 10 GB.

How much VRAM does Phi-4 14B need?

Phi-4 14B needs about 8.5 GB at Q4_K_M (recommended) and 15 GB at Q8_0.

How many tokens per second will the RTX 3080 10GB get on Phi-4 14B?

Roughly ~35 tokens/sec at Q4 in Ollama or llama.cpp.

About the speed figure. The tokens/sec number above is an estimate, not a measured benchmark. Real throughput depends on your runtime and backend (Ollama, llama.cpp, vLLM), the exact quantization you download, context length, memory bandwidth, and whether any layers are offloaded to CPU. Treat it as a rough guide to the tier of performance, not a promised result.

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