Phi-4 14B VRAM Requirements: 12GB, 16GB or 24GB?

Short answer: Microsoft’s official Phi-4 GGUF repository lists a 14B dense text model from 5.55GB Q2_K through 29.3GB BF16. For mainstream choices, 12GB is a controlled Q4/Q5 starting tier, 16GB is a more flexible Q4–Q6 tier, and 24GB is the practical Q8_0 tier. The 15.6GB Q8_0 file is too tight for a nominal 16GB pool once cache and runtime allocations are included.

Official Phi-4 GGUF sizes

Official artifact	Displayed size	Capacity implication
Q2_K	5.55GB	Fits smaller pools, but aggressive compression requires task-quality validation
Q3_K	7.36GB	8GB is a file-size boundary, not a complete-workload promise
Q4_K_S	8.44GB	Points to 12GB as the first common one-device tier
Q4_K	9.05GB	12GB can be viable with controlled context and overhead
Q5_0 / Q5_K	10.2GB / 10.6GB	12GB is tighter; 16GB provides more useful margin
Q6_K	12GB	Does not leave working room on a nominal 12GB pool
Q8_0	15.6GB	Too tight for 16GB as a complete workload; 24GB is the practical tier
BF16	29.3GB	Too tight for 32GB as a complete workload; consider 48GB+

12GB, 16GB or 24GB?

• 12GB: target the 8.44GB Q4_K_S or 9.05GB Q4_K artifacts with controlled context, one sequence and measured runtime overhead. Q5 leaves less room.
• 16GB: gives Q4/Q5 more working margin and makes the 12GB Q6_K file more plausible. The 15.6GB Q8_0 file remains a file-size trap.
• 24GB: is the practical Q8_0 tier and adds context or batching margin, but capacity alone does not establish speed or quality.
• 32GB: the 29.3GB BF16 file is technically below the label but leaves little complete-workload room.
• 48GB: provides materially more room for BF16 plus cache and runtime allocations.

Compare the broader class in the 14B requirements guide and hardware paths in the 14B GPU guide.

Phi-4 is a 14B text model with 16K context

Microsoft’s official model card describes Phi-4 as a 14-billion-parameter dense decoder-only Transformer for text input and generated-text output, with a 16K-token context length. Do not confuse it with Phi-4-mini, Phi-4-multimodal or later reasoning variants: those are different model identities with different memory plans.

Context and runtime still matter

The model’s 16K context capability does not mean every quantization reaches 16K on its nearest memory label. KV cache, cache precision, prompt length, generated tokens, batching, concurrency and runtime buffers all consume memory beyond the file. Start below the maximum, measure peak allocation and increase deliberately.

Avoid false quantization rankings

The official repository gives file sizes, not a universal quality or speed ranking. A larger quantization can preserve more information yet still perform worse for a specific system or workload because kernels, memory bandwidth and runtime support differ. Compare task output as well as fit. The quantization guide explains the naming without treating bits alone as a quality score.

A reproducible Phi-4 fit test

1. Record the exact Microsoft filename, displayed size and checksum.
2. Record GPU or unified-memory capacity, OS, driver and runtime version.
3. Measure available memory before loading.
4. Record context, output target, concurrency, cache precision and offload settings.
5. Run a representative long prompt and capture peak memory.
6. Record time to first token, generation throughput and any fallback or out-of-memory error.
7. Evaluate task quality before choosing a more aggressive quantization.

Use the workload test and share a measured result.

Official sources and limits

• Microsoft Phi-4 official GGUF repository
• Microsoft Phi-4 model card
• Microsoft Phi-4 collection
• HighVRAM methodology

Reviewed October 25, 2026. File sizes are Microsoft’s repository display values on that date. HighVRAM has not independently benchmarked every artifact or runtime. Fit does not establish speed or output quality.

Need a larger reasoning model? Compare the official Magistral Small 1.2 24B VRAM guide, including its 14.3GB Q4 through 47.2GB BF16 files and runtime warnings.