Microsoft Phi-4 14B is trained on high-density synthetic textbook data, achieving STEM reasoning comparable to models 5x its size while fitting on 12GB GPUs.
ollama run phi4:14b
Sizing Microsoft Phi-4 14B on 1x NVIDIA GeForce RTX 4090 (24 GB)
| Quantization Format | Bitrate | Weights | KV Cache | Total VRAM | Throughput | Quality Retention | Compatibility |
|---|---|---|---|---|---|---|---|
|
FP16 / BF16
Full Precision
|
16.00 bpw | 29.40 GB | 1.56 GB | 32.46 GB | ~5.9 tok/s |
100% Lossless
+0.000 (Baseline)
|
Hybrid Offload |
|
FP8 / Q8_0
Production Grade
GGUF / vLLM
|
8.50 bpw | 15.62 GB | 1.56 GB | 18.68 GB | ~46.5 tok/s |
99.9% Near-Lossless
+0.004 ppl
|
100% GPU VRAM |
|
Q6_K (6-bit)
High Precision
GGUF k-quant
|
6.56 bpw | 12.05 GB | 1.56 GB | 15.12 GB | ~60.2 tok/s |
99.5% Excellent
+0.021 ppl
|
100% GPU VRAM |
|
Q5_K_M (5-bit)
Balanced Plus
GGUF k-quant
|
5.69 bpw | 10.46 GB | 1.56 GB | 13.52 GB | ~69.4 tok/s |
99.0% Very High
+0.056 ppl
|
100% GPU VRAM |
|
Q4_K_M (4-bit)
Recommended
GGUF k-quant
|
4.85 bpw | 8.91 GB | 1.56 GB | 11.97 GB | ~81.4 tok/s |
98.2% Sweet Spot
+0.152 ppl
|
100% GPU VRAM |
|
AWQ / GPTQ 4-bit
High Throughput
vLLM / TensorRT
|
4.50 bpw | 8.27 GB | 1.56 GB | 11.33 GB | ~87.8 tok/s |
98.0% Fast Serving
+0.160 ppl
|
100% GPU VRAM |
|
IQ4_XS (i-Matrix)
GGUF i-quant
|
4.25 bpw | 7.81 GB | 1.56 GB | 10.87 GB | ~92.9 tok/s |
97.8% Compact 4-bit
+0.180 ppl
|
100% GPU VRAM |
|
EXL2 (4.0 bpw)
Max Speed
ExLlamaV2
|
4.00 bpw | 7.35 GB | 1.56 GB | 10.41 GB | ~98.7 tok/s |
97.6% Ultra Fast
+0.195 ppl
|
100% GPU VRAM |
|
Q3_K_M (3-bit)
GGUF k-quant
|
3.91 bpw | 7.18 GB | 1.56 GB | 10.25 GB | ~101.0 tok/s |
94.5% Noticeable Loss
+0.480 ppl
|
100% GPU VRAM |
|
IQ3_XXS (3-bit)
GGUF i-quant
|
3.06 bpw | 5.62 GB | 1.56 GB | 8.69 GB | ~129.1 tok/s |
92.0% Degraded
+0.710 ppl
|
100% GPU VRAM |
|
Q2_K (2-bit)
GGUF k-quant
|
2.63 bpw | 4.83 GB | 1.56 GB | 7.90 GB | ~150.2 tok/s |
85.0% Experimental
+1.650 ppl
|
100% GPU VRAM |
|
IQ1_S (1.58-bit)
GGUF Sub-2-bit
|
1.56 bpw | 2.87 GB | 1.56 GB | 5.93 GB | ~253.2 tok/s |
72.0% Heavy Loss
+3.800 ppl
|
100% GPU VRAM |
Side-by-side Q4_K_M memory utilization and token speed across NVIDIA, Apple Silicon, AMD Radeon, and Intel Arc.
Dedicated architecture profiles with pre-computed GQA KV cache tables. Supports unlimited dynamic URLs (e.g., /model/any-model-name-70b).
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Our edge calculator models the exact memory allocation used by llama.cpp, Ollama, ExLlamaV2, and vLLM:
Weights_GB = (Params_B × Bits_Per_Weight) / 8
KV_GB = (2 × Layers × KV_Heads × Head_Dim × Context × (KV_Bits/8)) / 1024³
Speed = (Bandwidth_GBs × 0.72) / Active_Weights_GB
In full FP16/BF16 precision, Microsoft Phi-4 14B requires approximately 32.5 GB of VRAM including an 8,192-token KV cache and CUDA runtime buffer. Using 4-bit GGUF quantization (Q4_K_M at 4.85 bits per weight), total VRAM drops to 12.0 GB, while 8-bit quantization (Q8_0) requires 18.7 GB.
GGUF (used by llama.cpp, Ollama, and LM Studio) supports both pure GPU execution and hybrid CPU+GPU RAM offloading, making Q4_K_M the most versatile format. EXL2 (ExLlamaV2) runs strictly inside GPU VRAM and offers fine-grained bitrates with maximum single-user tokens/sec. AWQ (Activation-aware Weight Quantization) is optimized for high-concurrency production inference engines like vLLM and TensorRT-LLM.
Every token in your active context window stores Key and Value tensors across all transformer layers. With Grouped-Query Attention (GQA), KV cache memory equals 2 * Layers * KV_Heads * Head_Dim * Context_Tokens * Bytes_Per_Element. Quantizing the KV cache from FP16 (16-bit) to Q8_0 (8-bit) or Q4_0 (4-bit) cuts context memory overhead by 50% to 75%.
Autoregressive token generation (batch size 1) is memory-bandwidth bound. For each generated token, the GPU reads all active model weights from VRAM into Tensor Cores. Peak speed equals Memory Bandwidth (GB/s) divided by Active Model Size (GB), typically achieving 65% to 78% real-world controller efficiency.