StarCoder2 15B

15B

BigCode

BigCode's open code model trained on 600+ languages. Great for polyglot dev.

Consumer GPU

16K

Max Context

2

Quant Variants

GGUF Q4_K_M

Best Quality

96.8%

Accuracy Retained

Calculate VRAMHugging FaceCompare

Quantization Variants

Per-quant VRAM, quality loss, and inference speed on RTX 4090

Measured = site benchmarks · Estimated = formula · Community = public reports

FormatLevelBPWVRAMPPL LossSpeedSourceActions
GGUFQ4_K_M4.8510.5 GB3.2%92 tok/sEstimated
CalcHF
GPTQINT449.2 GB4.8%115 tok/sEstimated
CalcHF

Similar models

Compare with Qwen2.5 14B
14B

Qwen2.5 14B Instruct

Alibaba Qwen2.5

Consumer GPUMac / Apple Silicon
9.2 GBmin VRAM·98.6%accuracy

The sweet spot between performance and resource usage. 16GB VRAM with Q4.

16B

DeepSeek-Coder-V2-Lite Instruct

DeepSeek

Consumer GPUMac / Apple Silicon
9.8 GBmin VRAM·99.8%accuracy

MoE architecture coding model. Active params ~2.4B, total ~16B. Exceptional code quality.

14B

DeepSeek-R1-Distill-Qwen-14B

DeepSeek

Consumer GPU
9.2 GBmin VRAM·98.0%accuracy

R1 reasoning distilled into 14B. Huge community interest; excellent chain-of-thought.

14B

Phi-3 Medium 14B Instruct

Microsoft Phi

Consumer GPUMac / Apple Silicon
8.8 GBmin VRAM·99.2%accuracy

Microsoft's mid-size Phi-3. Excellent quality-per-GB on 16GB cards.