How much VRAM does DeepCoder 14B need?

DeepCoder 14B requires approximately 0.0 GB VRAM at Q4_K_M quantization with 1K context and 10% overhead. At Q8_0 it needs ~0.0 GB, and at FP16 ~0.0 GB. Use the PCPARTGUIDE VRAM Calculator for exact requirements at any context length and overhead setting.

Can I run DeepCoder 14B on a consumer GPU?

Yes, DeepCoder 14B can run on consumer GPUs. At Q4_K_M quantization with moderate context length, it fits on a 12 GB GPU. For best performance, use a 16 GB or larger GPU with at least 8 GB VRAM for reasonable context lengths.

What is DeepCoder 14B's context window?

DeepCoder 14B supports a maximum context window of 32,768 tokens. The default context is 4,096 tokens. Larger contexts require proportionally more VRAM for the KV cache — use the VRAM Calculator to estimate exact requirements at your target context length.

What quantization level should I use for DeepCoder 14B?

For DeepCoder 14B, Q4_K_M is the recommended quantization — it provides an excellent quality-to-VRAM ratio at ~0.0 GB. Q8_0 offers slightly better quality at ~2× the VRAM. Q4_K_M is the sweet spot for most users.

How does DeepCoder 14B compare to similar models?

DeepCoder 14B is a 14.0B Dense model from the Coding family. Browse other Coding models on the PCPARTGUIDE directory for comparable alternatives with different parameter counts and VRAM requirements. Use the Model Transparency scores to assess documentation quality and reproducibility.

CodingDenseOpen Weights

DeepCoder 14B

DeepCoder 14B is a dense transformer language model from the Coding family, containing 14B parameters across 40 layers. It supports up to 33K tokens of context with a hidden dimension of 5120 and 8 KV heads for efficient grouped-query atten…

14.0B

Parameters

32K

Max Context

Dense

Architecture

—

Released

Text

Modality

About DeepCoder 14B

CodeReasoning

Technical Specifications

Total Parameters14.0B

ArchitectureDense

Attention TypeGQA (Grouped Query Attention)

Hidden Dimensiond = 5,120

Transformer Layers40

Attention Heads32

KV Headsn_kv = 8

Head Dimensiond_head = 128

Activation FunctionSwiGLU

NormalizationRMSNorm

Position EmbeddingRoPE

System Requirements

Estimated VRAM at 10% overhead for different quantization methods and context sizes.

Quantization	1K ctx	32K ctx
Q4_K_M0.50 B/W ~97% of FP16	7.39Consumer GPU	12.24Consumer GPU
Q8_01.00 B/W ~100% of FP16	14.63Consumer GPU	19.47Consumer GPU
F162.00 B/W Reference	29.10Datacenter GPU	33.95Datacenter GPU

Fits 24 GB consumer GPU

Fits 80 GB datacenter GPU

Requires cluster / multi-GPU

Model Transparency

Scores how well-documented and reproducible DeepCoder 14B is across 12 provenance dimensions. Higher is more transparent.

Param Density59

Tokenizer55

Dataset46

Training50

Reproducibility53

Upstream60

Architecture62

Versioning56

Hardware67

Downstream57

License60

Identity60

85+ Transparent

70+ Adequate

<70 Opaque

Find the right GPU for DeepCoder 14B

Use the interactive VRAM Calculator to see exactly how much memory you need at any quantization level, context length, and overhead setting.

Open VRAM Calculator Best GPU Guide VRAM Guide