How much VRAM does StarCoder2 15B need?

StarCoder2 15B 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 StarCoder2 15B on a consumer GPU?

Yes, StarCoder2 15B can run on consumer GPUs. At Q4_K_M quantization with moderate context length, it fits on a 24 GB GPU. For best performance, use a 24 GB or larger GPU with at least 8 GB VRAM for reasonable context lengths.

What is StarCoder2 15B's context window?

StarCoder2 15B supports a maximum context window of 16,384 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 StarCoder2 15B?

For StarCoder2 15B, start with Q4_K_M (~0.0 GB) if VRAM-limited. Q8_0 offers better quality at ~2× the VRAM. If you have enough VRAM, Q6_K (~1.5× Q4 VRAM) provides a good balance. For models this large, Q3_K_M may be necessary to fit on consumer hardware.

How does StarCoder2 15B compare to similar models?

StarCoder2 15B is a 15.0B Dense model from the BigCode family. Browse other BigCode 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.

BigCodeDenseOpenRAIL BigCode License

StarCoder2 15B

StarCoder2 15B is a dense transformer language model from the BigCode family, containing 15B parameters across 40 layers. It supports up to 16K tokens of context with a hidden dimension of 6144 and 8 KV heads for efficient grouped-query att…

15.0B

Parameters

16K

Max Context

Dense

Architecture

—

Released

Text

Modality

About StarCoder2 15B

Code

Technical Specifications

Total Parameters15.0B

ArchitectureDense

Attention TypeGQA (Grouped Query Attention)

Hidden Dimensiond = 6,144

Transformer Layers40

Attention Heads48

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	16K ctx
Q4_K_M0.50 B/W ~97% of FP16	7.91Consumer GPU	10.25Consumer GPU
Q8_01.00 B/W ~100% of FP16	15.66Consumer GPU	18.01Consumer GPU
F162.00 B/W Reference	31.17Datacenter GPU	33.51Datacenter GPU

Fits 24 GB consumer GPU

Fits 80 GB datacenter GPU

Requires cluster / multi-GPU

Other BigCode Models

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StarCoder2 7B

Params

7.0B

Layers

Context

16K

Find the right GPU for StarCoder2 15B

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

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