FitMyLLM
BigCode/Dense

BigCodeStarCoder2 15B

StarCoder2 15B — top open code model for its size, extensive language support.

coding
15.96B
Parameters
16K
Context length
9
Benchmarks
10
Quantizations
0
Architecture
Dense
Released
2024-02-28
Layers
40
KV Heads
4
Head Dim
128
Family
starcoder

Quantization Options

Context length:
QuantBitsVRAM @ 16KQuality
Q3_K_M4
9.4 GB
8.5 + 0.9 KV
low
Q3_K_L4.3
10.0 GB
9.1 + 0.9 KV
moderate
IQ4_XS4.46
10.3 GB
9.4 + 0.9 KV
moderate
Q4_K_S4.67
10.7 GB
9.8 + 0.9 KV
moderate
Q4_K_M4.89
11.2 GB
10.2 + 0.9 KV
good
Q5_K_S5.57
12.5 GB
11.6 + 0.9 KV
good
Q5_K_M5.7
12.8 GB
11.9 + 0.9 KV
good
Q6_K6.56
14.5 GB
13.6 + 0.9 KV
excellent
Q8_08.5
18.4 GB
17.4 + 0.9 KV
lossless
FP1616
33.3 GB
32.4 + 0.9 KV
lossless

Select your GPU above to see speed estimates and compatibility for each quantization.

Deploying for a team or in production? Size GPUs, cost & scaling in Enterprise →
READY TO RUN THIS?RENT BY THE HOUR

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Spin up an A100 / H100 / 4090 in ~60s. Pay by the second. Cancel anytime.

Community Ratings

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Benchmarks (9)

MBPP65.1
HumanEval60.4
BigCodeBench37.6
IFEval27.8
BBH20.4
MMLU-PRO15.0
MATH6.0
GPQA3.1
MUSR2.9

Run this model

Easiest way to get started·Beginners
DOCS ↗
curl -fsSL https://ollama.com/install.sh | sh
$ollama run starcoder2:15b-q4_K_M

Downloads and runs automatically. Add --verbose for speed stats.

▸ SETUP GUIDE
>_

Auto-setup with fitmyllm CLI

Detects your GPU, recommends the best model, downloads it, and starts chatting — zero config. Benchmarks your speed and contributes anonymous data to improve predictions.

pip install fitmyllmthen run fitmyllmLearn more
Auto-detect GPULive tok/s in chatSpeed benchmarks9 inference engines

GPUs that can run this model

At Q4_K_M quantization. Sorted by minimum VRAM.

NVIDIA Tesla K40c
12 GB VRAM • 288 GB/s
NVIDIA
NVIDIA Tesla K40d
12 GB VRAM • 288 GB/s
NVIDIA
NVIDIA Tesla K40m
12 GB VRAM • 288 GB/s
NVIDIA
NVIDIA Tesla K40s
12 GB VRAM • 288 GB/s
NVIDIA
NVIDIA Tesla K40st
12 GB VRAM • 288 GB/s
NVIDIA
NVIDIA Tesla K40t
12 GB VRAM • 288 GB/s
NVIDIA
NVIDIA Tesla K80
12 GB VRAM • 241 GB/s
NVIDIA
NVIDIA Tesla M40
12 GB VRAM • 288 GB/s
NVIDIA
NVIDIA Tesla P100 PCIe 12 GB
12 GB VRAM • 549 GB/s
NVIDIA
NVIDIA RTX A2000 12 GB
12 GB VRAM • 288 GB/s
NVIDIA
$550

Find the best GPU for StarCoder2 15B

Build Hardware for StarCoder2 15B

StarCoder2 15B — top open code model for its size, extensive language support.

▸ SPEC SHEET

StarCoder2 15B15.96B Dense.

▸ SPECIFICATIONS
PARAMETERS
15.96B
ARCHITECTURE
Dense Transformer
CONTEXT LENGTH
16K tokens
CAPABILITIES
coding
RELEASE DATE
2024-02-28
PROVIDER
BigCode
FAMILY
starcoder
▸ VRAM REQUIREMENTS
QUANTBPWVRAMQUALITY
Q3_K_M48.5 GB88%
Q3_K_L4.39.1 GB90%
IQ4_XS4.469.4 GB92%
Q4_K_S4.679.8 GB93%
Q4_K_M4.8910.2 GB94%
Q5_K_S5.5711.6 GB96%
Q5_K_M5.711.9 GB96%
Q6_K6.5613.6 GB97%
Q8_08.517.4 GB100%
FP161632.4 GB100%
§ 01BENCHMARK SCORES
HumanEval60.4
MMLU-PRO15.0
MATH6.0
IFEval27.8
BBH20.4
GPQA3.1
MUSR2.9
MBPP65.1
BigCodeBench37.6
§ 02RUN COMMAND

Run StarCoder2 15B locally with Ollama — needs 10.2 GB VRAM at Q4_K_M:

$ollama run starcoder2:15b
§ 03COMPATIBLE GPUs
30 @ Q4_K_M