StarCoder 2 7B — VRAM, Speed & Local Setup

Name: LLM Configurator — GPU VRAM Checker
Author: LLM Configurator

Written by Jakub Rusinowski · Last updated February 28, 2024

Model library → StarCoder 2 → StarCoder 2 7B

The sweet spot for code generation. Strong multi-language support, good at completing complex functions. Works well on 8GB VRAM GPUs as a lightweight coding assistant.

StarCoder 2 7B needs about 5 GB of VRAM at Q4_K_M — quantized weights plus framework overhead, before any KV cache. On Apple Silicon that figure comes out of unified memory.

Specifications

Parameters	7 Billion
Context window	16,384
Architecture	Dense
Provider	BigCode
Licence	BigCode Open RAIL-M v1
Specified at	Q4_K_M
System RAM	16 GB
Record updated	2024-02-28

Licence

BigCode OpenRAIL-M — commercial use permitted. Weights are downloadable and commercial use is permitted, subject to the licence’s acceptable-use terms.

VRAM and Speed by Quantization

Modelled on a reference NVIDIA RTX 4090 (24 GB), at 8K context. Speed figures are ESTIMATES from the memory-bandwidth roofline described on the methodology page, not benchmarks we ran — rows marked measured come from published or reader-submitted runs. VRAM here includes the KV cache, so it reads higher than the headline figure above, which does not.

Quant	Weights	VRAM needed	Est. speed	Fit on 24 GB
Q2_K	2.4 GB	3.7 GB	~174 tok/s (est.)	Fits comfortably
Q3_K_M	3.1 GB	4.4 GB	~150 tok/s (est.)	Fits comfortably
Q4_K_M	4.3 GB	5.7 GB	~120 tok/s (est.)	Fits comfortably
Q5_K_M	5.1 GB	6.4 GB	~107 tok/s (est.)	Fits comfortably
Q6_K	5.9 GB	7.2 GB	~96 tok/s (est.)	Fits comfortably
Q8_0	7.7 GB	9.0 GB	~79 tok/s (est.)	Fits comfortably
F16	14.4 GB	15.7 GB	~46 tok/s (est.)	Fits comfortably

Want the memory numbers alone, at every quantization level and your own context length? Use the StarCoder 2 7B VRAM calculator.

Buy This HardwareIntel Arc B570 10GB — 10 GB VRAM · 150 W board power Deploy in the Cloud NowRTX 4090 on RunPod — from $0.34/hr · rate checked 2026-07

or compare on Vast.ai from $0.35/hr (typical low · varies)

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Recommended GPU

The cheapest catalogued GPU that runs StarCoder 2 7B is the Intel Arc B570 (10 GB).

Affiliate disclosure: Some links on this page are affiliate links — if you buy through them, LLM Configurator may earn a commission at no extra cost to you. As an Amazon Associate, LLM Configurator earns from qualifying purchases.

Intel Arc B570 10GB

10 GB VRAM · 150 W board power

2026 prices are volatile — check the current listing.

Check price on Amazon

How to Run StarCoder 2 7B

Install Ollama, then run:

ollama run starcoder2:7b

Weights on Hugging Face: bigcode/starcoder2-7b.

Best for: code completion, coding, multi language.

Can I Run StarCoder 2 7B on My GPU?

Other StarCoder 2 Sizes

StarCoder 2 3B — about 3 GB VRAM at Q4_K_M
StarCoder 2 15B — about 10 GB VRAM at Q4_K_M

StarCoder 2 7B — Frequently Asked Questions

How much VRAM does StarCoder 2 7B need?

About 5 GB at Q4_K_M — quantized weights plus framework overhead, before any KV cache. The cache grows with context length and is added on top; the table above folds it in. Apple Silicon counts unified memory toward the same figure.

Does StarCoder 2 7B run on an RTX 4090 (24 GB)?

Yes. StarCoder 2 7B needs about 5 GB at Q4_K_M, inside a 24 GB card, at an estimated 120 tokens/sec.

How do I run StarCoder 2 7B locally?

Install Ollama and run `ollama run starcoder2:7b`. That pulls the weights and starts a local OpenAI-compatible endpoint; after the download nothing leaves the machine.

What other sizes does StarCoder 2 come in?

StarCoder 2 3B (3 GB), StarCoder 2 7B (5 GB), StarCoder 2 15B (10 GB). Every size shares the family's training and licence; the larger ones score higher and need proportionally more memory.

← All StarCoder 2 models | VRAM calculator | Check your own hardware