Starcoder2 15B — Hardware Requirements & GPU Compatibility
ChatCode
Starcoder2 15B is a 16.0B-parameter open language model from BigCode in the StarCoder family. It supports a context window of up to 16,384 tokens. At Q4_K_M it needs about 10.04 GB of VRAM — see which GPUs and Macs can run it below.
10.2K downloads
672 likesSpecifications
- Publisher
- BigCode
- Family
- StarCoder
- Parameters
- 16.0B
- Architecture
- Starcoder2ForCausalLM
- Context Length
- 16,384 tokens
- Vocabulary Size
- 49,152
- Release Date
- 2024-02-20
- License
- bigcode-openrail-m
Get Started
HuggingFace
How Much VRAM Does Starcoder2 15B Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 7.3 GB | 8.4 GB | 6.78 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 7.5 GB | 8.6 GB | 6.98 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 8.3 GB | 9.4 GB | 7.78 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 8.4 GB | 9.6 GB | 7.98 GB | 4-bit legacy quantization |
| Q4_K_M | 4.80 | 10.0 GB | 11.2 GB | 9.57 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 11.8 GB | 13.0 GB | 11.37 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 13.6 GB | 14.8 GB | 13.17 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 16.4 GB | 17.6 GB | 15.96 GB | 8-bit quantization, near-lossless |
est.= calculated VRAM estimate; no published GGUF file found for that quantization yet. Other rows are verified against real community uploads.
Which GPUs Can Run Starcoder2 15B?
Q4_K_M · 10.0 GBStarcoder2 15B (Q4_K_M) requires 10.0 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 14+ GB is recommended. Using the full 16K context window can add up to 1.2 GB, bringing total usage to 11.2 GB. 37 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 3080 Ti.
Runs great
— Plenty of headroom
Decent
— Enough VRAM, may be tightWhich Devices Can Run Starcoder2 15B?
Q4_K_M · 10.0 GB48 devices with unified memory can run Starcoder2 15B, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, NVIDIA Jetson Orin NX 16GB.
Runs great
— Plenty of headroomDecent
— Enough memory, may be tightWhere to Download Starcoder2 15B
Community quantizations of this model — GGUF for llama.cpp, Ollama, and LM Studio, plus AWQ/MLX variants where available.
Benchmarks
Benchmark details →Related Models
Frequently Asked Questions
- How much VRAM does Starcoder2 15B need?
Starcoder2 15B requires 10.0 GB of VRAM at Q4_K_M, or 32.4 GB at BF16. Full 16K context adds up to 1.2 GB (11.2 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 16.0B × 4.8 bits ÷ 8 = 9.6 GB
KV Cache + Overhead ≈ 0.4 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 1.6 GB (at full 16K context)
VRAM usage by quantization
Q4_K_M10.0 GBQ4_K_M + full context11.2 GB- Can NVIDIA GeForce RTX 4090 run Starcoder2 15B?
Yes, at Q8_0 (16.4 GB) or lower. Higher quantizations like BF16 (32.4 GB) exceed the NVIDIA GeForce RTX 4090's 24 GB.
- What's the best quantization for Starcoder2 15B?
For Starcoder2 15B, Q4_K_M (10.0 GB) offers the best balance of quality and VRAM usage. Q5_0 (10.4 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 7.3 GB.
VRAM requirement by quantization
Q2_K7.3 GBQ4_08.4 GBQ4_K_M ★10.0 GBQ5_010.4 GBQ5_K_M11.8 GBBF1632.4 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Starcoder2 15B on a Mac?
Starcoder2 15B requires at least 7.3 GB at Q2_K, which exceeds the unified memory of most consumer Macs. You would need a Mac Studio or Mac Pro with a high-memory configuration.
- Can I run Starcoder2 15B locally?
Yes — Starcoder2 15B can run locally on consumer hardware. At Q4_K_M quantization it needs 10.0 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Starcoder2 15B?
At Q4_K_M, Starcoder2 15B can reach ~438 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~65 tok/s. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: NVIDIA B200 → 8000 ÷ 10.0 × 0.65 = ~518 tok/s
Estimated speed at Q4_K_M (10.0 GB)
~518 tok/s~65 tok/s~518 tok/s~438 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Starcoder2 15B?
At Q4_K_M, the download is about 9.57 GB. The full-precision BF16 version is 31.92 GB. The smallest option (Q2_K) is 6.78 GB.
- Which GPUs can run Starcoder2 15B?
37 consumer GPUs can run Starcoder2 15B at Q4_K_M (10.0 GB). Top options include AMD Radeon RX 6800, AMD Radeon RX 6800 XT, AMD Radeon RX 6900 XT, AMD Radeon RX 6700 XT. 26 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Starcoder2 15B?
52 devices with unified memory can run Starcoder2 15B at Q4_K_M (10.0 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Apple iPhone 17 Pro, Asus ROG Flow Z13 (2025, Ryzen AI Max+ 395, 128 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.