Starcoder2 7B — Hardware Requirements & GPU Compatibility
ChatCode
Starcoder2 7B is a 7.2B-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 4.74 GB of VRAM — see which GPUs and Macs can run it below.
19.8K downloads
210 likesSpecifications
- Publisher
- BigCode
- Family
- StarCoder
- Parameters
- 7.2B
- Architecture
- Starcoder2ForCausalLM
- Context Length
- 16,384 tokens
- Vocabulary Size
- 49,152
- Release Date
- 2024-06-11
- License
- bigcode-openrail-m
Get Started
HuggingFace
How Much VRAM Does Starcoder2 7B Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 3.5 GB | 4.4 GB | 3.05 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 3.6 GB | 4.5 GB | 3.14 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 3.9 GB | 4.9 GB | 3.50 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 4.0 GB | 5.0 GB | 3.59 GB | 4-bit legacy quantization |
| Q4_K_M | 4.80 | 4.7 GB | 5.7 GB | 4.30 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 5.5 GB | 6.5 GB | 5.11 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 6.3 GB | 7.3 GB | 5.92 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 7.6 GB | 8.6 GB | 7.17 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run Starcoder2 7B?
Q4_K_M · 4.7 GBStarcoder2 7B (Q4_K_M) requires 4.7 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 7+ GB is recommended. Using the full 16K context window can add up to 0.9 GB, bringing total usage to 5.7 GB. 35 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroomWhich Devices Can Run Starcoder2 7B?
Q4_K_M · 4.7 GB33 devices with unified memory can run Starcoder2 7B, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomBenchmarks
View all 2 →Related Models
Derivatives (1)
Frequently Asked Questions
- How much VRAM does Starcoder2 7B need?
Starcoder2 7B requires 4.7 GB of VRAM at Q4_K_M, or 7.6 GB at Q8_0. Full 16K context adds up to 0.9 GB (5.7 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 7.2B × 4.8 bits ÷ 8 = 4.3 GB
KV Cache + Overhead ≈ 0.4 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 1.4 GB (at full 16K context)
VRAM usage by quantization
Q4_K_M4.7 GBQ4_K_M + full context5.7 GB- What's the best quantization for Starcoder2 7B?
For Starcoder2 7B, Q4_K_M (4.7 GB) offers the best balance of quality and VRAM usage. Q5_0 (4.9 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 3.5 GB.
VRAM requirement by quantization
Q2_K3.5 GBQ4_04.0 GBQ4_K_M ★4.7 GBQ5_04.9 GBQ5_K_S5.4 GBQ8_07.6 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Starcoder2 7B on a Mac?
Starcoder2 7B requires at least 3.5 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 7B locally?
Yes — Starcoder2 7B can run locally on consumer hardware. At Q4_K_M quantization it needs 4.7 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Starcoder2 7B?
At Q4_K_M, Starcoder2 7B can reach ~615 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~138 tok/s. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: AMD Instinct MI300X → 5300 ÷ 4.7 × 0.55 = ~615 tok/s
Estimated speed at Q4_K_M (4.7 GB)
~615 tok/s~138 tok/s~460 tok/s~380 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Starcoder2 7B?
At Q4_K_M, the download is about 4.30 GB. The full-precision Q8_0 version is 7.17 GB. The smallest option (Q2_K) is 3.05 GB.
- Which GPUs can run Starcoder2 7B?
35 consumer GPUs can run Starcoder2 7B at Q4_K_M (4.7 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT. 35 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Starcoder2 7B?
33 devices with unified memory can run Starcoder2 7B at Q4_K_M (4.7 GB), including Mac Mini M4 (16 GB), Mac Mini M4 (32 GB), Mac Mini M4 Pro (24 GB), Mac Mini M4 Pro (48 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.