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AI Code Generator, Cheap GPU Servers for Code LLMs
Discover the power of our AI Code Generator and GPU servers designed for code LLMs. Enhance your coding efficiency and performance today.
Cheap GPU Servers for AI Code Generator
We offer cost-effective NVIDIA GPU optimized servers for coding large language models (LLMs).
Flash Sale to June 16
Professional GPU VPS - A4000
- 32GB RAM
- 24 CPU Cores
- 320GB SSD
- 300Mbps Unmetered Bandwidth
- Once per 2 Weeks Backup
- OS: Linux / Windows 10/ Windows 11
- Dedicated GPU: Quadro RTX A4000
- CUDA Cores: 6,144
- Tensor Cores: 192
- GPU Memory: 16GB GDDR6
- FP32 Performance: 19.2 TFLOPS
Advanced GPU Dedicated Server - V100
- 128GB RAM
- Dual 12-Core E5-2690v3
- 240GB SSD + 2TB SSD
- 100Mbps-1Gbps
- OS: Windows / Linux
- GPU: Nvidia V100
- Microarchitecture: Volta
- CUDA Cores: 5,120
- Tensor Cores: 640
- GPU Memory: 16GB HBM2
- FP32 Performance: 14 TFLOPS
Advanced GPU Dedicated Server - A5000
- 128GB RAM
- Dual 12-Core E5-2697v2
- 240GB SSD + 2TB SSD
- 100Mbps-1Gbps
- OS: Windows / Linux
- GPU: Nvidia Quadro RTX A5000
- Microarchitecture: Ampere
- CUDA Cores: 8192
- Tensor Cores: 256
- GPU Memory: 24GB GDDR6
- FP32 Performance: 27.8 TFLOPS
Enterprise GPU Dedicated Server - RTX A6000
- 256GB RAM
- Dual 18-Core E5-2697v4
- 240GB SSD + 2TB NVMe + 8TB SATA
- 100Mbps-1Gbps
- OS: Windows / Linux
- GPU: Nvidia Quadro RTX A6000
- Microarchitecture: Ampere
- CUDA Cores: 10,752
- Tensor Cores: 336
- GPU Memory: 48GB GDDR6
- FP32 Performance: 38.71 TFLOPS
Enterprise GPU Dedicated Server - RTX 4090
- 256GB RAM
- Dual 18-Core E5-2697v4
- 240GB SSD + 2TB NVMe + 8TB SATA
- 100Mbps-1Gbps
- OS: Windows / Linux
- GPU: GeForce RTX 4090
- Microarchitecture: Ada Lovelace
- CUDA Cores: 16,384
- Tensor Cores: 512
- GPU Memory: 24 GB GDDR6X
- FP32 Performance: 82.6 TFLOPS
Enterprise GPU Dedicated Server - A40
- 256GB RAM
- Dual 18-Core E5-2697v4
- 240GB SSD + 2TB NVMe + 8TB SATA
- 100Mbps-1Gbps
- OS: Windows / Linux
- GPU: Nvidia A40
- Microarchitecture: Ampere
- CUDA Cores: 10,752
- Tensor Cores: 336
- GPU Memory: 48GB GDDR6
- FP32 Performance: 37.48 TFLOPS
Flash sale to June 16
Enterprise GPU Dedicated Server - A100
- 256GB RAM
- Dual 18-Core E5-2697v4
- 240GB SSD + 2TB NVMe + 8TB SATA
- 100Mbps-1Gbps
- OS: Windows / Linux
- GPU: Nvidia A100
- Microarchitecture: Ampere
- CUDA Cores: 6912
- Tensor Cores: 432
- GPU Memory: 40GB HBM2
- FP32 Performance: 19.5 TFLOPS
New Arrival
Enterprise GPU Dedicated Server - A100(80GB)
- 256GB RAM
- Dual 18-Core E5-2697v4
- 240GB SSD + 2TB NVMe + 8TB SATA
- 100Mbps-1Gbps
- OS: Windows / Linux
- GPU: Nvidia A100
- Microarchitecture: Ampere
- CUDA Cores: 6912
- Tensor Cores: 432
- GPU Memory: 80GB HBM2e
- FP32 Performance: 19.5 TFLOPS
Enterprise GPU Dedicated Server - H100
- 256GB RAM
- Dual 18-Core E5-2697v4
- 240GB SSD + 2TB NVMe + 8TB SATA
- 100Mbps-1Gbps
- OS: Windows / Linux
- GPU: Nvidia H100
- Microarchitecture: Hopper
- CUDA Cores: 14,592
- Tensor Cores: 456
- GPU Memory: 80GB HBM2e
- FP32 Performance: 183TFLOPS
Multi-GPU Dedicated Server - 2xA100
- 256GB RAM
- Dual 18-Core E5-2697v4
- 240GB SSD + 2TB NVMe + 8TB SATA
- 1Gbps
- OS: Windows / Linux
- GPU: Nvidia A100
- Microarchitecture: Ampere
- CUDA Cores: 6912
- Tensor Cores: 432
- GPU Memory: 40GB HBM2
- FP32 Performance: 19.5 TFLOPS
- Free NVLink Included
Multi-GPU Dedicated Server - 4xA100
- 512GB RAM
- Dual 22-Core E5-2699v4
- 240GB SSD + 4TB NVMe + 16TB SATA
- 1Gbps
- OS: Windows / Linux
- GPU: 4 x Nvidia A100
- Microarchitecture: Ampere
- CUDA Cores: 6912
- Tensor Cores: 432
- GPU Memory: 40GB HBM2
- FP32 Performance: 19.5 TFLOPS
Multi-GPU Dedicated Server- 2xRTX 4090
- 256GB RAM
- Dual 18-Core E5-2697v4
- 240GB SSD + 2TB NVMe + 8TB SATA
- 1Gbps
- OS: Windows / Linux
- GPU: 2 x GeForce RTX 4090
- Microarchitecture: Ada Lovelace
- CUDA Cores: 16,384
- Tensor Cores: 512
- GPU Memory: 24 GB GDDR6X
- FP32 Performance: 82.6 TFLOPS
New Arrival
Multi-GPU Dedicated Server- 2xRTX 5090
- 256GB RAM
- Dual Gold 6148
- 240GB SSD + 2TB NVMe + 8TB SATA
- 1Gbps
- OS: Windows / Linux
- GPU: 2 x GeForce RTX 5090
- Microarchitecture: Ada Lovelace
- CUDA Cores: 20,480
- Tensor Cores: 680
- GPU Memory: 32 GB GDDR7
- FP32 Performance: 109.7 TFLOPS
Why Choose our AI Code LLMs Hosting
Database Mart enables powerful GPU hosting features on raw bare metal hardware, served on-demand. No more inefficiency, noisy neighbors, or complex pricing calculators.
Premium Hardware
Our GPU dedicated servers and VPS are equipped with high-quality NVIDIA graphics cards, efficient Intel CPUs, pure SSD storage, and renowned memory brands such as Samsung and Hynix.
Full Root/Admin Access
With full root/admin access, you will be able to take full control of your dedicated GPU servers for deep learning very easily and quickly.
99.9% Uptime Guarantee
With enterprise-class data centers and infrastructure, we provide a 99.9% uptime guarantee for hosted GPUs for deep learning and networks.
Dedicated IP
One of the premium features is the dedicated IP address. Even the cheapest GPU dedicated hosting plan is fully packed with dedicated IPv4 & IPv6 Internet protocols.
24/7/365 Free Expert Support
Our dedicated support team is comprised of experienced professionals. From initial deployment to ongoing maintenance and troubleshooting, we're here to provide the assistance you need, whenever you need it, without extra fee.
How to Choose the Best Coding LLMs
Here’s a detailed comparison of the strongest open-source coding large language models (LLMs) as of 2025: CodeGemma vs StarCoder2 vs DeepSeek-Coder V2 vs CodeLLaMA vs Codestral:
🔍 Quick Overview
| Model | Params | Context Window | Code Languages | License | Notable Strengths |
|---|---|---|---|---|---|
| CodeGemma | 2B / 7B | 8K tokens | Python, C++, etc. | Apache 2.0 | Lightweight, fast, Google-backed |
| StarCoder2 | 3B / 7B / 15B | 16K tokens | 600+ languages | BigCode (open) | Fully open, rich plugin ecosystem |
| DeepSeek-Coder V2 | 7B / 33B / 100B | 16K tokens | Multilingual (EN + CN) | DeepSeek (open) | Dual-language support, top-tier coding ability |
| CodeLLaMA | 7B / 13B / 34B / 70B | 16K+ | Multi-language | Meta (open) | Great for finetuning, popular base model |
| Codestral | 22B | 32K tokens | 80+ languages | MNPL (non-commercial) | SOTA-level performance, FIM support |
🧠 Performance Benchmarks (HumanEval / MBPP / FIM)
| Model | HumanEval (Pass@1) | MBPP | FIM Support | Comment |
|---|---|---|---|---|
| Codestral 22B | ~78-82% (SOTA) | ✅ | ✅ Yes | Among top open models, long context |
| DeepSeek-Coder V2 (33B) | ~76% | ✅ | ✅ Yes | Near GPT-4 level in some tests |
| StarCoder2 (15B) | ~65-70% | ✅ | ✅ Yes | Versatile, high multilingual coverage |
| CodeLLaMA 70B | ~70% | ✅ | ❌ Partial | Great base model, often used in finetuning |
| CodeGemma 7B | ~60-65% | ❌ Limited | ❌ No | Best for edge/local use, lightweight |
✅ Pros & Cons Summary
| Model | Pros | Cons |
|---|---|---|
| CodeGemma | Fast, small size, ideal for real-time coding & edge deployment | Lower coding performance compared to others |
| StarCoder2 | Full open-source, strong community, supports many languages | Moderate performance ceiling |
| DeepSeek V2 | Excellent bilingual performance (English + Chinese), high accuracy | 33B and 100B models require strong hardware |
| CodeLLaMA | Strong as base model for finetuning and instruction tuning | Needs finetuning to perform well in specific tasks |
| Codestral | State-of-the-art code performance, long context, Fill-in-the-middle | Non-commercial license (MNPL), not usable in production |
FAQs of GPU Servers for Code LLMs
Here’s a Frequently Asked Questions guide specifically for using GPU servers to run Code LLMs:
What are Code LLMs, and why do they need GPUs?
Code LLMs (like StarCoder2, DeepSeek-Coder, CodeLLaMA, etc.) are large language models fine-tuned for code generation, understanding, and completion. They often contain billions of parameters, which require GPU acceleration for fast inference and training.
Which frameworks are commonly used for inference?
🤖 vLLM – Fast transformer engine for serving LLMs
🧱 Transformers (HF) – Popular for research and standard deployment
🧠 llama.cpp – CPU/GPU inference for quantized GGUF models
🧪 Text Generation Web UI / Open WebUI – UI-based inference and testing
🧱 Transformers (HF) – Popular for research and standard deployment
🧠 llama.cpp – CPU/GPU inference for quantized GGUF models
🧪 Text Generation Web UI / Open WebUI – UI-based inference and testing
What tasks can I use Code LLMs for on a GPU server?
1. Code completion (like Copilot)
2. Bug fixing / debugging suggestions
3. Code summarization / explanation
4. Test case generation
5. Code translation (e.g., Python → Java)
6. Chat-style programming agents
2. Bug fixing / debugging suggestions
3. Code summarization / explanation
4. Test case generation
5. Code translation (e.g., Python → Java)
6. Chat-style programming agents
Can I use these models interactively like ChatGPT?
Yes. Combine the LLM with a chat interface using:
1. Open WebUI
2. LangChain / LlamaIndex + Gradio
3. Ollama or LM Studio (for local GUI chat)
1. Open WebUI
2. LangChain / LlamaIndex + Gradio
3. Ollama or LM Studio (for local GUI chat)
What’s the cheapest way to run Code LLMs?
Use GGUF quantized models with llama.cpp (runs on lower VRAM)
1. Choose spot instances or shared GPU platforms
2. Run 7B models locally on a 16–24 GB GPU
3. Use dedicated small GPU servers from hosting providers
1. Choose spot instances or shared GPU platforms
2. Run 7B models locally on a 16–24 GB GPU
3. Use dedicated small GPU servers from hosting providers
What GPU specifications are best for serving Code LLMs?
For optimal performance with Code LLMs, consider GPUs with:
• High memory capacity (24GB+ for models up to 70B parameters)
• Tensor cores (4th gen Hopper architecture preferred)
• High memory bandwidth (HBM3 in data-center GPUs like H100)
• Multi-GPU configurations for larger models
• High memory capacity (24GB+ for models up to 70B parameters)
• Tensor cores (4th gen Hopper architecture preferred)
• High memory bandwidth (HBM3 in data-center GPUs like H100)
• Multi-GPU configurations for larger models
How can I improve inference speed for Code LLMs?
Key optimization techniques include:
• Quantization: 4-bit AWQ or GPTQ quantization can reduce memory requirements by 4x while maintaining accuracy
• Efficient scheduling: Use systems like Sarathi-Serve that implement chunked prefilling to reduce latency by 2.6-5.6x compared to vLLM
• Memory optimization: Techniques like paged attention (vLLM) and activation offloading can handle larger contexts 18
• Quantization: 4-bit AWQ or GPTQ quantization can reduce memory requirements by 4x while maintaining accuracy
• Efficient scheduling: Use systems like Sarathi-Serve that implement chunked prefilling to reduce latency by 2.6-5.6x compared to vLLM
• Memory optimization: Techniques like paged attention (vLLM) and activation offloading can handle larger contexts 18