In the world of high-performance computing and AI, NVIDIA and AMD continue to battle for dominance. With large language models (LLMs) growing rapidly in size and complexity, benchmarking tools like DeepSeek have become essential for measuring real-world performance. Whether you’re training new models or deploying inference at scale, the choice between NVIDIA and AMD can significantly impact speed, efficiency, and cost.
This guide breaks down how NVIDIA and AMD compare in the DeepSeek benchmark, covering everything from raw performance and software support.
What is DeepSeek?
DeepSeek is a modern benchmarking suite specifically tailored for evaluating the performance of hardware running AI and machine learning workloads. Its primary focus is on inference tasks and large-scale language model (LLM) evaluations.
Core Metrics Evaluated:
- Inference Speed (tokens per second): How quickly the GPU can process input.
- Latency: Time taken to generate the first token.
- Power Efficiency: Performance per watt consumed.
- Throughput: The number of concurrent requests a GPU can handle.
- Memory Utilization: How efficiently GPU memory is allocated and used.
DeepSeek is compatible with models like DeepSeek-VL, DeepSeek-Coder, LLaMA 2/3, and other transformer-based architectures. It supports both PyTorch and TensorFlow, making it ideal for a wide range of AI practitioners.
NVIDIA GPUs for DeepSeek
NVIDIA has maintained a leadership position in the AI hardware industry through innovation in GPU architecture, extensive software support, and seamless integration with ML libraries.
Popular NVIDIA Models for AI:
- NVIDIA H100 (Hopper): Current flagship GPU, ideal for enterprise inference workloads.
- NVIDIA A100 (Ampere): Widely used in production environments.
- RTX 4090 and L40: Prosumer options with strong single-GPU performance.
Key Hardware Features:
- Tensor Cores: Provide enhanced performance for FP8/FP16 mixed-precision operations.
- NVLink & NVSwitch: Allow high-speed interconnects between multiple GPUs.
- High Memory Bandwidth: Supports fast access for large LLMs.
Software Stack:
- CUDA: Proprietary API and parallel computing platform.
- cuDNN: Deep learning acceleration library.
- TensorRT: For deploying optimized inference workloads.
DeepSeek Performance Highlights:
- H100 achieves ~30,000+ tokens/sec on DeepSeek-Coder with batch optimization.
- RTX 4090 delivers surprisingly strong results for local inference and dev workloads.
- Excellent memory and throughput management across tasks.
AMD GPUs for DeepSeek
AMD is rapidly catching up with its MI300X and MI250X accelerators designed specifically for large AI and HPC tasks. Their focus on open-source and energy efficiency has made them a strong alternative to NVIDIA.
Key AMD Models for AI:
- MI300X (CDNA 3): AMD’s most powerful AI chip to date.
- MI250X: Based on CDNA 2 architecture, used in supercomputers.
- Radeon Instinct MI100/MI200 Series: Early AI-focused accelerators.
Hardware Highlights:
- HBM3 Memory: Up to 192GB of high-speed memory.
- Infinity Fabric: High-bandwidth interconnect for scaling across GPUs.
- PCIe Gen5 Support: Faster communication with CPUs.
Software Ecosystem:
- ROCm (Radeon Open Compute): Open-source compute platform.
- MIOpen: Deep learning library akin to cuDNN.
- HIP: CUDA-compatible C++ language.
DeepSeek Benchmark Insights:
- MI300X often matches or exceeds H100 in inference performance under large batch loads.
- Power efficiency is nearly on par, though not always better.
- ROCm performance has improved significantly with PyTorch 2.x compatibility.
DeepSeek Benchmark Comparison: NVIDIA vs AMD
| Feature | NVIDIA H100 | AMD MI300X |
|---|---|---|
| Architecture | Hopper | CDNA 3 |
| Memory | 80GB HBM3 | 192GB HBM3 |
| Max Inference Speed (tokens/sec) | ~30,000+ | ~28,000–32,000 |
| Latency (first token) | Low (~1.2ms) | Moderate (~1.5–2ms) |
| Power Efficiency | Excellent | Very Good |
| Ecosystem | CUDA, TensorRT | ROCm, MIOpen |
| Software Maturity | Highly Mature | Improving Rapidly |
| Parallelism | NVLink, NVSwitch | Infinity Fabric |
| Developer Support | Extensive | Growing |
| Cloud Availability | AWS, Azure, GCP | Lambda Labs, CoreWeave |
| Price Range | $$$$ | $$$ |
Pros and Cons: NVIDIA vs AMD (DeepSeek Use Case)
| Criteria | NVIDIA (H100, A100, RTX 4090) | AMD (MI300X, MI250X) |
| Performance | Top-tier inference speeds, especially on LLMs | Competitive performance, especially with large batch sizes |
| Memory Capacity | 80GB max (H100) | Up to 192GB HBM3 (MI300X) |
| Software Support | CUDA, cuDNN, TensorRT = seamless | ROCm is improving but still behind CUDA |
| Compatibility | Excellent with all major ML tools | Good, but some libraries still catching up |
| Ecosystem Maturity | Very mature, widely adopted | Open-source but less widespread |
| Price-to-Performance | Higher cost but highly optimized | Competitive pricing with strong efficiency |
| Developer Learning Curve | Easier for most teams | Requires more customization and debugging |
| Open Source Flexibility | Proprietary stack | Fully open-source stack |
Cost and Total Ownership
- NVIDIA H100: Higher upfront and operational cost, but mature support can reduce dev overhead.
- AMD MI300X: Lower entry cost with potential savings over time, especially with memory-intensive models.
If you’re running containerized, scalable workloads, AMD could offer a better TCO (total cost of ownership). However, NVIDIA’s mature tooling can lead to quicker deployment cycles.
Conclusion: Which GPU Wins for DeepSeek?
Choosing between NVIDIA and AMD depends on your specific workload, budget, and development capabilities.
- Choose NVIDIA H100 if you need mature, stable, and plug-and-play performance with vast documentation and support.
- Choose AMD MI300X if you want cost-effective scalability, massive memory capacity, and are comfortable navigating ROCm’s learning curve.
FAQ’S
1. Is DeepSeek better than Nvidia?
DeepSeek isn’t a GPU it’s a benchmark tool used to test performance across different GPUs, including Nvidia and AMD. It doesn’t replace Nvidia but evaluates its strengths.
2. Is AMD better than Nvidia?
AMD GPUs offer great performance for the price, but Nvidia typically performs better in AI and DeepSeek benchmarks due to its advanced hardware and mature software stack.
3. Is DeepSeek 7900 XTX better than 4090?
The RX 7900 XTX delivers strong performance, but the RTX 4090 generally outperforms it in DeepSeek due to superior AI hardware like Tensor Cores and CUDA support.
4. Which GPU for DeepSeek?
The best GPU for DeepSeek is the Nvidia RTX 4090 or A100, thanks to their large VRAM, Tensor Cores, and optimized deep learning frameworks like CUDA and cuDNN.
5. Is DeepSeek a threat to Nvidia?
DeepSeek isn’t a threat itself, but if AMD consistently scores better in its benchmarks, it could shift market perception and challenge Nvidia’s dominance in AI.
6. Can a 3090 run DeepSeek?
Yes, the Nvidia RTX 3090 can run DeepSeek effectively. With 24GB of VRAM and AI-optimized hardware, it handles deep learning tasks and benchmarks without issues.
7. Is Nvidia or AMD GPU 2025?
In 2025, Nvidia leads for AI and DeepSeek workloads due to better AI integration, while AMD remains a solid option for gaming and value-focused buyers.
