DeepSeek-V3

Our Take

A solid 671B-parameter MoE language model from DeepSeek. Pulls ahead on grade-school math (89/100), so reach for it when that's the dimension that matters.

Generated from this model’s benchmarks and ranking signals. Editor reviews refine it over time.

In a standard dense model, every parameter is activated for every token. In DeepSeek-V3’s MoE setup, the model contains 671B total parameters, but only 37B are "active" during the forward pass. This DeepSeek-V3 MoE efficiency allows the model to deliver the reasoning capabilities of a 600B+ parameter model while maintaining the inference latency of a much smaller 37B parameter model. If you have the VRAM to house the weights, the DeepSeek-V3 tokens per second will be surprisingly high compared to dense models like Llama 3.1 405B.

DeepSeek-V3 utilizes MLA to significantly compress the KV (Key-Value) cache. In traditional Multi-Head Attention (MHA), the KV cache grows linearly with context length and model size, often becoming the primary bottleneck for long-context inference. MLA compresses the KV cache into a latent vector, reducing the memory footprint of the 128,000-token context window. This makes it feasible to run long-context queries on hardware that would otherwise run out of memory.

The model was trained on 14.8 trillion tokens with a training cutoff in early 2024. Remarkably, DeepSeek achieved this for a total training cost of approximately $5.6M, showcasing extreme algorithmic efficiency. The model supports a wide array of programming languages and demonstrates state-of-the-art performance in mathematics and logic.

DeepSeek-V3 is a general-purpose model with specific tuning for high-complexity tasks. It is not merely a chatbot; it is a functional engine for automated workflows and technical development.

• DeepSeek-V3 for Coding: The model excels at code generation, refactoring, and debugging across dozens of languages. It inherits the strengths of the DeepSeek-Coder series, making it highly effective for boilerplate generation, unit test creation, and explaining complex legacy codebases.
• Reasoning and Mathematics: In DeepSeek-V3 reasoning benchmarks, the model consistently rivals or beats GPT-4o in MATH and GSM8K datasets. This makes it ideal for local deployments involving data science, financial modeling, or architectural planning.
• Function-Calling and Tool Use: DeepSeek-V3 features robust support for function-calling, allowing it to act as an agentic core. It can parse user intent and output structured JSON to interact with external APIs, databases, or local file systems.
• Multilingual Support: While many models are English-centric, DeepSeek-V3 provides high-tier performance in Chinese, English, and several other major languages, making it a viable option for global applications.

Running a 671B parameter model locally is a significant engineering undertaking. The primary hurdle is the DeepSeek-V3 VRAM requirements. Because the model must be loaded into memory to achieve usable speeds, consumer-grade hardware requires aggressive quantization.

To determine the best GPU for DeepSeek-V3, you must first decide on your quantization level.

For most practitioners, the only way to run DeepSeek-V3 locally on consumer hardware is through a multi-GPU cluster or a high-spec Mac.

1. NVIDIA RTX 3090/4090 Clusters: You will need at least 8x 24GB GPUs to run a Q2 quantization. This requires specialized motherboards and power delivery systems.
2. Apple Silicon (Mac Studio/Mac Pro): An M2 Ultra or M3 Ultra with 192GB of Unified Memory is the most "accessible" way to run this model. While 192GB is technically short of the ~210GB needed for Q2, macOS can use swap memory to bridge the gap, though performance will degrade significantly once swapping begins.
3. Ollama: This is the quickest way to get started. By running ollama run deepseek-v3, the software will attempt to manage the memory allocation and quantization for you, though it will likely default to a heavily quantized version (IQ2_XXS) if your VRAM is limited.

For a model of this size, Q4_K_M is generally considered the "gold standard" for maintaining intelligence. However, the jump from Q2 to Q4 almost doubles the VRAM requirement. For local practitioners, Q2_K or IQ2_M is often the only realistic choice. Due to the massive parameter count, even a Q2 quantization of DeepSeek-V3 often outperforms a Q8 quantization of a 70B model.

DeepSeek-V3 occupies a unique niche as a high-parameter MoE model. Here is how it stacks up against realistic alternatives:

Llama 3.1 405B is a dense model, meaning every parameter is used for every token.

• Performance: Both models trade blows on benchmarks, with DeepSeek-V3 often leading in coding and math.
• Inference Speed: DeepSeek-V3 is significantly faster during inference because it only activates 37B parameters. Llama 405B is computationally much heavier.
• Hardware: Both require massive VRAM (350GB+ for 4-bit), but DeepSeek-V3 provides a better "intelligence-per-watt" ratio during active generation.

Qwen2.5 72B is a much smaller dense model that is easier to run on a single or dual-GPU setup.

• Performance: DeepSeek-V3 is substantially more capable in complex reasoning and long-context retrieval (128k context vs Qwen's typical performance drop-off at high context).
• Accessibility: Qwen2.5 72B can run on 2x RTX 3090s at Q4 quantization. DeepSeek-V3 requires a massive hardware investment by comparison.

DeepSeek-V3 is the local AI model 671B parameters 2025 benchmark. It is intended for users who have moved past the capabilities of 70B-class models and have the hardware infrastructure to support a massive memory footprint in exchange for frontier-level reasoning and coding performance.