Best Local LLMs for 8GB VRAM: Complete 2025 Performance Guide

Interactive Model Benchmark Comparison

Use the dashboard below to explore the performance of leading 8GB VRAM models across various industry-standard benchmarks. Select a metric from the buttons to update the chart, and click on model names to filter the view for a direct comparison.

Understanding the 8GB VRAM Sweet Spot: Parameter Size and Quantization

Models in the 7-9 billion parameter range hit a sweet spot for machines with 8GB of VRAM because of how weight storage, KV cache size, and quantization interact. At this scale, the model weights with 4-bit quantization fit comfortably into memory, leaving enough headroom for the attention cache required during inference.

The balance becomes especially clear when factoring in context length. At 8K tokens, the KV cache grows linearly with sequence size, so efficient use of remaining VRAM after loading model weights is critical. A quantized 7-9B model leaves approximately 2-3 GB for the cache and system overhead, which is sufficient for smooth inference without offloading to slower system RAM.

That's why this range is often considered the "best local LLM" tier for 8GB systems: it maximizes model capability while staying within practical memory limits, offering users advanced reasoning and stable long-context performance without the painful trade-offs of either too small or too large models.

2025 Benchmark Analysis: Top Performers by Category

Mathematical and Reasoning Excellence

The benchmarks split the field into clear specialists. On pure math and reasoning tests, the Qwen3 8B family (especially the reasoning variant) and DeepSeek R1 0528 Qwen3 8B sit at the top. They post the highest Artificial Analysis Math Index values, very strong MMLU-Pro (~0.74) scores, and the largest Math 500 / AIME numbers. DeepSeek's Math 500 score is outstanding, and Qwen3's reasoning variant also scores extremely high on AIME.

Coding and Development Leadership

By contrast, NVIDIA Nemotron Nano 9B (both standard and reasoning variants) emerges as the leader for coding, with a very high Artificial Analysis Coding Index and LiveCodeBench results around the 0.7 range. Most other 7-9B models (Mistral, Gemini/Gemma, Llama variants, etc.) fall into a middle or lower tier on these specific math/coding benchmarks, making them useful for general tasks but not top performers on the measured academic and coding suites.

Practical Coding Comparison: Qwen3 8B vs. DeepSeek R1 8B with Ollama

To validate benchmark results with practical applications, we conducted a hands-on comparison of the coding abilities of Qwen3 8B (Reasoning) and DeepSeek R1 8B, both available on Ollama.

Inference Testing Hardware Configurations

The 41.64 tokens/s rate on the local RTX 4060 is very impressive and sufficient for most interactive use cases. The model utilized ~5.59 GB of VRAM, very close to the value predicted by our VRAM calculator. The throughput of 28.00 tokens/s on the T4 is also respectable, though significantly less than the consumer-grade RTX 4060.

Code Quality Comparison: DeepSeek R1 vs. Qwen3

Both models delivered impressive, modern landing pages, but they showcase a classic trade-off between ambition and execution.

DeepSeek's code is more technically impressive and aligns better with the "2025 unicorn" visual trend. However, its ambition is also its downfall; the JavaScript for the interactive demo is broken, making the page feel like a beautiful but non-functional prototype. On the other hand, Qwen's page is simpler but far more practical and complete. It delivered a solid, fully-structured page that works perfectly out of the box. In short, DeepSeek produced a stunning design concept, while Qwen produced a finished, functional product.

Google Colab Setup Code for Testing

For readers interested in replicating our testing methodology, here is the complete setup code used for our Google Colab test:

!curl -fsSL https://ollama.com/install.sh | sh

!nohup ollama serve &

!ollama pull deepseek-r1:8b

!ollama run deepseek-r1:8b --verbose "Create a landing page for voice cloning ai startup. UI UX must be like a unicorn startup in 2025. Output should be a single html file."

Selection Guide: Choosing Your Optimal Model

Practically speaking, you should pick the best local LLM for your 8GB VRAM GPU by workload and practically testing a few of them for your workflows, not by a single headline score. These recommendations reflect the benchmark trade-offs shown in the data.

Memory Optimization and Deployment Best Practices

VRAM Management Strategies

Context Length Optimization

• Monitor KV cache growth with longer conversations and implement context trimming strategies for extended sessions.
• Use sliding window techniques for maintaining recent context without running out of memory.

System Configuration Optimization

Conclusion

The landscape of 8GB VRAM-optimized LLMs in 2025 offers unprecedented choice and capability for local AI deployment. The clear specialization patterns with Qwen3 8B variants leading mathematical reasoning, NVIDIA Nemotron Nano 9B excelling in coding tasks, and DeepSeek R1 models providing creative and design capabilities enable precise selection based on specific use case requirements.

Our practical testing demonstrates that these models deliver production-ready performance on consumer hardware. The choice between models should align with primary use cases: mathematical and scientific work benefits most from Qwen3 variants, software development workflows are best served by Nemotron models, and creative applications can leverage DeepSeek's innovative design capabilities. For quick setup and reliable performance across diverse tasks, the Ollama versions of deepseek-r1:8b and qwen3:8b with built-in thinking capabilities remain excellent choices.

References

Frequently Asked Questions