Which AI Models Can I Run on an NVIDIA RTX 6000 Ada GPU?

Vishnu Subramanian

Founder @JarvisLabs.ai

The NVIDIA RTX 6000 Ada can comfortably run most models up to 13B parameters at full precision, and larger models (30B-70B) with appropriate quantization. With 48GB VRAM and excellent performance-to-cost ratio, it's ideal for startups and researchers who need more power than consumer GPUs without paying premium H100/A100 prices.

RTX 6000 Ada Specifications

The RTX 6000 Ada sits in the sweet spot between consumer GPUs and data center cards, offering:

• VRAM: 48GB GDDR6 memory (same capacity as A6000, but faster)
• Compute: 91.1 TFLOPS FP32 performance (33% faster than previous generation)
• Memory Bandwidth: 960 GB/s (significantly better than consumer RTX cards)
• Architecture: Ada Lovelace with 4th-gen Tensor Cores
• Price: ₹80.19/hour (approximately $0.99/hour) on JarvisLabs.ai

These specs make it particularly well-suited for deploying mid-sized LLMs and image generation models.

Model Compatibility Table

Here's what you can realistically run on the RTX 6000 Ada:

Performance Insights

Having run extensive benchmarks on our RTX 6000 Ada fleet at Javis Labs, I can share some real-world performance data:

• Llama 2 7B: ~115 tokens/second for generation (about 4x faster than RTX 3090)
• Stable Diffusion XL: ~7 seconds per image at 1024x1024 (compared to ~12 seconds on A5000)
• Mixtral 8x7B (4-bit): ~45 tokens/second (impressive for a 47B parameter model)

The RTX 6000 Ada shows particularly strong performance on transformer-based models thanks to its 4th-gen Tensor Cores, which are optimized for the matrix multiplications that dominate LLM workloads.

Memory Management Strategies

To maximize your RTX 6000 Ada's capabilities:

1. Quantization is your friend: Libraries like bitsandbytes, AutoGPTQ, and ExLlama make 4-bit and 8-bit quantization straightforward with minimal quality loss.

2. Consider payload size: Remember to account for context window requirements. A 7B model with 32K context will need more VRAM than one limited to 4K tokens.

3. Optimize attention mechanisms: For very large contexts, techniques like Flash Attention can reduce memory usage by up to 20%.

4. Offload when necessary: CPU offloading for specific model layers can allow you to run even larger models, albeit with a performance hit.

Cost-Effectiveness Analysis

At ₹80.19/hour (approximately $0.99/hour), the RTX 6000 Ada delivers exceptional value:

• vs. A100: The A100 (₹104.49/hour) is about 30% more expensive but doesn't always deliver 30% better performance for mid-sized models
• vs. H100: The H100 (₹242.19/hour) is nearly 3x the price and while significantly faster, the RTX 6000 Ada still wins on price/performance for many workloads
• vs. A6000: Similar VRAM (48GB) but the RTX 6000 Ada offers better performance at a slightly higher price point

I've found that for many startups and research teams, the RTX 6000 Ada hits the perfect balance between capability and cost.

When to Choose RTX 6000 Ada

Based on my experience bootstrapping Javis Labs and working with hundreds of AI teams, I recommend the RTX 6000 Ada when:

• You need to run models larger than what fits on consumer GPUs (RTX 4090's 24GB)
• You're fine-tuning models in the 7B-13B range
• You're deploying inference for multiple smaller models simultaneously
• You need better performance than A5000/A6000 but can't justify A100/H100 prices
• Your batch sizes are modest (1-8) for inference workloads

When to Look Elsewhere

Consider alternatives when:

• You absolutely need to run 70B+ models at full precision (consider H100/H200)
• You're training very large models from scratch (multiple A100s/H100s would be better)
• Your workload is extremely latency-sensitive and budget isn't a concern
• You need to process very large batch sizes

My Recommendation

Having bootstrapped Javis Labs without VC funding, I'm particularly sensitive to maximizing GPU value. The RTX 6000 Ada has become my go-to recommendation for teams building AI products with mid-sized models.

For most startups implementing LLM-based features, the ability to run multiple 7B or 13B models—or a single quantized 70B model—is more than sufficient for MVP and even production deployments. The cost savings compared to premium GPUs can extend your runway significantly.

What specific model are you looking to deploy? I can help think through the memory requirements and performance expectations for your particular use case.