Upgrade Announcement: Our Cloud GPU Servers Now Run on NVIDIA RTX 6000 Blackwell

Does your model need more GPU memory than a 48 GB card offers? The Managed GPU Server 96 provides a dedicated NVIDIA RTX PRO 6000 Blackwell Max-Q with 96 GB, including an agreed model, vLLM, Open WebUI, and 24/7 monitoring in a German data center. €1,799 net per month plus a one-time €999 setup fee. View Managed GPU Server · Book a call

As of October 2026: At the time of this switch the offer was called AI Server Pro; today it is called Managed GPU Server 96. Price and scope in this post reflect the current offer.

In the Managed GPU Server 96 we no longer use the RTX 6000 Ada but the NVIDIA RTX PRO 6000 Blackwell Max-Q. This provides significantly more headroom for large models, longer contexts, and higher throughput requirements.

Table of Contents

• Key Changes at a Glance
• Why This Step
• Performance: Blackwell vs. Ada
• What 96 GB VRAM Enables
• What Stays the Same
• Availability

Key Changes at a Glance

1) Significantly More VRAM: 96 GB Instead of 48 GB

The Managed GPU Server 96 comes with 96 GB GDDR7 VRAM (ECC) in the Blackwell generation; the RTX 6000 Ada had 48 GB GDDR6 (ECC). This is the most important change for LLMs, because VRAM in practice determines model size, context length, batch size, and parallelism.

2) New Architecture (Blackwell) and New Core Generations

The RTX PRO 6000 Blackwell series brings Blackwell architecture, 5th-Gen Tensor Cores, and 4th-Gen RT Cores. The RTX 6000 Ada is based on Ada Lovelace with 4th-Gen Tensor Cores and 3rd-Gen RT Cores. For AI workloads, the jump in Tensor Cores is particularly relevant.

3) Platform Modernization: PCIe Gen 5 vs. PCIe Gen 4

The RTX PRO 6000 Blackwell variants are designed for PCIe Gen 5 x16, the RTX 6000 Ada for PCIe Gen 4 x16. This can provide additional reserves depending on workload (e.g., data streaming, multi-node pipelines).

4) Max-Q: Density & Efficiency for Scalable Setups

The Managed GPU Server 96 uses the Max-Q variant. NVIDIA explicitly positions it for dense configurations and as a balance of performance and energy efficiency, i.e. for setups that optimize performance per rack and server.

Why This Step: Larger Models, Longer Contexts, More Concurrent Users

In practice, we see three typical bottlenecks in productive LLM applications:

1. VRAM Limit – Model doesn't fit completely in the GPU
2. Time-to-First-Token – Prompt processing / prefill is too slow
3. Throughput – Token generation under load is too low

With Blackwell, we address exactly these points: more VRAM for large models/long context and noticeably more performance in both prompt processing and token generation.

Performance: Blackwell vs. Ada (LLM Inference Benchmarks)

For external, verifiable context, we use measurements from Hardware Corner. They test with llama.cpp / llama-bench on Ubuntu 24.04 and CUDA 12.8; token generation is in tokens/second, at 4-bit quantization (Q4_K_XL).

Token Generation at 16K Context

Source: Hardware Corner Token Generation Table (16K Context)

Important for practice: The larger the model and context, the more Blackwell pays off. At 65K context, you see nearly a doubling (or more) of token generation in several cases – e.g., Qwen3 14B +123%, Qwen3 32B +173%.

Prompt Processing (Time-to-First-Token)

Prefill/prompt processing is often the underestimated factor. At 16K context:

This is particularly relevant for RAG (long prompts), tool use, or multi-turn chats – because it directly affects perceived responsiveness.

What 96 GB VRAM Enables for You

The biggest jump isn't "just" faster – but more possible on a single GPU:

• 100B+ class without offloading / without multi-GPU as requirement: The 96 GB class is the practical single-GPU option when models in the 100B+ parameter range should run without system RAM offloading.
• With Blackwell, large models like GPT-OSS 120B or Mistral Large 2 (123B) run directly on a single GPU. The newer Mistral Large 3, by contrast, is a mixture-of-experts model with 675 billion parameters (41 billion active) and does not fit into 96 GB.

Comparison: What Fits on Which GPU?

What Stays the Same: Your Stack, Your Control, Your Location

The GPU change doesn't affect the basic principles of our Managed GPU Servers:

• Server location Germany: a dedicated server in a German data center. External APIs, telemetry, and backup targets are agreed as separate data paths.
• Dedicated GPU resources without sharing: one dedicated NVIDIA GPU per server
• Agreed model, vLLM, and Open WebUI are included in the monthly price
• Managed operations: proactive 24/7 monitoring, OS, driver, vLLM, and Open WebUI updates with CVE assessment, incident handling according to the service level
• The 96 GB leave room for selected fine-tuning methods; scope and architecture are agreed per project

Note on Benchmark Transferability

The values quoted above come from a clearly defined test setup (llama.cpp/llama-bench, CUDA 12.8, 4-bit quantization). In production environments (e.g., vLLM, other quantizations, batch settings, KV cache strategies), absolute numbers may vary. The Max-Q variant also runs at a lower power draw than the Workstation Edition. The direction (more VRAM and a significant performance jump) remains consistent.

Availability & Next Step

The Managed GPU Server 96 with NVIDIA RTX PRO 6000 Blackwell Max-Q (96 GB GDDR7 ECC) costs €1,799 net per month plus a one-time €999 net setup fee. For more compact models, the Managed GPU Server 24 with an NVIDIA RTX PRO 4000 Blackwell SFF Edition (24 GB GDDR7 ECC) costs €699 net per month plus €499 setup. Host system, term, and provisioning are confirmed in the proposal.

If you already have a specific goal (e.g., "70B chat with 32K context and X concurrent users" or "RAG with large document corpus"), we check before provisioning whether 24 GB, 96 GB, or a custom architecture fits.

View Managed GPU Server · Book a call

Further Guides

• AI overview
• GPU Server for AI
• AI Cube as a local AI server
• Ollama vs vLLM Comparison
• Run GPT-OSS 120B on the AI Cube
• GDPR-Compliant AI Inference