Unlocking AI Performance: Our Workstation Benchmark Insights
Curious about how a $30K-$50K AI workstation can empower teams? My team and I conducted an in-depth analysis of a dual RTX PRO 6000 setup to examine its capabilities for multi-user environments.
Key Findings:
-
Workstation Configurations:
- 2x NVIDIA RTX PRO 6000 Max-Q (192GB VRAM)
- AMD EPYC 9645 (96-core)
- 1.15TB DDR5 RAM
-
Performance Tests:
- Compared native fp8 vs. int4 quantization
- Results:
- Int4: 2-4x faster on prefill; limited to ~3 concurrent requests
- Native fp8: Scales well for 10+ users but slower overall
-
Core Metrics:
- Prefill Speed: Important for user experience
- Queue Time: Key bottleneck indicator
As we explore coding agents and larger workloads, we invite the community to share insights or suggest specific tests. Want to see how we tackle AI challenges? Let’s connect and discuss!
If you’re engaged with AI tech, drop a comment and let’s chat!
