Running Large Models on RTX 6000 PRO – 4‑8x 6000 PRO Experience
Benchmark your models on RTX 6000 PRO using the provided GitHub results and adjust quantization to 4‑bit for best performance.
Benchmark your models on RTX 6000 PRO using the provided GitHub results and adjust quantization to 4‑bit for best performance.
Summary
Users with 4‑8× RTX 6000 PRO GPUs, offering 384–768 GB VRAM, are exploring how larger models perform on these cards. The author asks whether GLM 5.2 can run at 4 bits (not 8 bits) and whether Kimi 2.7 or DeepSeek V4 Pro behave similarly, citing a benchmark repository on GitHub. They note that 4‑bit quantization can incur a higher performance hit for agentic or programming tasks, but the impact on larger models is unclear.
The post also inquires about backend choices, specifically vLLM or SGLang, and how they affect performance. The linked benchmark results provide throughput and latency metrics for various models on the RTX 6000 PRO. Overall, the discussion highlights the need to evaluate quantization levels and backend frameworks when scaling large models on high‑end GPUs.
Key changes
- RTX 6000 PRO GPUs provide 384–768 GB VRAM
- GLM 5.2 can run at 4 bits but not 8 bits
- Kimi 2.7 and DeepSeek V4 Pro similar quantization constraints
- 4‑bit quantization may hit performance for agentic tasks
- Backend options include vLLM and SGLang
- Benchmark repository offers throughput and latency data