Opus 4.6 Audit Insights (2026-03-16)

• openai/gpt-oss-120b は Hugging Face で実在を確認。117B MoE（アクティブ 5.1B）、MXFP4 量子化、Apache 2.0 ライセンス。
• アクティブパラメータが 5.1B と小さいため、1051 tok/s のピーク性能は妥当。
• vLLM 0.11.0、CUDA 13.0 の記述は実在するバージョン。
• 96GB VRAM で 128K コンテキスト + 20 並列ユーザーでもメモリスワップなしに動作するという報告は、MXFP4 量子化の恩恵。

RTX Pro 6000 Blackwell vLLM Benchmark: 120B Model Performance Analysis Unverified Community Insight – Reddit Score: 173 Upvotes

This post from the r/LocalLLaMA community presents a detailed performance profile of a 120-billion-parameter model running on NVIDIA’s RTX Pro 6000 Blackwell workstation, using vLLM 0.11.0. With 96GB of VRAM, the system demonstrates compelling results for large-scale local inference. While unconfirmed by official benchmarks, the data comes from a reputed local AI enthusiast and carries 89 comments discussing its practical implications — suggesting a high level of community engagement and perceived credibility.

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Hardware & Software Setup

• GPU: NVIDIA RTX Pro 6000 Blackwell Workstation Edition (96GB VRAM)
• CUDA: 13.0
• Driver: 580.82.09
• Framework: vLLM 0.11.0
• Model: openai/gpt-oss-120b (120B parameters)
• Source: Hugging Face – gpt-oss-120b

The benchmark was executed using the vllm-benchmark-suite, a community-driven tool for evaluating LLM inference performance under varying workloads.

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Test Scenarios

Two primary test profiles were evaluated:

1. Short Output (500 tokens)
   • Context lengths: 1K–128K
   • Concurrency: 1–20 users
2. Extended Output (1000–2000 tokens)
   • Same context and concurrency ranges

Performance was measured in tokens per second (tok/s), with attention to throughput, latency, power consumption, and batch efficiency.

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Key Findings

Peak Performance (500-token output):

• 1051 tok/s achieved at 1 user with 1K context
• 300–476 tok/s sustained across 20 concurrent users, regardless of context length
• TTFT (Time to First Token): 200–400ms at low concurrency; increases to 2000–3000ms at 20 users
• Average latency: 2.6 seconds (1 user) → 30.2 seconds (20 users) at 128K context

These results indicate strong linear scaling up to moderate concurrency, with predictable latency growth under load.

Extended Output (1000–2000 tokens):

• 1016 tok/s peak throughput — only a minor (~3%) drop from 500-token output
• Higher latencies due to longer decoding phases
• Power draw: 300–600W depending on system load
• Batch scaling efficiency: Excellent at 2–5 users; remains usable up to 10 users

The model demonstrates stable performance even under extended token generation, with minimal efficiency loss.

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Observations on Blackwell Architecture

• Linear scaling observed up to approximately 5 concurrent users
• GPU clock stability: Maintained at 2800+ MHz under sustained load
• Inter-token latency: Remains sub-50ms (“INSTANT”) for most configurations
• Context length behavior: Throughput roughly halves with every 32K increase in context length

The 96GB VRAM capacity proves sufficient to avoid memory swapping, even at maximum context (128K) and concurrency.

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TL;DR

For users running 100B+ parameter models locally, the RTX Pro 6000 Blackwell delivers production-grade throughput with excellent multi-user scalability. While power consumption is moderate (300–600W), the system’s compute density and VRAM headroom make it a compelling choice for enterprise-grade local inference workloads — especially when paired with vLLM’s efficient attention implementation.

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Note: This analysis is based on unverified community data from r/LocalLLaMA (173 upvotes, 89 comments). Always validate performance with your own hardware and software stack.