BenHamm

Privacy Policy — Race Mode

Last updated: 28 July 2026

Race Mode does not collect, transmit, store or share any personal information.

What the extension does

Race Mode runs entirely inside your browser. When you are on youtube.com it reads the text of video titles, description snippets and search suggestions already on

BenHamm

Privacy Policy — Race Mode

Last updated: 28 July 2026

Race Mode does not collect, transmit, store or share any personal information.

What the extension does

Race Mode runs entirely inside your browser. When you are on youtube.com it reads the text of video titles, description snippets and search suggestions already on

BenHamm

Scraping SemiAnalysis InferenceX results programmatically

InferenceX is SemiAnalysis's public dashboard of NVIDIA-vs-AMD LLM inference benchmarks (throughput, accuracy evals, GPU reliability). The front end is a JavaScript single-page app, so it looks like you'd need a headless browser — but the whole dashboard is backed by three plain, unauthenticated JSON endpoints. No API key, no browser, no scraping of rendered HTML. Just HTTP GET.

This gist documents those endpoints and ships a dependency-free Python client

BenHamm

Benchmarking multi-turn LLM workloads with AIPerf

Goal: Simulate 100 multi-turn conversations averaging 12 turns each, with 60K avg ISL / 1K avg OSL, hitting 95% KV cache reuse — a large shared system prompt across all conversations plus accumulating turn-over-turn history.

This is a useful pattern for stress-testing KV-aware routing in disaggregated inference, prefix-cache hit rates, and goodput under realistic agentic-conversation load. It's also the rough shape of real coding-agent traffic (Claude Code, Cursor, Cline, etc.) — long shared system prompt + turn-over-turn growth.

The command

aiperf profile \

BenHamm

Deployment Guide

Prerequisites

• Kubernetes cluster with NVIDIA GPU nodes
• kubectl configured and authenticated
• GPU with ≥16 GB VRAM (A100, H100, B200, etc.)

1. Create Namespace and Storage

BenHamm

SGLang B200 Benchmark Sweep — 9 Models on 8×B200

Date: 2026-02-15/16
Hardware: 8× NVIDIA B200 183GB, NV18 NVSwitch, Xeon 8570 (224 cores), 2TB RAM
GPU Clocks: SM 1965 MHz, MEM 3996 MHz (locked at max)
Driver: 570.195.03
Framework: SGLang v0.5.8.post1 (Docker lmsysorg/sglang:v0.5.8.post1)
Benchmark: sglang.bench_serving, random 1K input / 1K output tokens
OS: Ubuntu 24.04.3 LTS

BenHamm

Context Management Rules for Long-Running AI Sub-Agents

When an AI agent runs long tasks (benchmarks, deployments, CI pipelines), context overflow is the #1 failure mode. Compaction only runs between turns — if a single turn accumulates too much tool output, the agent dies with no recovery.

The Problem

A sub-agent doing GPU benchmarks might chain tool calls like:

1. docker pull → 200 lines of layer progress
2. docker logs → 500 lines of model loading / warmup
3. bench_serving → 1000+ lines of progress bars

BenHamm

OpenClaw - GLM-5-FP8 Inference Benchmark Report: 8×B200 vs 8×H200

Date: 2026-02-13
Model: zai-org/GLM-5-FP8 (744B MoE, 40B active parameters)
Framework: SGLang (lmsysorg/sglang:glm5-blackwell)
Hardware: 8× NVIDIA B200 183GB, NV18 NVSwitch, Xeon 8570 (224 cores), 2TB RAM
Reference: SGLang GLM-5 Cookbook (8×H200 141GB) AI Agent: Claude-4.6-Opus Running on OpenClaw

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BenHamm

Qwen3-32B Disaggregated Serving Benchmark Results

Date: December 17, 2024
Model: Qwen/Qwen3-32B-FP8
Cluster: Nebius H200 (16 GPUs)
Framework: TensorRT-LLM via Dynamo

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1. Cluster Configuration

BenHamm

AIConfigurator: Fast-Track Your LLM Deployment on NVIDIA Dynamo

What is NVIDIA Dynamo?

NVIDIA Dynamo is a high-throughput, low-latency inference framework for serving generative AI models across multi-node GPU clusters. As LLMs grow beyond what a single GPU can handle, Dynamo solves the orchestration challenge of coordinating shards, routing requests, and transferring KV cache data across distributed systems.

Key capabilities:

• Disaggregated serving — Separates prefill and decode phases for optimized GPU utilization
• KV-aware routing — Routes requests to workers with the highest cache hit rate
• KV Block Manager — Offloads KV cache to CPU, SSD, or remote memory (G2/G3/G4) for higher throughput