Llmtop – Htop for LLM Inference Clusters (vLLM, SGLang, Ollama, llama)
I work on inference scheduling — KV cache-aware routing, load balancing across GPU workers, that kind of thing. I wanted something like k9s but for my inference stack. Nothing existed, so I built it. llmtop is a real-time terminal dashboard for LLM inference workers. It scrapes the Prometheus /metrics endpoints that vLLM, SGLang, and LMCache already expose and shows everything in one view: KV cache usage, queue depth, TTFT/ITL latencies (P50/P99 from histogram buckets), token throughput, prefix cache hit rates. Color-coded — red means go fix it. ``` brew install…
Llmtop – Htop for LLM Inference Clusters (vLLM, SGLang, Ollama, llama) launched on March 18, 2026 with 5 votes, #1,928 of 23,380 launches that month and more than 75% of that year's launches.
What it does
htop for your LLM inference cluster. Contribute to InfraWhisperer/llmtop development by creating an account on GitHub.
Real-time terminal dashboard for vLLM , SGLang , LMCache , NVIDIA NIM , and NVIDIA Dynamo inference clusters. go install github.com/InfraWhisperer/llmtop/cmd/llmtop@latest Quick Start # Kubernetes — auto-discovers inference pods via API server proxy llmtop # Specific namespace llmtop -n inference # Direct endpoints llmtop -e http://10.0.0.1:8000 -e http://10.0.0.2:8000 # Config file llmtop --config cluster.yaml # Snapshot mode llmtop --once --output json What It Does Real-time KV cache, queue depth, TTFT/ITL latency, token throughput across all workers GPU resource view ( g ) — utilization, VRAM, temperature, power via DCGM exporter Model-grouped view ( m ) — aggregate stats by model with…from github.com
In the maker’s words, at launch
I work on inference scheduling — KV cache-aware routing, load balancing across GPU workers, that kind of thing. I wanted something like k9s but for my inference stack. Nothing existed, so I built it. llmtop is a real-time terminal dashboard for LLM inference workers. It scrapes the Prometheus /metrics endpoints…
Does a similar job
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LLM Gateway2025 · llmgateway.io · ▲296I built an OpenAI-compatible LLM gateway that routes requests to OpenAI, Anthropic, Ollama, vLLM, llama-server, SGLang... anything that speaks /v1/chat/completions. Single Go binary, one YAML config file, no infrastructure. It does the things you'd expect from this kind of gateway... semantic routing via a three-layer cascade (keyword heuristics, embedding similarity, LLM classifier) that picks the best model when clients omit the model field, weighted round-robin load balancing across local inference servers with health checks and failover. The part I think is most…
TensorZero2025 · github.com · ▲307Hi HN! We're Gabriel & Viraj, and we're excited to open source TensorZero. To be a little cheeky, TensorZero is an open-source platform that helps LLM applications graduate from API wrappers into defensible AI products. 1. Integrate our model gateway 2. Send metrics or feedback 3. Unlock compounding improvements in quality, cost, and latency It enables a data & learning flywheel for LLMs by unifying: • Inference: one API for all LLMs, with <1ms P99 overhead • Observability: inference & feedback → your database • Optimization: better prompts, models, inference strategies • Experimentation:…
KVSplit – Run 2-3x longer contexts on Apple Silicon2025 · github.com · ▲272I discovered that in LLM inference, keys and values in the KV cache have very different quantization sensitivities. Keys need higher precision than values to maintain quality. I patched llama.cpp to enable different bit-widths for keys vs. values on Apple Silicon. The results are surprising: - K8V4 (8-bit keys, 4-bit values): 59% memory reduction with only 0.86% perplexity loss - K4V8 (4-bit keys, 8-bit values): 59% memory reduction but 6.06% perplexity loss - The configurations use the same number of bits, but K8V4 is 7× better for quality This means you can run LLMs with 2-3× longer…
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