Graphsignal @GraphsignalAI
Inference Observability graphsignal.com San Francisco, CA Joined March 2019-
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@GraphsignalAI More on how Graphsignal uses dstack across on-prem and GPU cloud for development and inference: dstack.ai/blog/graphsign…
New: a case study on how @GraphsignalAI uses dstack for development and inference benchmarking. Graphsignal builds tooling to profile model inference, and uses dstack across a fleet of @nvidia DGX Spark devices and @verdacloud to keep the workflow consistent across on-prem and cloud: dstack.ai/blog/graphsign…
@dstackai And we also rely on @dstackai for development and testing of our SDK: github.com/graphsignal/gr…. Can’t wait to share more in another post!
autodebug by @GraphsignalAI is a closed-loop system for inference optimization. It uses @dstackai to provision GPUs and redeploy services on each pass through the loop: benchmark → read profiling telemetry → tweak config → redeploy → repeat. What's interesting here is the combination of agentic optimization and heterogeneous hardware: the system is not tuning a fixed deployment, it is continuously searching across infrastructure and configuration. There's no manual step between iterations. @dmitrimelikyan's writeup: graphsignal.com/blog/autodebug…
@dstackai and here is the repo github.com/graphsignal/au…, inspired by @karpathy authoresearch
autodebug: an autonomous loop that deploys an inference service, benchmarks it, reads profiling telemetry, and redeploys with a better config. Then repeats. Uses @GraphsignalAI for inference profiling, @dstackai for GPU provisioning, Claude Code as the agent. graphsignal.com/blog/autodebug… github.com/graphsignal/au…
Config tuning is just the start. The same loop can optimize inference code and even custom CUDA kernels. It all depends on what tools the agent can use.
autodebug: an autonomous loop that deploys an inference service, benchmarks it, reads profiling telemetry, and redeploys with a better config. Then repeats. Uses @GraphsignalAI for inference profiling, @dstackai for GPU provisioning, Claude Code as the agent.
Agent orchestration is evolving fast! Agents + orchestration + telemetry → closed-loop systems. Our friends at GraphSignal show how this unlocks continuous inference optimization in production — across heterogeneous hardware. This is where things get interesting.
autodebug: an autonomous loop that deploys an inference service, benchmarks it, reads profiling telemetry, and redeploys with a better config. Then repeats. Uses @GraphsignalAI for inference profiling, @dstackai for GPU provisioning, Claude Code as the agent.
Now @GraphsignalAI integrates with dstack — add @sgl_project profiling, tracing, and GPU metrics to your inference services. pip install 'graphsignal[cu12]' + wrap with graphsignal-run. That's it. graphsignal.com/docs/integrati…
New post: AI Debugging and Optimization For Production Inference graphsignal.com/blog/ai-debugg… Use Claude Code to debug and optimize AI systems with rich production context from Graphsignal
New post: Traditional Observability Is Blind to Inference graphsignal.com/blog/tradition…
New post: vLLM production observability - from model to hardware. graphsignal.com/blog/vllm-prod…
Fresh dev setup on #dgx_spark running as a @dstackai fleet⚡️
LLM API Latency Optimization Explained graphsignal.com/blog/llm-api-l…
berlin done, munich next! good crew coming too:
here's the final lineup for @airstreet munich ai meetup on 25th feb! - @cedapprox, director of ai @helsingai (defense) - @fabianjakobi, ceo of @interloom (agents) - @tuschermarc, cto of @sereactai (embodied ai) - frederik chettouh, ai pm at @celonis (automation)
YES, you need to see the prompts! Great article by @HamelHusain hamel.dev/blog/posts/pro…
Learn how to measure and analyze LLM streaming performance using time-to-first-token metrics and traces ➡️ graphsignal.com/blog/measuring…
#AI observability is evolving. Today's tools not only monitor AI performance but also unravel complex model behaviors, enhancing transparency and reliability.
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