VQV Signal
According to a post on X by @sinasanm, the dl harness now runs both the harness and the model locally. The dl model up command sets up an open model on the user's own GPU, then checks chat, speed, tool calls and JSON output. It can also swap in new model versions without downtime. The post reports 126 tokens per second for one request and 3,387 tokens per second across 32 requests on one RTX PRO 5000 using a 4B model.
According to a post on X by @sinasanm, the dl harness now runs both the harness and the model locally. The dl model up command sets up an open model on the user's own GPU, then checks chat, speed, tool calls and JSON output. It can also swap in new model versions without downtime. The post reports 126 tokens per second for one request and 3,387 tokens per second across 32 requests on one RTX PRO 5000 using a 4B model.
AI-assisted summary based on the listed source.
@sinasanm says the dl harness now runs both harness and model locally: dl model up sets up an open model on your own GPU, checks chat, speed, tool calls and JSON, and swaps in new versions without downtime. The post reports 126 tokens/sec for one request and 3,387 tokens/sec across 32 on one RTX PRO 5000 with a 4B...
Developers running local AI agents may find setup easier, since the tool checks key capabilities before use. Teams weighing local serving against hosted APIs should watch for independent benchmarks and wider adoption of this workflow.
Hardware and robotics watchers may want to track whether this becomes a product, benchmark, or deployment signal.
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VQV surfaced this signal because it is recent, relevant to LLM Inference, connected to Scobleizer News Pulse (X).
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