# Lambda GPU cloud

Published articles for Lambda GPU cloud.

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## Open Jarvis: making local LLMs work as agents

DevFeed: [Open Jarvis: making local LLMs work as agents](<https://devfeed.tech/articles/open-jarvis-making-local-llms-work-as-agents-118905.md>)

Original publisher: [Read original article](<https://lambda.com/blog/open-jarvis-local-llm-agents>)

Author: caia.costello@lambdal.com (Caia Costello)

Published: 2026-09-30T14:49:50Z

Content type: article

Language: en

Sources: [The Lambda deep learning blog](<https://devfeed.tech/sources/the-lambda-deep-learning-blog.md>)

Topics: [Agent Harness](<https://devfeed.tech/topics/agent-harness.md>), [Local AI](<https://devfeed.tech/topics/local-ai.md>), [LLM optimization](<https://devfeed.tech/topics/llm-optimization.md>), [llama.cpp](<https://devfeed.tech/topics/llama-cpp.md>), [Ollama](<https://devfeed.tech/topics/ollama.md>)

Tags: [agent](<https://devfeed.tech/tags/agent.md>), [agent-harness](<https://devfeed.tech/tags/agent-harness.md>), [agent-harnesses](<https://devfeed.tech/tags/agent-harnesses.md>), [agent-tools](<https://devfeed.tech/tags/agent-tools.md>), [agentic](<https://devfeed.tech/tags/agentic.md>), [agentic-ai](<https://devfeed.tech/tags/agentic-ai.md>), [agents](<https://devfeed.tech/tags/agents.md>), [ai](<https://devfeed.tech/tags/ai.md>), [ai-benchmarks](<https://devfeed.tech/tags/ai-benchmarks.md>), [api](<https://devfeed.tech/tags/api.md>), [benchmark](<https://devfeed.tech/tags/benchmark.md>), [benchmarks](<https://devfeed.tech/tags/benchmarks.md>), [code](<https://devfeed.tech/tags/code.md>), [code-execution](<https://devfeed.tech/tags/code-execution.md>), [config](<https://devfeed.tech/tags/config.md>), [configuration](<https://devfeed.tech/tags/configuration.md>), [control](<https://devfeed.tech/tags/control.md>), [cost](<https://devfeed.tech/tags/cost.md>), [harness](<https://devfeed.tech/tags/harness.md>), [kimi-k2-6](<https://devfeed.tech/tags/kimi-k2-6.md>), [lambda-gpu-cloud](<https://devfeed.tech/tags/lambda-gpu-cloud.md>), [llm-agents](<https://devfeed.tech/tags/llm-agents.md>), [llms](<https://devfeed.tech/tags/llms.md>), [local](<https://devfeed.tech/tags/local.md>), [local-inference](<https://devfeed.tech/tags/local-inference.md>), [local-llms](<https://devfeed.tech/tags/local-llms.md>), [open-weight-models](<https://devfeed.tech/tags/open-weight-models.md>), [pinchbench](<https://devfeed.tech/tags/pinchbench.md>)

### AI overview

Open Jarvis argues that an agent's harness must be retuned for its model and hardware: prompts and tool strategies built for cloud models can limit open-weight models. Its spec separates model settings, inference engine, agent logic, tools and memory, and learning; automated search uses a frontier model to diagnose failures and accept improvements across four editable areas. On PinchBench, retargeting a Qwen3.5-9B harness raised accuracy from 62.3% to 88.4%, recovering 77% of the gap to Claude Opus 4.6. The excerpt also reports broader local-model results, but notes a workload-specific tradeoff: on 75 shared PinchBench tasks using the native OpenHands harness, Kimi K2.6 scored below Claude Fable 5.1 (0.7301 vs. 0.8216) while estimated cost was about 34% lower ($2.07 vs. $3.14 per task). Self-hosting may suit workloads where Kimi's quality meets requirements; the excerpt provides setup commands for trying Open Jarvis.

### Source excerpt

Running an open-weight model is only half the job. The other half is the harness: what tells the model what to do, which tools it can use, and how to learn from its mistakes.

## Open Jarvis: making local LLMs work as agents

DevFeed: [Open Jarvis: making local LLMs work as agents](<https://devfeed.tech/articles/open-jarvis-making-local-llms-work-as-agents-84859.md>)

Original publisher: [Read original article](<https://lambda.ai/blog/open-jarvis-local-llm-agents>)

Author: caia.costello@lambdal.com (Caia Costello)

Published: 2026-09-30T14:49:50Z

Content type: article

Language: en

Sources: [The Lambda deep learning blog](<https://devfeed.tech/sources/the-lambda-deep-learning-blog.md>)

Topics: [Local AI](<https://devfeed.tech/topics/local-ai.md>), [Agent Harness](<https://devfeed.tech/topics/agent-harness.md>), [agentic-coding](<https://devfeed.tech/topics/agentic-coding.md>), [OpenClaw](<https://devfeed.tech/topics/openclaw.md>)

Tags: [agent-harness](<https://devfeed.tech/tags/agent-harness.md>), [agent-harnesses](<https://devfeed.tech/tags/agent-harnesses.md>), [agent-tools](<https://devfeed.tech/tags/agent-tools.md>), [agentic-ai](<https://devfeed.tech/tags/agentic-ai.md>), [agents](<https://devfeed.tech/tags/agents.md>), [ai](<https://devfeed.tech/tags/ai.md>), [code](<https://devfeed.tech/tags/code.md>), [kimi-k2-6](<https://devfeed.tech/tags/kimi-k2-6.md>), [lambda-gpu-cloud](<https://devfeed.tech/tags/lambda-gpu-cloud.md>), [llm-agents](<https://devfeed.tech/tags/llm-agents.md>), [local-inference](<https://devfeed.tech/tags/local-inference.md>), [local-llms](<https://devfeed.tech/tags/local-llms.md>), [open-weight-models](<https://devfeed.tech/tags/open-weight-models.md>), [pinchbench](<https://devfeed.tech/tags/pinchbench.md>)

### AI overview

Open Jarvis retunes an AI agent harness--including its model, inference engine, agent logic, tools, and memory--for a particular local model and machine. The article reports that this recovered much of the accuracy lost when replacing a cloud model with a smaller open-weight model on PinchBench, and describes harness configuration and optimization as key to making local models useful for agentic tasks. It also presents Lambda infrastructure benchmarks and cost comparisons, with results that vary by model, benchmark, and workload.

### Source excerpt

Running an open-weight model is only half the job. The other half is the harness: what tells the model what to do, which tools it can use, and how to learn from its mistakes.