{"type":"post","id":"prismml-introducing-ternary-bonsai-2-2b-4b-and-8cd13c","slug":"prismml-introducing-ternary-bonsai-2-2b-4b-and-8cd13c","title":"PrismML — Introducing Ternary Bonsai 2 2B, 4B, and 9B: Bringing Local Agents to Everyday Devices","description":"Thesis: Compact model releases make local agent deployment a memory-and-power allocation decision, but published capability retention varies by task.","retrieval_nugget":"Thesis: Compact model releases make local agent deployment a memory-and-power allocation decision, but published capability retention varies by task.","published_at":"2026-10-16","updated_at":"2026-10-16","record_date":"2026-10-16","date_kind":"published_at","topics":["local-models","agents","models"],"source_urls":["https://prismml.com/news/bonsai-2-family"],"schema_version":"newruntime-agent-readable-v0.2","stable_id":"post:prismml-introducing-ternary-bonsai-2-2b-4b-and-8cd13c","status":"published","visuals":[],"editorial_provenance":{"schema_version":"newruntime-editorial-copy-v1","content_status":"source_grounded_final","final_copy_sha256":"sha256:38be3f2748a20ea952e03c14d72742cf37beaef2520762bac2645ddf31af946d","reviewed_at":"2026-10-16T12:08:14.601Z","source_evidence_count":1,"verified_claim_count":3,"site_analysis_schema_version":"newruntime-site-analysis-v1","site_object_kind":"field_note","observed_fact_count":3,"implication_count":2,"watch_condition_count":1,"related_record_count":0},"analysis":{"schema_version":"newruntime-site-analysis-v1","object_kind":"field_note","thesis":"Compact model releases make local agent deployment a memory-and-power allocation decision, but published capability retention varies by task.","observed_facts":[{"text":"PrismML announced Bonsai 2 models at 2B, 4B, and 9B parameters with reported footprints of 0.43GB, 0.95GB, and 2GB.","source_urls":["https://prismml.com/news/bonsai-2-family"]},{"text":"PrismML reports the 9B model retains 85.7% of Qwen3.5 9B's Tau2Bench score and 83.2% of its Omni_doc score.","source_urls":["https://prismml.com/news/bonsai-2-family"]},{"text":"PrismML lists NVIDIA GPU support through llama.cpp and Apple-device support through MLX, with weights under Apache 2.0.","source_urls":["https://prismml.com/news/bonsai-2-family"]}],"mechanism":"Ternary weights restricted to −1, 0, and +1 reduce model storage, allowing smaller deployments within device memory budgets. Smaller storage can enable on-device inference, but the benchmark results show that compression does not preserve every capability equally.","why_now":"PrismML has released downloadable weights under Apache 2.0 and describes support for common NVIDIA and Apple deployment paths, making the tradeoff actionable for developers evaluating local assistants.","implications":["Teams can test whether the memory and latency benefits justify task-specific quality loss before moving sensitive or intermittent workloads on-device.","Local execution may reduce dependence on network access, but the announcement alone does not establish privacy, total energy use, or production reliability."],"evidence_boundary":"All performance figures and comparisons here are PrismML-reported; the public announcement does not provide independent validation, and results may vary by hardware, workload, and evaluation setup.","watch_conditions":["Independent replication across devices and representative tool-use workflows would show whether the claimed footprint and speed translate into reliable local agents."],"related_records":[],"new_branch_reason":"No prior New Runtime analytical record was supplied for this specific PrismML release; this starts a distinct record on compact ternary models and local agent deployment tradeoffs."},"editor":{"role":"editor","name":"Andrey Reshetnikov","profile":"https://newruntime.com/owner-profile.md","profile_json":"https://newruntime.com/owner-profile.json","same_as":["https://www.linkedin.com/in/reshetnikov1/","https://t.me/lapetuse"]},"routes":{"html":"https://newruntime.com/posts/prismml-introducing-ternary-bonsai-2-2b-4b-and-8cd13c/","markdown":"https://newruntime.com/posts/prismml-introducing-ternary-bonsai-2-2b-4b-and-8cd13c.md","json":"https://newruntime.com/posts/prismml-introducing-ternary-bonsai-2-2b-4b-and-8cd13c.json"},"next_reads":[{"type":"topic","path":"/topics/local-models/","url":"https://newruntime.com/topics/local-models/","title":"Local Models","reason":"Explore the local-models topic hub.","media_type":"text/html","markdown_url":"https://newruntime.com/topics/local-models.md","json_url":"https://newruntime.com/topics/local-models.json"},{"type":"topic","path":"/topics/agents/","url":"https://newruntime.com/topics/agents/","title":"Agents","reason":"Explore the agents topic hub.","media_type":"text/html","markdown_url":"https://newruntime.com/topics/agents.md","json_url":"https://newruntime.com/topics/agents.json"},{"type":"topic","path":"/topics/models/","url":"https://newruntime.com/topics/models/","title":"Models","reason":"Explore the models topic hub.","media_type":"text/html","markdown_url":"https://newruntime.com/topics/models.md","json_url":"https://newruntime.com/topics/models.json"},{"type":"collection","path":"/posts/","url":"https://newruntime.com/posts/","title":"Field Notes","reason":"Open the latest editorial analysis.","media_type":"text/html","json_url":"https://newruntime.com/posts.json"},{"type":"collection","path":"/source-ledger.json","url":"https://newruntime.com/source-ledger.json","title":"Source Ledger","reason":"Inspect the public source evidence graph.","media_type":"application/json"}]}