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Diffusion Intelligence releases Clover-35B-A3B-Preview, a 35-billion-parameter diffusion language model
Diffusion Intelligence releases the diffusion language model Clover-35B-A3B-Preview, which uses a MoE architecture with 35 billion total parameters and about 3 billion activated per token, and supports a thinking mode. On the 49 tasks of the web Agent benchmark WindTunnel, its median execution time was 1.52 seconds, lower than the 3.21 seconds of the Jev + Mercury 2.5 combination, with success rates of 98.6% and 95.9% respectively. On code benchmarks, it scored 73.6 on SWE-bench Verified and 51.7 on Terminal-Bench 2.1, and the team said it will subsequently launch a multimodal version and advance on-device adaptation.
Diffusion Intelligence releases the diffusion language model Clover-35B-A3B-Preview, which uses a MoE architecture with 35 billion total parameters and about 3 billion activated per token, and supports a thinking mode. On the 49 tasks of the web Agent benchmark WindTunnel, its median execution time was 1.52 seconds, lower than the 3.21 seconds of the Jev + Mercury 2.5 combination, with success rates of 98.6% and 95.9% respectively. On code benchmarks, it scored 73.6 on SWE-bench Verified and 51.
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NVIDIA releases Nemotron-3.5-Bolt-E4B, which can run locally on 6GB of memory
NVIDIA releases Nemotron-3.5-Bolt-E4B, which can run locally on devices with 6GB of memory, targeting on-device devices such as phones. The model is trained based on Gemma 4, supports vision, and Unsloth also provides GGUF files and a guide link. NVIDIA says it targets scenarios such as local assistants, interactive characters, and robots that can converse and call tools.
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