NVIDIA releases Nemotron 3.5 Bolt E4B 4GB, a small open model post trained from Google's Gemma 4 E4B for local voice assistants, game characters and robots
Available now: NVIDIA Nemotron 3.5 Bolt E4B 4GB ⚡️ Our newest small open model is built for local assistants, interactive characters and robots that can carry on a conversation and use tools to get things done. Watch Reachy Mini chat and move, with Bolt handling conversation and NVIDIA Cosmos handling vision. Both models run locally on Jetson Orin Nano 8GB. Learn more:
Context
It targets agents that must run on small local hardware such as an 8GB Jetson board, and its quality and token savings figures come from NVIDIA's own benchmarks.
NVIDIA's release page, dated October 23, says Bolt E4B builds on Gemma 4 E4B with Nemotron post training and fits in about 4GB in its target workloads. NVIDIA compares it mainly with its prior small model, Nemotron 3 Nano 4B. The X post was published at 11:41 AM Eastern on October 23.
Deep dive
NVIDIA calls Bolt E4B an open small language model for voice and device assistants, interactive characters and robotics or IoT interfaces on GeForce RTX, Jetson, RTX Spark and DGX Spark hardware. Listed features include multi turn tool use, a conversational mode for short speech ready replies, reasoning that can be switched on or off, a 64K token context and support for 35 languages.
NVIDIA reports about 40% higher average scores than Nemotron 3 Nano 4B and about 10% higher than other peers across its chosen benchmarks, and up to about 80% fewer output tokens in reasoning comparisons. Its tables are mixed: Bolt E4B leads Qwen 3.5 4B on IHEval, 84.8 against 60.6, and on RoleBench, but trails it on Tau2 Bench, 55.1 against 75.1, and on BFCL v3 and RULER at 64K. These are NVIDIA's measurements, and two of the benchmarks have not yet been released, NVIDIA says.
On a Jetson Orin Nano 8GB, NVIDIA lists 4.2GB of memory, about 5.8 seconds to first token for a 3K token prompt and 15.5 tokens per second, using 4 bit Q4_K_M quantization. On an RTX 5070 it lists 548 milliseconds and 133.4 tokens per second. The release also includes a DFlash drafter that NVIDIA says gives up to 21% more throughput than the original Gemma 4 E4B drafter.
In the video, a Reachy Mini robot chats with a user while, according to NVIDIA, Cosmos supplies vision and Bolt E4B triggers animations, with both models running locally on the Jetson.
What to expect next
NVIDIA points to a technical report, a model card, deployment snippets and open training recipes. The license, which builds on a Gemma base model, was not checked in the sources reviewed for this entry. Watch for independent tests of the speed and quality claims on Jetson hardware, release of the Brevity Bench and When2Call MT benchmarks, and use in robots and games.