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Nemotron-Cascade-2-30B-A3B with Hermes Agent

Q4_K_M (GGUF) on llama.cpp commit a37cc46. Tested

Task completion
100% (30/30)
Tool-call success
81%
Decode tok/s
70.4
Prefill tok/s
3522.9
Server memory
23.6 GB
Avg turns
5

Setup

Model
Nemotron-Cascade-2-30B-A3B
Family / maker
Nemotron · NVIDIA
Parameters
30B (3B active)
Architecture
MoE
Weights
bartowski/nvidia_Nemotron-Cascade-2-30B-A3B-GGUF
Quant
Q4_K_M (GGUF)
Engine
llama.cpp commit a37cc46
Hermes Agent
v0.22.4
Tested
2026-10-23
Context
64K (65,536 tokens); long-context task t09 at 128K or 192K
Memory tier
≤24 GB (measured)

Per-task results

  • t01: 3/3
  • t02: 3/3
  • t03: 3/3
  • t04: 3/3
  • t05: 3/3
  • t06: 3/3
  • t07: 3/3
  • t08: 3/3
  • t09: 3/3
  • t10: 3/3

Copy-paste config

1. Start the model server (llama.cpp)

Exact command used in our test. Download the GGUF from bartowski/nvidia_Nemotron-Cascade-2-30B-A3B-GGUF into ./models/.

llama-server --model ./models/nvidia_Nemotron-Cascade-2-30B-A3B-Q4_K_M.gguf --alias nemotron-cascade-2-30b --host 127.0.0.1 --port 18080 --ctx-size 196608 --parallel 1 --n-gpu-layers 999 --flash-attn on --jinja

2. Point Hermes Agent at it

hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:18080/v1
hermes config set model.default nemotron-cascade-2-30b

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