Laguna S 2.1 vs Nemotron 3 Ultra 550B A55B

Laguna S 2.1

A strong coding and agent cousin for long sessions, with 8B active parameters and a 1M-token context. At 4-bit it needs a 96 to 128 GB machine, tight on a 128 GB one. The main risks are that nobody at Poolside answers its many reports, there are no safety results, and a build card misdescribes its file.

Nemotron 3 Ultra 550B A55B

NVIDIA's frontier-scale agent cousin with trained parallel tool calls, up to a 1M-token context and training against prompt injection through tool output. It needs at least four Blackwell GPUs for the 352 GB NVFP4 build, so most agents will use NVIDIA's API or a provider. The main risks are serving commands that run repository code and no published safety results.

Laguna S 2.1

1. Quickest start is ollama run laguna-s-2.1, 96 GB for the default Q4_K_M, or the 66 GB nvfp4 tag on Apple Silicon
2. On our reference machine try llama-server -hf poolside/Laguna-S-2.1-GGUF:Q4_K_M --jinja -c 65536, from a llama.cpp release with the laguna architecture (b11772 has it). The 96 GB file leaves little room for context in 107.4 GB
3. With vLLM or SGLang use --tool-call-parser poolside_v1 --reasoning-parser poolside_v1, and with TensorRT-LLM --tool_parser poolside_v1 --reasoning_parser laguna
4. Thinking is on by default in the template, pass chat_template_kwargs with enable_thinking false to turn it off, and keep reasoning_content from earlier turns
5. The GGUFs ship set to 262,144 tokens. For 1M with llama.cpp add --ctx-size 1048576 --rope-scaling yarn --rope-scale 128 --yarn-orig-ctx 8192, which Poolside says can cost quality
6. Sampling per the generation config is temperature 1.0 and top_k 20, and Poolside suggests temperature 0.7 and top_p 0.95 at 1M
7. Expect occasional tool-schema slips in harnesses unlike Poolside's own, and validate JSON arguments, which Poolside lists as known limitations

Nemotron 3 Ultra 550B A55B

1. It doesn't fit the 128 GB reference machine at any precision. Call NVIDIA's API (model nvidia/nemotron-3-ultra-550b-a55b) or a provider instead
2. With vLLM serve the NVFP4 build with --trust-remote-code --reasoning-parser nemotron_v3 --enable-auto-tool-choice --tool-call-parser qwen3_coder on 4 × B200 or more
3. With SGLang use --reasoning-parser nemotron_3, not the BF16 card's nemotron_v3, which SGLang doesn't register
4. On SGLang with tools, pass chat_template_kwargs with enable_thinking true and force_nonempty_content true, as the card says, or reasoning and calls won't both parse
5. Thinking is on by default. Pass enable_thinking: false for short tasks or medium_effort: true to cut reasoning tokens
6. Use temperature=1.0 and top_p=0.95, as the generation config sets