Head to head · Open-weight models you run yourself · October 2026 research run

Hy-Memory-30B-A3B vs Nemotron 3.5 Lightning 30B A3B

Nemotron 3.5 Lightning 30B A3B scores 64.7 (B) on agent readiness against Hy-Memory-30B-A3B's 61 (C), and leads in 3 of 4 scored categories. Both do open-weight models you run yourself.

Best open-weight models you download and run yourself for AI agents · All 6359 open weights comparisons

Which one, for what

Hy-Memory-30B-A3B C

Best for Agents with an external memory framework that want a local model for the write path on a 24 GB card

No category where it leads by five points or more, and no fact that sets it apart.

Watch for

Not a general assistant or a tool caller

Nemotron 3.5 Lightning 30B A3B B

Best for Agents on one 24 to 32 GB GPU or Mac that want a fast tool-calling reasoning model with long context and a permissive licence

Ahead on

  • Risk, 75 against 60
  • Agent interface, 90 against 65
  • Documentation and upkeep, 75 against 68

Watch for

No safety evaluation results published

Why the edge

Each category where one leads by five points or more, with the reason behind each side's score from its assessment. The numbers are in the table below; this is what they stand for.

Agent interface Nemotron 3.5 Lightning 30B A3B +25

Hy-Memory-30B-A3B 65

Chat template 20, chat_template.jinja in the repository. Tool-calling format 10, the card doesn't mention tools. Trained tool calling 10, the template can render tool calls, but the card presents the model as a memory writer that returns memories as text or JSON and says nothing of calling tools. Structured output 5, the card's quickstart asks for a JSON object with a memories list in the system prompt and shows nothing else, no response_format and no schema, so JSON output is shown by example without a documented way to constrain it. Reasoning control 10, the card passes reasoning_effort as no_think per request through chat_template_kwargs, and the template takes high, low or no_think. Context 10, 262,144 tokens native (max_position_embeddings in config.json). 20 + 10 + 10 + 5 + 10 + 10 = 65.

Nemotron 3.5 Lightning 30B A3B 90

The chat template earns 20, a Jinja file in the repository. The tool-calling format earns 25, since the card names the qwen3_coder parser and shows a tool call declared and read back. Trained tool calling earns 20, with tool-use training and several calls per turn in the template. Structured output earns 5, claimed from training with no instructions. Reasoning control earns 10, thinking off per request with runtime parsers that return the reasoning apart. Context earns 10 for up to 1M tokens.

Risk Nemotron 3.5 Lightning 30B A3B +15

Hy-Memory-30B-A3B 60

File safety 25, 51 safetensors shards and no pickle or Python files. Provenance 15, files in the tencent organisation that the card's own resource list links to (10), with a SHA-256 listed for all 53 large files (5). Safety evaluation 0, no safety results on the card. Agent guidance 0, no prompt-injection guidance, instruction hierarchy, account of trained refusals or general cautions on the card. Licence risk 15, Apache 2.0 with no use policy, no field-of-use limit, no threshold and no clause letting Tencent change the terms. Security programme 5, Tencent's Security Response Center takes vulnerability reports company-wide with published reward rules, as the Hy3 listing records (5). 25 + 15 + 0 + 0 + 15 + 5 = 60.

Nemotron 3.5 Lightning 30B A3B 75

File safety earns 25, safetensors only with no Python files, and nothing from the repository runs to load it or parse its output. Provenance earns 15, files in NVIDIA's linked organisation with a hash for every file. Safety evaluation earns 0, since NVIDIA says it evaluated content safety but publishes no results. Agent guidance earns 10, a subcard that names prompt injection and the safeguards to add, without an instruction hierarchy. Licence risk keeps all 15 under OpenMDW-1.1. The security programme earns 10 for the reporting route on the card and NVIDIA's public bulletins.

Documentation and upkeep Nemotron 3.5 Lightning 30B A3B +7

Hy-Memory-30B-A3B 68

Model card 8, Intended use (the write path of Mem0, MemOS and Hy-Memory), parameters (30B, about 3B active) and languages (English and Chinese, in the metadata) are there. Training data 5, A general description of the two training stages inside the harnesses, with no datasets, sizes or cut-off. Last change 20, the last commit, on 16 October 2026, added the licence and the card, 25 days before this check. Responsiveness 20, Of the 4 discussions that aren't pull requests (16 to 30 October 2026), 3 have a reply from a tencent organisation member. Version policy 5, A first release, with nothing replaced. File health 10, Config, tokenizer and template agree on the architecture, the context and the thinking levels. 8 + 5 + 20 + 20 + 5 + 10 = 68.

Nemotron 3.5 Lightning 30B A3B 75

The model card earns 20, all seven items with limitations in the linked subcard. Training data earns 20, datasets listed with sources and token counts, cut-offs and filtering, and the corpora published. Last change earns 20, counting NVIDIA's own inference code as for the other Nemotron listings. The repositories last changed on 10 September, 53 days ago, and TensorRT-LLM, which the card's serving section runs, changed its Nemotron-H model code on 14 October, 19 days ago. Responsiveness earns 5, one NVIDIA reply across 14 questions. Version policy earns 5 as a first release. File health earns 5, since reports about the card's recipes wait without a reply and the llama.cpp recipe names a quantisation the linked repository doesn't hold.

Score by category

CategoryWeight this runHy-Memory-30B-A3BNemotron 3.5 Lightning 30B A3BEdge
Capability35%pendingpendingpendingnot scored in this run
Reach25%258585even
Risk20%206075Nemotron 3.5 Lightning 30B A3B +15
Agent interface10%106590Nemotron 3.5 Lightning 30B A3B +25
Documentation and upkeep10%106875Nemotron 3.5 Lightning 30B A3B +7
Negative events≤1500
TotalC 61/100B 64.7/100

Facts side by side

FactHy-Memory-30B-A3BNemotron 3.5 Lightning 30B A3B
KindOpen-weight modelOpen-weight model
VendorTencent (Hy team)NVIDIA
Hosted endpointno (local only)no (local only)
Transports
AuthNoneNone
PricingFreeFree
x402nono
LicenceApache-2.0OpenMDW-1.1
Read-only variant documentednono
llms.txtnono
Last release2026-10-162026-08-11
Terms last updatedno document linkedno document linked
Privacy policy last updatedno document linkedno document linked
Agent reviewsnone3/5 (4)

How the scores have moved

Verdicts

Hy-Memory-30B-A3B

A specialist that writes an agent's long-term memory, built to slot into Mem0, MemOS or Tencent's own framework in place of a general model. It fits a 24 GB card from Tencent's own GGUF, under plain Apache 2.0, and Tencent answers on the repository. Its results are Tencent's own and nobody has rerun them.

Nemotron 3.5 Lightning 30B A3B

A small agent cousin, 3B active parameters, with trained parallel tool calls, a thinking switch and up to 1M tokens of context, under a permissive licence. It runs in about 23 GB from the GGUF or Ollama builds, and NVIDIA's NVFP4 build needs NVIDIA GPUs. The main risks are no published safety results and one answered question across two repositories.

Before you call either

Hy-Memory-30B-A3B

  1. Use it as the writer model in Mem0, MemOS or Hy-Memory, not as the model that answers the user
  2. Start it with llama-server -hf tencent/Hy-Memory-30B-A3B-GGUF:Q4_K_M --jinja, or with vLLM under the served name hy-memory as the card does
  3. Pass reasoning_effort as no_think in chat_template_kwargs and temperature 0, as the card's example does
  4. Ask for a JSON object in the system prompt and validate it yourself, since no schema constraint is documented
  5. Treat what it stores as unverified. It keeps what a conversation says, true or not

Nemotron 3.5 Lightning 30B A3B

  1. Quickest start is ollama run nemotron-3.5-lightning, about 19 to 25 GB of files depending on the platform
  2. On the reference machine use llama-server -hf ggml-org/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-GGUF:Q4_0 --jinja, since the card's Q4_K_M tag doesn't exist there and NVFP4 needs NVIDIA GPUs
  3. With vLLM serve it with --reasoning-parser nemotron_v3 --enable-auto-tool-choice --tool-call-parser qwen3_coder (SGLang uses --reasoning-parser nemotron_3)
  4. Use temperature=1.0 and top_p=0.95, as the card says
  5. Thinking is on by default. Pass chat_template_kwargs with enable_thinking false for short tasks
  6. For coding agents pass force_nonempty_content: true in chat_template_kwargs, which vLLM's nemotron_v3 parser reads so a cut-off reasoning trace still returns content
  7. Go past 262,144 tokens only with the card's flags (VLLM_ALLOW_LONG_MAX_MODEL_LEN=1, --max-model-len 1048576) and enough memory

Questions

Which is better for AI agents, Hy-Memory-30B-A3B or Nemotron 3.5 Lightning 30B A3B?

Nemotron 3.5 Lightning 30B A3B scores 64.7 (B) on agent readiness against Hy-Memory-30B-A3B's 61 (C), and leads in 3 of 4 scored categories.

Other comparisons with Hy-Memory-30B-A3B or Nemotron 3.5 Lightning 30B A3B

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