Nanbeige4.3-33B is an open-weight model aimed at coding agents and professional tool workflows. Its practical interest is not that it appears in a trending list. The publisher supplies weights, a configurable reasoning and tool template, and serving examples. That gives developers something concrete to deploy and evaluate.
The important size distinction comes first: this is the 33B teacher model, not a promised small distilled model. An announcement about future smaller agents does not make this checkpoint suitable for a small laptop. This guide is based on the release artifacts and documented configuration; we have not benchmarked or load-tested the weights.
Release identity and license
The authoritative download is Nanbeige/Nanbeige4.3-33B, published by Nanbeige LLM Lab. The model card labels it Apache 2.0 and positions it as a teacher for the 4.3 family. Its Hugging Face configuration identifies LlamaForCausalLM, not a new hosted model with a universal token price.
Keep the chat checkpoint distinct from Nanbeige4.3-33B-Base. A base artifact and an instruction-tuned artifact can share architectural ancestry while behaving differently in an agent harness. Record the exact model repository and revision in your deployment manifest.
Apache 2.0 is not a promise that every downstream dataset, fine-tune or hosted service has identical terms. Preserve license notices and review additional artifacts separately. This matters when a community package bundles the model with a tool adapter or training material.
Context and conversation configuration
The publisher documents up to 524,288 tokens of context. That is a capacity ceiling, not an instruction to allocate half a million tokens on every request. Input, generated reasoning, final answers and tool history all compete for serving resources.
Two template controls are particularly relevant. enable_thinking controls reasoning on the current response. preserve_thinking controls retention of earlier assistant reasoning. The card recommends preserving it for multi-turn agents and using a different policy for ordinary chat. Tool-call formatting also has XML and JSON options, with the publisher favoring XML for its tested tool setup.
Do not migrate a generic chat history into the agent configuration without checking those controls. A harness that silently removes reasoning or changes tool serialization is not reproducing the publisher’s setup. Our AI application architecture hub covers conversation contracts and state boundaries.
Memory planning: arithmetic, not a minimum specification
Using the named 33-billion-parameter class, two bytes per weight gives about 66 GB in decimal units for weights alone. Four bits per weight gives about 16.5 GB before quantization metadata, runtime buffers or cache. These are planning calculations, not measured file sizes or a guarantee that the model fits a particular GPU.
Long context can add substantial KV-cache demand. Concurrency and batch size increase the working set again. A quantized checkpoint that loads successfully with a short prompt may fail on a much longer conversation. Treat “it loaded” and “it can serve our workload” as separate acceptance tests.
The publisher’s serving examples use four-way tensor parallelism, but that is an example configuration, not a universal requirement or a published minimum. A single guaranteed VRAM floor for every precision and context length is not published in the evidence used here. Use our GPU memory planning guide before renting hardware.
For a first trial, choose a conservative context limit and one request at a time. Increase one dimension at a time while recording peak memory and accepted output. Do not simultaneously change quantization, concurrency and maximum context, because a failure then has several plausible causes.
Serving paths and a safer first bind address
The official card supplies Transformers, SGLang and vLLM examples. Before installing, create an isolated Python environment and select compatible framework, CUDA and driver versions. Review code and pin the artifact revision if your setup requires trust_remote_code=True.
For vLLM, the publisher’s example combines reasoning and tool parsers. The following shows that configuration with a loopback bind, rather than exposing the service publicly:
vllm serve /path/to/Nanbeige4.3-33B \
--host 127.0.0.1 \
--port 8000 \
--tensor-parallel-size 4 \
--gpu-memory-utilization 0.8 \
--enable-auto-tool-choice \
--reasoning-parser qwen3 \
--tool-call-parser qwen3_coderThe local path must contain the selected checkpoint. The GPU count and memory settings are inherited from the documented example, not a recommendation for every machine. We did not execute this load for the article. Check support against the installed vLLM version before assuming a flag is available.
A successful health response is only the first check. Send a short text request, then a controlled tool request and a multi-turn continuation. Inspect whether reasoning and tool fields are parsed correctly instead of leaking markup into ordinary output. See vLLM serving and SGLang versus vLLM for the surrounding serving choices.
Quantizations are separate release artifacts
Do not assume that a GGUF, AWQ or other community conversion preserves every tool behavior from the original checkpoint. Confirm source revision, conversion method, license, tokenizer and chat template. A familiar model name in a desktop app does not establish that these are identical.
For evaluation, keep one full-precision or publisher-recommended reference when feasible. Compare the converted artifact on structured tool arguments and long conversations, not only a short factual answer. A quantization can be useful while still changing the failure profile that matters to an agent.
This guide does not certify an Ollama package or promise a one-command desktop install. Use the framework and artifact combination actually documented or validated for your environment. Our serving-framework comparison explains why runtime convenience and model compatibility are different questions.
Benchmarks and adoption evidence
The official card reports 80.9 on Terminal-Bench 2.1 and 60.3 on SWE-Bench Pro, with named harnesses and configuration notes. These are publisher-reported results, not our own measurements or a guarantee that this model beats every similar-sized alternative. Retaining thinking and using the correct tool template are part of the reported conditions.
As an adoption snapshot on October 29, Hugging Face’s public metadata showed 9,655 downloads for this checkpoint in its rolling download metric. That is evidence of artifact activity, not a count of production customers or a quality score. It is not the reason for the recommendation.
The stronger reason to evaluate is that a team can inspect and pin real weights, use documented parsers and test local operation against its own constraints. Compare completed tasks, time, memory and total cost with your existing setup. Our open coding-model guide is the broader shortlist, not an automatic ranking promotion for this release.
My take
Nanbeige4.3-33B is a credible local agent evaluation candidate for a team willing to operate a substantial checkpoint. The template details are more useful than a leaderboard headline. Start with a small tool-use suite and realistic memory limits; do not sell the teacher release to yourself as the future small distilled model.
FAQ
Is Nanbeige4.3-33B downloadable now?
Yes. The official Hugging Face repository contains the model artifacts. That is separate from any future smaller model in the family.
Is it a 3B model?
No. This guide covers the named 33B teacher. A planned small-model distillation is not the same checkpoint.
Does 512K context fit on a normal desktop GPU?
Not automatically. Weights, KV cache, precision and concurrency all affect memory. No universal fit guarantee is supported here.
Are the benchmark scores independent tests by AI Made Tools?
No. They come from the publisher’s card and retain that attribution.
Is there a verified hosted token price?
This guide establishes a local deployment path, not a standard hosted price. Hardware, utilization and serving configuration determine your operating cost.