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You can run a Nemotron checkpoint behind an OpenAI-compatible API with vLLM, but there is no single Nemotron hardware requirement. Choose the exact repository first, then match its precision, context length, modality and parallelism needs to your NVIDIA system. The reliable sequence is: prepare a compatible CUDA host, authenticate and cache the weights, install vLLM or its container, start vllm serve, verify a small request, and tune memory before increasing load.
What you need to decide before installing anything
“Nemotron” identifies a family of checkpoints rather than one model. A compact text model, a multimodal checkpoint, a quantized release and a very large mixture-of-experts model can have radically different memory and topology requirements. The repository name alone is not enough to size a machine.
Open the model card for the exact checkpoint you intend to run and record these values:
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- Precision or quantization: BF16, FP16, NVFP4 and other formats change weight memory, supported kernels and sometimes quality.
- Context limit: a model that loads at a short context can run out of memory when the key-value cache grows for long prompts or many concurrent requests.
- Modality: text-only and multimodal checkpoints may require different processors, flags and memory budgets.
- License and production terms: confirm that your intended commercial or internal use is allowed.
- Documented runtime: NVIDIA’s deployment index and model cards identify supported engines and any model-specific vLLM instructions.
NVIDIA lists vLLM as a deployment framework for supported Nemotron platforms. The official materials also include model-specific cookbooks, so treat a cookbook for your selected checkpoint as the authority over a generic command.
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Hardware: why one VRAM number would be misleading
Estimate memory from the selected checkpoint, not from the Nemotron family name. Weight storage, runtime workspace, CUDA graphs, activations and the KV cache all compete for GPU memory. Quantization can reduce weight memory, while a larger context window and higher concurrency increase KV-cache demand. Driver, CUDA, host-RAM and interconnect compatibility matter as well.
| Hardware question | Why it matters | What to verify |
|---|---|---|
| How many GPUs? | Some checkpoints fit on one card; larger ones require tensor parallelism across a replica. | Compare the model card’s recommendation with the number of identical GPUs available. |
| Usable VRAM per GPU | Weights and cache must fit after other processes and vLLM overhead are reserved. | Check free memory with your normal driver stack, not only the card’s advertised capacity. |
| Interconnect | Tensor-parallel workers exchange data continually; slow links can reduce throughput or make a configuration impractical. | Confirm PCIe, NVLink or the inter-node fabric expected by the deployment guide. |
| Host RAM and disk | Downloads, tokenizer files, CPU staging and optional offload consume system resources. | Leave room for the complete cache, temporary files and the operating system. |
| Software compatibility | vLLM wheels and containers depend on CUDA and driver support. | Record the driver, CUDA environment, vLLM release and container tag before deployment. |
NVIDIA’s Nemotron 3 Ultra hybrid Mamba-Transformer MoE cookbook describes a 550B-parameter model with 55B active parameters. Its Station Deployment Guide targets a GB300-based DGX Station, while a separate DGX Spark route specifies five GB200 nodes or three B200 nodes. Those figures describe the named deployment targets; they are not a minimum specification for every Nemotron checkpoint.
A single GPU is therefore possible only when the selected checkpoint, precision, context and concurrency fit that card. If the model card calls for multiple devices, set tensor parallelism to the number of GPUs in one replica and provide a topology that the guide supports. Do not assume that adding unrelated cards produces a useful or supported configuration.
Prepare the NVIDIA host
Check the physical and software baseline
Use a supported NVIDIA GPU with a working driver and CUDA runtime. Before installing vLLM, verify GPU visibility, free VRAM, host memory, disk capacity and the interconnect between cards or nodes. Stop unrelated GPU jobs while measuring available memory; a model that fits on an idle machine may fail when another process reserves several gigabytes.
For production, write down the GPU model and count, driver and CUDA versions, host operating system, vLLM version, model revision, precision, context limit and every launch flag. This record turns an opaque “out of memory” incident into a reproducible configuration.
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Create a persistent Hugging Face cache
Weights can occupy tens or hundreds of gigabytes. Put the cache on fast, durable storage rather than a container’s temporary filesystem. When using Docker, mount the same host directory on every restart so the image does not download the model again.
export MODEL="nvidia/<exact-nemotron-repository>"
export HF_CACHE_DIR="${HF_HOME:-$HOME/.cache/huggingface}"
mkdir -p "$HF_CACHE_DIR"Authenticate when access is gated
If the model card requires approval or authenticated access, log in on the host before starting the server:
hf auth loginFor a container, pass a Hugging Face token through an environment variable and avoid baking it into an image or shell history. Ensure the account is entitled to the exact repository; authentication does not bypass a model’s access or license conditions.
Install vLLM with Docker
The container route keeps the CUDA, Python and vLLM userspace together. NVIDIA’s Ultra guide names an image tagged vllm/vllm-openai:v0.22.0. Use the tag specified by your model card or deployment guide after checking compatibility. The latest tag below is only a template and should be replaced with a pinned version for a repeatable deployment.
export MODEL="nvidia/<exact-nemotron-repository>"
export HF_CACHE_DIR="${HF_HOME:-$HOME/.cache/huggingface}"
mkdir -p "$HF_CACHE_DIR"
docker run --rm --name nemotron-vllm
--gpus all
--ipc=host
-p 8000:8000
-v "$HF_CACHE_DIR:/root/.cache/huggingface"
-e HF_TOKEN="$HF_TOKEN"
vllm/vllm-openai:latest
--model "$MODEL"--gpus all exposes the visible NVIDIA devices. Port 8000 is the API endpoint. --ipc=host gives tensor-parallel workers the shared memory they need; an equivalent explicit --shm-size can be used when your container policy does not allow host IPC. Restrict published ports with your firewall or bind to a private interface when the service should not be public.
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Pin the image tag once you have confirmed the model card’s supported vLLM release. A newer image can change kernels, defaults or model support, while an older image may lack the implementation required by a new checkpoint.
Install vLLM directly on the host
If you manage the CUDA and Python environment yourself, install a vLLM release compatible with the selected model and driver, then use the same serving entry point. The exact installation command is release-specific; follow the vLLM installation instructions associated with the version named by the model card rather than mixing an arbitrary wheel with an unrelated CUDA stack.
After installation, confirm that Python can import vLLM and that the process can see the intended GPUs. Keep this environment isolated from other projects so upgrading a dependency does not silently alter a working server.
Start the Nemotron server
The direct command below illustrates the important controls:
vllm serve nvidia/<exact-nemotron-repository>
--tensor-parallel-size 1
--gpu-memory-utilization 0.90
--host 0.0.0.0
--port 8000Choose tensor parallelism
Set --tensor-parallel-size to the number of GPUs assigned to one model replica when the checkpoint and hardware topology support it. Use 1 for a single-GPU deployment. For a multi-GPU replica, every worker must be able to communicate with the others, and the aggregate memory must cover the model plus runtime overhead. Tensor parallelism is not the same as running independent replicas: one replica splits a model across devices, whereas multiple replicas duplicate it to serve more requests.
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Reserve memory deliberately
--gpu-memory-utilization sets the fraction of GPU memory that vLLM may use for an instance. Current vLLM documentation lists 0.92 as the default; the example uses 0.90 to leave a little more headroom for a first launch. Raising the value can make a tight model fit, but leaves less room for driver allocations and other processes. Lowering it can prevent interference at the cost of available cache.
Bind the network safely
--host 0.0.0.0 listens on all interfaces, which is convenient in a container or a private server but exposes the API wherever the firewall permits. Bind to 127.0.0.1 for local-only use, or place the service behind an authenticated reverse proxy before allowing remote clients. Port 8000 is conventional; choose another port if it conflicts with an existing service.
Wait for the startup logs to report that the engine is ready. The logs show the served model name and often reveal whether the checkpoint loaded in the expected dtype, how many workers started and how much cache was reserved. Save those logs with your deployment notes.
Verify the OpenAI-compatible API
Use the model identifier printed in the startup logs. It may be the full repository name or a configured alias; do not guess a different name in the request.
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-H "Content-Type: application/json"
-d '{
"model": "nvidia/<exact-nemotron-repository>",
"messages": [{"role": "user", "content": "Reply with one short sentence confirming the server is reachable."}],
"max_tokens": 32,
"temperature": 0
}'A JSON completion confirms routing, tokenizer loading and generation. Start with a short prompt and a small output limit. Only after that smoke check should you test the target context length, concurrent requests, streaming and any multimodal input format documented for the checkpoint.
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OpenAI-compatible clients normally use http://localhost:8000/v1 as their base URL. If a client asks for an API key even though your local server does not enforce one, provide a placeholder value accepted by the client and keep the endpoint private.
Tune memory, context and throughput
When a model loads but performs poorly, change one variable at a time and measure on the hardware and workload you actually care about. No generic throughput number applies across Nemotron checkpoints or GPU topologies.
If loading fails with out-of-memory errors
- Confirm that no other process is using VRAM and that all expected GPUs are visible.
- Reduce the maximum context length or initial concurrency; KV-cache allocation grows with both.
- Use a smaller checkpoint or a quantized precision supported by that model and your vLLM build.
- Increase
--tensor-parallel-sizeacross supported GPUs, making sure the topology matches the model’s deployment guidance. - Adjust vLLM’s GPU-memory and KV-cache settings instead of immediately changing unrelated CUDA variables.
- Consider CPU offload only as a fallback. It can make a model fit by borrowing host memory, but transfers add latency and do not guarantee usable throughput.
If throughput is lower than expected
- Measure prompt length, generated tokens, concurrency, batch size and latency separately; a short single request is not a throughput benchmark.
- Check that tensor-parallel workers use the intended interconnect and that the host is not paging or swapping.
- Increase concurrency gradually while watching VRAM and KV-cache utilization.
- Use vLLM’s batching and KV-cache controls appropriate to your version, then repeat the same workload for comparison.
- Keep model, vLLM, CUDA, driver and container versions fixed during a comparison.
If the server starts but requests fail
- Compare the request’s
modelvalue with the startup log exactly. - Check that the client uses the
/v1base path and the port exposed by Docker or the host process. - Inspect logs for tokenizer, processor or modality errors and follow the selected model card’s input format.
- Try a short text-only request before testing long context, images or high concurrency.
How to compare two Nemotron checkpoints
Choose between viable checkpoints by matching the workload, not by selecting the largest parameter count. Record the following in a comparison sheet:
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| Model size | What are the total parameters and, for a mixture-of-experts model, the active parameters per token? |
| Precision | Is the release BF16, FP16, NVFP4 or another quantization, and does your vLLM build support it? |
| Capability | Is it text-only or multimodal, and does it provide the input processors your application needs? |
| Context | What maximum context is documented, and how much of it will your workload use concurrently? |
| Hardware | How many GPUs, how much VRAM and what interconnect are required for one replica? |
| Performance | What latency and throughput do you measure at the intended prompt mix and concurrency? |
| Runtime support | Does the model card identify a compatible vLLM version and any required flags? |
| Terms | Does the exact license permit your deployment and redistribution pattern? |
The 550B-total/55B-active figures published for Nemotron 3 Ultra illustrate why active-expert size and total size should both be recorded. They do not, by themselves, predict the VRAM required by every precision, context or implementation.
Production checklist
- Pin the model revision, vLLM version, container tag, CUDA and driver versions.
- Store the launch command, tensor-parallel size, memory fraction, cache settings and hardware topology in deployment notes.
- Keep Hugging Face credentials outside images and source repositories; rotate them according to your organization’s policy.
- Persist and monitor the model cache, logs, disk space, VRAM, request latency, token rates and error rates.
- Protect the OpenAI-compatible endpoint with network controls and authentication before exposing it beyond a trusted host.
- Load-test the actual prompt lengths, output limits, modalities and concurrency your users will send.
- Recheck the exact checkpoint license and any production restrictions before commercial use.
- Retest after changing a model revision, quantization, vLLM release, CUDA driver or GPU topology.
What the official deployment examples do—and do not—establish
NVIDIA’s published examples establish a supported deployment path using vLLM, Hugging Face authentication and a persistent cache. They also show that some Ultra configurations target specialized GB300, GB200 or B200 systems and may span multiple nodes. They do not establish a universal minimum VRAM figure, a guaranteed speed, or a successful run for every Nemotron checkpoint on consumer hardware.
Validate the selected model card on your target machine. Treat the first request as an operational check, then collect your own measurements before promising latency, capacity or cost to users.
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