Nemotron-3.5-Embed-8B-BF16 API
Nemotron-3.5-Embed-8B-BF16 from nvidia converts text into numerical vectors for similarity search. It is an embedding service, so the response is a vector rather than a chat answer.
PreparingToman per million tokens
The usage rate is fixed at request admission. Reported consumption determines the final usage amount.
- API model ID
- di-nvidianemotron-35-embed-8b-bf16-8c81fe254d
- Model developer
- nvidia
- Service type
- Embeddings
- Current availability
- Preparing
- Configured context limit
- 32,768 tokens
Model features
No additional gateway capabilities have been confirmed for this model.
What is Nemotron-3.5-Embed-8B-BF16?
Use the public identifier di-nvidianemotron-35-embed-8b-bf16-8c81fe254d for Nemotron-3.5-Embed-8B-BF16 in ServerNet. The gateway uses this identifier to select the configured route; the developer is nvidia.
Where to use Nemotron-3.5-Embed-8B-BF16
- Index document chunks with Nemotron-3.5-Embed-8B-BF16 for semantic search.
- Embed the search query with the same Nemotron-3.5-Embed-8B-BF16 model used to build your index.
- Evaluate retrieval quality before combining the results with a text-generation model.
Build your integration
Send a string or a list of strings to /v1/embeddings. Store the returned vectors in your search index. When switching models, rebuild the index and check vector dimensions instead of mixing incompatible vectors.
Inputs and service limits
The route accepts plain text and batches of up to 128 texts. Token ID arrays, media inputs and unsupported output formats are refused. Choose chunk sizes within the model’s configured input limit.
A request cost example
For 1,000 input tokens and 0 output tokens, without a cache discount. This is a catalogue estimate, not a fixed request fee. Reported consumption and the price frozen for your request determine the usage amount.
- Estimated usage, Toman
- 11
Connect to Nemotron-3.5-Embed-8B-BF16 API
Enable ai:embedding on your key and ai:models:read to check availability. Set SERVERNET_API_KEY in your server environment. Change the idempotency key for each new request; keep it unchanged only for an identical retry.
This example documents the request format. Send it only after this model appears in GET /v1/models.
curl https://servernet.cloud/v1/embeddings \
-H "Authorization: Bearer $SERVERNET_API_KEY" \
-H 'Content-Type: application/json' \
-H 'Idempotency-Key: CHANGE_FOR_EACH_NEW_REQUEST' \
-d '{
"model": "di-nvidianemotron-35-embed-8b-bf16-8c81fe254d",
"input": [
"Hello world",
"Search documents"
]
}'
Use the same Idempotency-Key when retrying an identical request. Successful responses are replayed without a second charge for 24 hours. Do not automatically retry when x-should-retry:false is present. The key is shown once. Store it in a server environment variable. Keep it out of browser code, GitHub and logs.
Keys, budgets and usage reportsFrequently asked questions
Is Nemotron-3.5-Embed-8B-BF16 available through ServerNet?
Nemotron-3.5-Embed-8B-BF16 is currently unavailable for API use. A catalogue listing does not activate billing or access. Choose an available related model or check this page again after the route is verified.
Which model ID and API endpoint should I use for Nemotron-3.5-Embed-8B-BF16?
Use di-nvidianemotron-35-embed-8b-bf16-8c81fe254d as the model field, the endpoint shown below and the matching key scope. ServerNet keys are project-specific; an SDK also needs https://servernet.cloud/v1 as its base URL.
How is Nemotron-3.5-Embed-8B-BF16 usage charged?
Only input tokens are billed for this embedding route. A returned vector is not billable generated text. Each text in a batch contributes to input consumption.
What should I check before using Nemotron-3.5-Embed-8B-BF16 in production?
The route accepts plain text and batches of up to 128 texts. Token ID arrays, media inputs and unsupported output formats are refused. Choose chunk sizes within the model’s configured input limit.
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