SambaStack Supported Models and Bundles

SambaStack supports a variety of models that can be deployed to both on-prem and hosted environments. Contact your system administrator to determine which models are available on your deployment. For definitions of the Preview and Stable designations, how models move between stages, the notice you receive before a model is retired, and the deprecation history, see SambaStack model lifecycle.

All supported models

Search or filter to find a model, then select it to jump to its full configuration.

Custom checkpoints

All decoder-only text generation models support custom checkpoints, except the models in the following table. To list only the models that support them, select Custom checkpoints in the filter above. To convert a checkpoint, see Checkpoint conversion tool.

Model	Reason
`Llama-4-Maverick-17B-128E-Instruct`	Multimodal, not decoder-only text
`MiniMax-M3`	Multimodal, not decoder-only text
`gemma-3-27b-it`	Multimodal, not decoder-only text
`gemma-3-12b-it`	Multimodal, not decoder-only text
`gemma-4-31B-it`	Multimodal, not decoder-only text
`Qwen3-32B`	No conversion package available yet
`Whisper-Large-v3`	Not a text generation model
`Qwen3-TTS-Talker`	Not a text generation model
`Qwen3-TTS-Vocoder`	Not a text generation model
`E5-Mistral-7B-Instruct`	Not a text generation model

Finding models on your cluster

You can run the following command to discover available models in your cluster:

kubectl -n <namespace> get models

kubectl get models does not return the name you send to the API

The command lists Kubernetes resource names (metadata.name). Inference requests must use the serving name (spec.name), which is what the Model ID headings below use.

apiVersion: sambanova.ai/v1alpha1
kind: Model
metadata:
  name: gemma-4-31b-it    # Kubernetes resource name – what kubectl get models returns
spec:
  name: gemma-4-31B-it    # serving name – use this in API requests

The two often differ only in case or punctuation, so the mismatch is easy to miss. In the example above they differ by a single character.To read the serving name of a deployed model, run:

kubectl -n <namespace> describe model <resource-name>

A model can also define spec.aliases, additional names that route to the same model. For the full Model field reference, see Deploy custom checkpoints.Text to speech is one case where the names diverge: address /v1/audio/speech as qwen3-tts, not by the talker or vocoder resource names. See Text to speech.

Recommended model bundles

In SambaStack, a bundle is a packaged deployment that groups one or more models together with their associated configurations, such as batch size and sequence length. A single model can also be deployed on its own by pairing it with a model profile, without creating a bundle. For example, deploying the Meta‑Llama‑3.3‑70B model with a batch size of 4 and a sequence length of 16K tokens constitutes a single configuration. A bundle, however, can contain multiple such configurations, either for the same model or for different models. SambaNova’s RDU technology enables several models and configurations to be loaded simultaneously in a single deployment. This allows you to switch instantly between models and between batch‑/sequence‑size profiles as needed. In contrast to traditional GPU systems, where deployments are typically single‑model and static, SambaStack supports multi‑model, multi‑configuration bundles. This approach delivers higher efficiency, greater flexibility, and increased throughput while preserving low latency. You can run the following command to discover available bundles in your cluster:

kubectl -n <namespace> get modelbundles

The table below lists the recommended bundles for the models currently available in SambaStack. Each entry pairs a model with its recommended deployment bundle.

If the bundles listed below do not satisfy your inference requirements, you can create custom bundles that combine any mix of models and configurations so long as they fit in DDR memory.

Deployment options

When deploying models in SambaStack, administrators can select from various context length and batch size combinations.

Smaller batch sizes provide higher token throughput (tokens/second).
Larger batch sizes provide better concurrency for multiple users.

SambaStack supports two deployment configurations – high-interactivity and high-throughput. See High-throughput deployment for when to use each.

Suggested bundles per model

For each model, this section lists the suggested bundle for typical use and any alternative bundles that trade off context length, batch size, or modality support. See Bundle configurations below for the seq length/batch size details of each bundle.

Provider	Model	Suggested bundle	Alternative bundles
Meta	`Meta-Llama-3.3-70B-Instruct`	`70b-3dot3-ss-4-8-16-32-64-128k`	`70b-3dot3-ss-full-whisper`, `us-agentic-rag-1-1`, `e5-mistral-70b-64k-128k`
Meta	`Meta-Llama-3.1-8B-Instruct`	`us-agentic-rag-1-1`	`qwen3-32b-llama405b-s-m`
Meta	`Meta-Llama-3.1-405B-Instruct`	`qwen3-32b-llama405b-s-m`	—
Meta	`Llama-4-Maverick-17B-128E-Instruct`	`llama-4-medium-8-16-32-64-128k`	`llama-4-medium-ss-16k-bs24`, `us-agentic-rag-1-1`
MiniMax	`MiniMax-M3`^PREVIEW	`minimax-m3-32-64-128-256-512k-1m` (up to 1M context)	`minimax-m3-512k` (up to 512K context), `minimax-m3-32k`, `minimax-m3-acb-prefix-caching` (adds prompt caching, text only)
MiniMax	`MiniMax-M2.7`	`dyt-minimax-m2p7-32-64-160-192k-pc` (adds prompt caching)	`dyt-minimax-m2p7-32k-pc`, `dyt-minimax-m2p7-32-64-160-192k`, `dyt-minimax-m2p7-32-160-192k`, `dyt-minimax-m2p7-32k-v2`
MiniMax	`MiniMax-M2.5`	`dyt-minimax-m2p5-32-160k`	`dyt-minimax-m2p5-32k`
Mistral AI	`Mistral-Large-3-675B-Instruct-2512`^PREVIEW	`mistral-large-3-fp8-8-16-32k`	`mistral-large-3-fp8-8k`
DeepSeek	`DeepSeek-R1-0528`	`deepseek-4in1-fp8-128k` (higher context length)	`deepseek-r1-v3-fp8-8k`, `deepseek-r1-v31-fp8-8k`
DeepSeek	`DeepSeek-V3-0324`	`deepseek-4in1-fp8-128k` (higher context length)	`deepseek-r1-v3-fp8-8k`, `deepseek-v3-v31-fp8-8k`, `deepseek-v3-v3termi-fp8-8k`
DeepSeek	`DeepSeek-V3.1`	`deepseek-4in1-fp8-128k` (higher context length)	`deepseek-r1-v31-fp8-8k`, `deepseek-v3-v31-fp8-8k`
DeepSeek	`DeepSeek-V3.1-Terminus`	`deepseek-4in1-fp8-128k` (higher context length)	`deepseek-v3-v3termi-fp8-8k`
OpenAI	`gpt-oss-120b`	`us-agentic-rag-1-1`	`cd-dyt-gpt-oss-120b-8-32-64-128k`, `gpt-gemma-whisper-mistral`
OpenAI	`gpt-oss-20b`^PREVIEW	`dyt-gpt-oss-20b-32-64-128k`	—
OpenAI	`Whisper-Large-v3`	`qwen3-32b-whisper-e5-mistral`	`70b-3dot3-ss-full-whisper` (known issue: does not load within default startup time), `gpt-gemma-whisper-mistral`
Google	`gemma-3-27b-it`^PREVIEW	`gemma3-27b-32-128k`	—
Google	`gemma-3-12b-it`^PREVIEW	`gemma3-v3`	`gpt-gemma-whisper-mistral`
Google	`gemma-4-31B-it`	`gemma-4-31b-32-128-256k` (adds 256K context; text, image, and video support), `gemma-4-31b-32-128k` (up to 128K context; text, image, and video support, higher throughput). Both bundles support constrained decoding across all of their sequence lengths. Choose between them on context length and throughput, not capability.	`gemma-4-31b-mtp-cd-8-32-64-128k` (Preview; multi-token prediction, up to 128K context)
Alibaba Cloud	`Qwen3-235B-A22B-Instruct-2507`	`dyt-qwen3-235b-32-128k`	—
Alibaba Cloud	`Qwen3-32B`	`qwen3-32b-whisper-e5-mistral`	`qwen3-32b-llama405b-s-m`
Alibaba Cloud	`Qwen3-TTS-Talker`^PREVIEW	`qwen3-tts-talker`	—
Alibaba Cloud	`Qwen3-TTS-Vocoder`^PREVIEW	`qwen3-tts-vocoder`	—
Other	`E5-Mistral-7B-Instruct`	`us-agentic-rag-1-1`	`e5-mistral-70b-64k-128k`, `qwen3-32b-whisper-e5-mistral`, `gpt-gemma-whisper-mistral`

Bundle configurations

The table below lists the configuration details for each bundle.

Model name	Bundle name	Bundle description	Bundle configuration
`DeepSeek-R1-0528` / `DeepSeek-V3.1`	deepseek-r1-v31-fp8-8k	Medium context length with low batch size	View Models: `DeepSeek-R1-0528` Seq Length: 8K, BS: 1, 4 `DeepSeek-V3.1` Seq Length: 8K, BS: 1, 4
`DeepSeek-R1-0528` / `DeepSeek-V3-0324`	deepseek-r1-v3-fp8-8k	Medium context length with low batch size	View Models: `DeepSeek-R1-0528` Seq Length: 8K, BS: 1, 4 `DeepSeek-V3-0324` Seq Length: 8K, BS: 1, 4
`DeepSeek-V3-0324` / `DeepSeek-V3.1`	deepseek-v3-v31-fp8-8k	Medium context length with low batch size	View Models: `DeepSeek-V3-0324` Seq Length: 8K, BS: 1, 4 `DeepSeek-V3.1` Seq Length: 8K, BS: 1, 4
`DeepSeek-V3-0324` / `DeepSeek-V3.1-Terminus`	deepseek-v3-v3termi-fp8-8k	Medium context length with low batch size	View Models: `DeepSeek-V3-0324` Seq Length: 8K, BS: 1, 4 `DeepSeek-V3.1-Terminus` Seq Length: 8K, BS: 1, 4
`DeepSeek-R1-0528` / `DeepSeek-V3-0324` / `DeepSeek-V3.1` / `DeepSeek-V3.1-Terminus`	deepseek-4in1-fp8-128k	Large context length with single batch size Four DeepSeek models in one bundle	View Models: `DeepSeek-R1-0528` Seq Length: 128K, BS: 1 `DeepSeek-V3-0324` Seq Length: 128K, BS: 1 `DeepSeek-V3.1` Seq Length: 128K, BS: 1 `DeepSeek-V3.1-Terminus` Seq Length: 128K, BS: 1
`E5-Mistral-7B-Instruct` / `Meta-Llama-3.1-8B-Instruct` / `Llama-4-Maverick-17B-128E-Instruct` / `Meta-Llama-3.3-70B-Instruct` / `gpt-oss-120b`	us-agentic-rag-1-1	Small to medium context length with varied batch size Speculative decoding supported for `Meta-Llama-3.3-70B`	View `gpt-oss-120b` Seq Length: 32K, BS: 4 Seq Length: 64K, BS: 2 Seq Length: 128K, BS: 2 `Llama-4-Maverick-17B-128E-Instruct` Seq Length: 8K, BS: 1 Seq Length: 16K, BS: 1 `Meta-Llama-3.3-70B` (Target)/ `Meta-Llama-3.2-1B` (Draft) Seq Length: 4K, BS: 1, 4, 8, 16, 32 Seq Length: 8K, BS: 1, 4, 8 Seq Length: 16K, BS: 1, 4 Seq Length: 32K, BS: 1, 4 Seq Length: 64K, BS: 1 Seq Length: 128K, BS: 1 `Meta-Llama-3.1-8B-Instruct` Seq Length: 4K, BS: 1, 4, 16, 32 Seq Length: 8K, BS: 1, 4, 16, 32 Seq Length: 16K, BS: 1, 4, 8 `E5-Mistral-7B-Instruct` Seq Length: 4K, BS: 1, 4, 8, 16, 32
`E5-Mistral-7B-Instruct` / `Meta-Llama-3.3-70B`	e5-mistral-70b-64k-128k	Large context length with low batch size Speculative decoding supported for `Meta-Llama-3.3-70B`	View Models: `E5-Mistral-7B-Instruct` Seq Length: 4K, BS: 1, 4, 8, 16, 32 `Meta-Llama-3.3-70B` (Target)/ `Meta-Llama-3.2-1B` (Draft) Seq Length: 64K, BS: 1 Seq Length: 128K, BS: 1
`gemma-3-27b-it`	gemma3-27b-32-128k	Homogeneous bundle containing `gemma-3-27b-it` configurations.	View `gemma-3-27b-it` Seq Length: 32K, BS: 2, 4, 6, 8 Seq Length: 128K, BS: 2, 4, 6, 8
`gemma-3-12b-it`	gemma3-v3	Homogeneous bundle containing `gemma-3-12b-it` configurations. Large context length with medium batch size	View `gemma-3-12b-it` Seq Length: 128K, BS: 2, 4, 6, 8
`gemma-4-31B-it`	gemma-4-31b-32-128k	Homogeneous bundle with constrained decoding for `gemma-4-31B-it`. Supports text, image, and video input.	View `gemma-4-31B-it` Seq Length: 32K, BS: 2, 4, 6, 8 Seq Length: 128K, BS: 2, 4, 6, 8
`gemma-4-31B-it`	`gemma-4-31b-32-128-256k`	Homogeneous bundle with constrained decoding for `gemma-4-31B-it`. Adds context support up to 256K. Supports text, image, and video input at all sequence lengths.	View `gemma-4-31B-it` Seq Length: 32K, BS: 2, 4, 6, 8 Seq Length: 128K, BS: 2, 4, 6, 8 Seq Length: 256K, BS: 2
`gemma-4-31B-it`^PREVIEW	`gemma-4-31b-mtp-cd-8-32-64-128k`	Homogeneous bundle with multi-token prediction and constrained decoding for `gemma-4-31B-it`. Up to 128K context; text, image, and video input at all sequence lengths. Multi-token prediction raises decode throughput; the model predicts several tokens per step and verifies them in the same pass.	View `gemma-4-31B-it` Seq Length: 8K, BS: 2, 4, 6, 8 Seq Length: 32K, BS: 2, 4, 6, 8 Seq Length: 64K, BS: 2, 4, 6, 8 Seq Length: 128K, BS: 2, 4, 6, 8
`gpt-oss-120b`	`cd-dyt-gpt-oss-120b-8-32-64-128k` †	Homogeneous bundle with constrained decoding for `gpt-oss-120b`. Covers 8K through 128K context. Includes structured output (logit-masking) support.	View `cd-dyt-gpt-oss-120b-8-32-64-128k` Seq Length: 8K, BS: 2, 4, 6, 8 Seq Length: 32K, BS: 2, 4, 6, 8 Seq Length: 64K, BS: 2, 4 Seq Length: 128K, BS: 2
`gpt-oss-20b`	`dyt-gpt-oss-20b-32-64-128k` †	Homogeneous bundle with constrained decoding for `gpt-oss-20b`.	View `dyt-gpt-oss-20b-32-64-128k` Seq Length: 32K, BS: 2, 4, 6, 8 Seq Length: 64K, BS: 2, 4 Seq Length: 128K, BS: 2
`E5-Mistral-7B-Instruct` / `Whisper-Large-v3` / `gemma-3-12b-it` / `gpt-oss-120b`	gpt-gemma-whisper-mistral	Small to medium context length combining embeddings, transcription, image understanding, and a tool-calling agent	View Models: `E5-Mistral-7B-Instruct` Seq Length: 4K, BS: 1 `Whisper-Large-v3` BS: 1 `gemma-3-12b-it` Seq Length: 128K, BS: 2, 4, 6, 8 `gpt-oss-120b` Seq Length: 8K, BS: 2, 4, 6, 8
`Llama-4-Maverick-17B-128E-Instruct`	llama-4-medium-8-16-32-64-128k	Homogeneous bundles containing `Llama-4-Maverick-17B-128E-Instruct` configurations. Small to large context length with low batch	View `Llama-4-Maverick-17B-128E-Instruct` Seq Length: 8K, BS: 1 Seq Length: 16K, BS: 1 Seq Length: 32K, BS: 1 Seq Length: 64K, BS: 1 Seq Length: 128K, BS: 1
`Llama-4-Maverick-17B-128E-Instruct`	llama-4-medium-ss-16k-bs24	Homogeneous bundle containing `Llama-4-Maverick-17B-128E-Instruct` configurations; medium context length with higher batch size.	View `Llama-4-Maverick-17B-128E-Instruct` Seq Length: 16K, BS: 2, 4
`Meta-Llama-3.3-70B-Instruct`	70b-3dot3-ss-4-8-16-32-64-128k	Medium to large context length with low batch size	View Target Models: `Meta-Llama-3.3-70B-Instruct` Seq Length: 4K, BS: 2, 4, 8, 16, 32 Seq Length: 8K, BS: 2, 4, 8, 16, 32 Seq Length: 16K, BS: 1, 2, 4 Seq Length: 32K, BS: 1, 2, 4 Seq Length: 64K, BS: 1, 2, 4 Seq Length: 128K, BS: 1 Draft Models: `Meta-Llama-3.2-1B-Instruct` Seq Length: 4K, BS: 2, 4, 8, 16, 32 Seq Length: 8K, BS: 2, 4, 8, 16, 32 Seq Length: 16K, BS: 1, 2, 4 Seq Length: 32K, BS: 1, 2, 4 Seq Length: 64K, BS: 1, 2, 4; private: true Seq Length: 128K, BS: 1; private: true
`Meta-Llama-3.3-70B-Instruct` / `Whisper-Large-v3`	70b-3dot3-ss-full-whisper	Full context-length range with low to medium batch size, plus Whisper transcription Speculative decoding supported for `Meta-Llama-3.3-70B-Instruct`	View Target Models: `Meta-Llama-3.3-70B-Instruct` Seq Length: 4K, BS: 2, 4, 8, 16, 32 Seq Length: 8K, BS: 2, 4, 8, 16, 32 Seq Length: 16K, BS: 1, 2, 4 Seq Length: 32K, BS: 1, 2, 4 Seq Length: 64K, BS: 1, 2, 4 Seq Length: 128K, BS: 1 `Whisper-Large-v3` BS: 1, 16, 32 Draft Models: `Meta-Llama-3.2-1B-Instruct` Seq Length: 4K, BS: 2, 4, 8, 16, 32 Seq Length: 8K, BS: 2, 4, 8, 16, 32 Seq Length: 16K, BS: 1, 2, 4 Seq Length: 32K, BS: 1, 2, 4 Seq Length: 64K, BS: 1, 2, 4 Seq Length: 128K, BS: 1
`MiniMax-M2.5`	dyt-minimax-m2p5-32k dyt-minimax-m2p5-32-160k	Homogeneous bundles containing `MiniMax-M2.5` configurations. dyt-minimax-m2p5-32k is better for medium sequence lengths and high batching. dyt-minimax-m2p5-32-160k is better for higher sequence lengths and low batching	View dyt-minimax-m2p5-32k Seq Length: 4K-32K, BS: 2, 4, 6, 8 dyt-minimax-m2p5-32-160k Seq Length: 32K, BS: 2 Seq Length: 160K, BS: 2
`MiniMax-M2.7`	dyt-minimax-m2p7-32k-v2	Homogeneous bundle containing `MiniMax-M2.7` configurations; medium context length with high batching.	View `MiniMax-M2.7` Seq Length: 8K-32K, BS: 2, 4, 6, 8
`MiniMax-M2.7`	dyt-minimax-m2p7-32-160-192k	Homogeneous bundle containing `MiniMax-M2.7` configurations; better for higher sequence lengths and low batching.	View `MiniMax-M2.7` Seq Length: 8K-32K, BS: 2, 4, 6, 8 Seq Length: 160K-192K, BS: 2
`MiniMax-M2.7`	dyt-minimax-m2p7-32-64-160-192k-pc	Homogeneous bundle with prompt caching for `MiniMax-M2.7`.	View `MiniMax-M2.7` Seq Length: 8K-32K, BS: 2, 4, 6, 8 Seq Length: 64K, BS: 2, 4 Seq Length: 160K-192K, BS: 2
`MiniMax-M2.7`	dyt-minimax-m2p7-32-64-160-192k	Homogeneous bundle containing `MiniMax-M2.7` configurations; same context range as `dyt-minimax-m2p7-32-64-160-192k-pc` without prompt caching.	View `MiniMax-M2.7` Seq Length: 8K-32K, BS: 2, 4, 6, 8 Seq Length: 64K, BS: 2 Seq Length: 160K-192K, BS: 2
`MiniMax-M2.7`	dyt-minimax-m2p7-32k-pc	Homogeneous bundle with prompt caching for `MiniMax-M2.7`; medium context length with high batching.	View `MiniMax-M2.7` Seq Length: 8K-32K, BS: 2, 4, 6, 8
`MiniMax-M3`^PREVIEW	minimax-m3-32k	Homogeneous bundle containing `MiniMax-M3` configurations; medium context length. Supports text, image, and video input.	View `MiniMax-M3` Seq Length: 8K-32K, BS: 1, 2, 4
`MiniMax-M3`^PREVIEW	minimax-m3-512k	Homogeneous bundle containing `MiniMax-M3` configurations. Up to 512K context; text, image, and video input at all sequence lengths.	View `MiniMax-M3` Seq Length: 8K-32K, BS: 1, 2, 4 Seq Length: 64K, BS: 1, 2, 4 Seq Length: 128K, BS: 1, 2, 4 Seq Length: 256K, BS: 1, 2, 4 Seq Length: 512K, BS: 1
`MiniMax-M3`^PREVIEW	minimax-m3-32-64-128-256-512k-1m	Homogeneous bundle containing `MiniMax-M3` configurations. Up to 1M context; text, image, and video input at all sequence lengths.	View `MiniMax-M3` Seq Length: 8K-32K, BS: 1, 2, 4 Seq Length: 64K, BS: 1, 2, 4 Seq Length: 128K, BS: 1, 2, 4 Seq Length: 256K, BS: 1, 2, 4 Seq Length: 512K, BS: 1 Seq Length: 1M, BS: 1
`MiniMax-M3`^PREVIEW	`minimax-m3-acb-prefix-caching`	Homogeneous bundle for `MiniMax-M3` with prompt caching, continuous batching, and constrained decoding. Up to 1M context. Text input only. The other `MiniMax-M3` bundles serve text, image, and video.	View `MiniMax-M3` Seq Length: 1M, BS: 2
`Mistral-Large-3-675B-Instruct-2512`^PREVIEW	mistral-large-3-fp8-8k	Homogeneous bundle containing `Mistral-Large-3-675B-Instruct-2512` configurations. Preview model – text-only.	View `Mistral-Large-3-675B-Instruct-2512` Seq Length: 8K, BS: 1
`Mistral-Large-3-675B-Instruct-2512`^PREVIEW	mistral-large-3-fp8-8-16-32k	Homogeneous bundle containing `Mistral-Large-3-675B-Instruct-2512` configurations. Wider context range than `mistral-large-3-fp8-8k`. Preview model – text-only.	View `Mistral-Large-3-675B-Instruct-2512` Seq Length: 8K, BS: 1, 4 Seq Length: 16K, BS: 1, 2 Seq Length: 32K, BS: 1
`Qwen3-235B-A22B-Instruct-2507`	dyt-qwen3-235b-32-128k	Homogeneous bundle containing `Qwen3-235B-A22B-Instruct-2507` configurations.	View `Qwen3-235B-A22B-Instruct-2507` Seq Length: 32K, BS: 2, 4, 6, 8 Seq Length: 128K, BS: 2
`Whisper-Large-v3` / `Qwen3-32B` / `E5-Mistral-7B-Instruct`	qwen3-32b-whisper-e5-mistral	Small to medium context length with varied batch size	View `E5-Mistral-7B-Instruct` Seq Length: 4K, BS: 1, 4, 8, 16, 32 `Qwen3-32B` Seq Length: 8K, BS: 1, 4 Seq Length: 16K, BS: 1 Seq Length: 32K, BS: 1, 2 `Whisper-Large-v3` BS: 1, 16, 32
`Qwen3-32B` / `Meta-Llama-3.1-405B-Instruct`	qwen3-32b-llama405b-s-m	Small to medium context length Speculative decoding supported for `Meta-Llama-3.1-405B-Instruct`	View Target Models: `Meta-Llama-3.1-405B-Instruct` Seq Length: 4K, BS: 1, 2, 4 Seq Length: 8K, BS: 1 Seq Length: 16K, BS: 1 Draft Models: `Meta-Llama-3.1-8B-Instruct` Seq Length: 16K, BS: 1 `Meta-Llama-3.2-3B-Instruct` Seq Length: 4K, BS: 1, 2, 4 Seq Length: 8K, BS: 1 Routable Models: `Qwen3-32B` Seq Length: 8K, BS: 1, 4 Seq Length: 16K, BS: 1 Seq Length: 32K, BS: 1
`Qwen3-TTS-Talker`^PREVIEW	qwen3-tts-talker	Homogeneous bundle containing `Qwen3-TTS-Talker` configurations.	View `Qwen3-TTS-Talker` Seq Length: 4K, BS: 2, 4, 8, 16
`Qwen3-TTS-Vocoder`^PREVIEW	qwen3-tts-vocoder	Homogeneous bundle containing `Qwen3-TTS-Vocoder` configurations.	View `Qwen3-TTS-Vocoder` Codes length: 5, 8, 10, 16, BS: 16

† cd-dyt-gpt-oss-120b-8-32-64-128k supports sequence lengths from 8K–128K, and dyt-gpt-oss-20b-32-64-128k supports 32K–128K. If you require shorter context lengths (4K–16K) for either model, contact your SambaNova representative.

​All supported models

​Custom checkpoints

​Finding models on your cluster

​Recommended model bundles

​Deployment options

​Suggested bundles per model

​Bundle configurations

​Model details

​Meta

​Meta-Llama-3.3-70B-Instruct

​Meta-Llama-3.1-8B-Instruct

​Meta-Llama-3.1-70B-Instruct

​Meta-Llama-3.1-405B-Instruct

​Llama-4-Maverick-17B-128E-Instruct

​MiniMax

​MiniMax-M3

​MiniMax-M2.7

​MiniMax-M2.5

​Mistral AI

​Mistral-Large-3-675B-Instruct-2512

​DeepSeek

​DeepSeek-R1-0528

​DeepSeek-R1-Distill-Llama-70B

​DeepSeek-V3-0324

​DeepSeek-V3.1

​DeepSeek-V3.2

​DeepSeek-V3.1-Terminus

​OpenAI

​gpt-oss-120b

​gpt-oss-20b

​Whisper-Large-v3

​Google

​gemma-3-27b-it

​gemma-3-12b-it

​gemma-4-31B-it

​Alibaba Cloud

​Qwen3-235B-A22B-Instruct-2507

​Qwen3-32B

​Qwen3-TTS-Talker

​Qwen3-TTS-Vocoder

​Tokyotech-llm

​Llama-3.3-Swallow-70B-Instruct-v0.4

​Other

​E5-Mistral-7B-Instruct

All supported models

Custom checkpoints

Finding models on your cluster

Recommended model bundles

Deployment options

Suggested bundles per model

Bundle configurations

Model details

Meta

Meta-Llama-3.3-70B-Instruct

Meta-Llama-3.1-8B-Instruct

Meta-Llama-3.1-70B-Instruct

Meta-Llama-3.1-405B-Instruct

Llama-4-Maverick-17B-128E-Instruct

MiniMax

MiniMax-M3

MiniMax-M2.7

MiniMax-M2.5

Mistral AI

Mistral-Large-3-675B-Instruct-2512

DeepSeek

DeepSeek-R1-0528

DeepSeek-R1-Distill-Llama-70B

DeepSeek-V3-0324

DeepSeek-V3.1

DeepSeek-V3.2

DeepSeek-V3.1-Terminus

OpenAI

gpt-oss-120b

gpt-oss-20b

Whisper-Large-v3

Google

gemma-3-27b-it

gemma-3-12b-it

gemma-4-31B-it

Alibaba Cloud

Qwen3-235B-A22B-Instruct-2507

Qwen3-32B

Qwen3-TTS-Talker

Qwen3-TTS-Vocoder

Tokyotech-llm

Llama-3.3-Swallow-70B-Instruct-v0.4

Other

E5-Mistral-7B-Instruct