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.

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.

Finding models on your cluster

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

kubectl -n <namespace> get models

All supported models

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

MiniMax

MiniMax-M2.5

DeepSeek

DeepSeek-R1-0528

DeepSeek-R1-Distill-Llama-70B

DeepSeek-V3-0324

DeepSeek-V3.1

DeepSeek-V3.2

OpenAI

gpt-oss-120b

Whisper-Large-v3

Google

gemma-3-27b-it

gemma-3-12b-it

Alibaba Cloud

Qwen3-235B-A22B-Instruct-2507

Qwen3-235B

Qwen3-32B

Tokyotech-llm

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

Other

E5-Mistral-7B-Instruct

Recommended model bundles

In SambaStack, models are not deployed individually; they are deployed as bundles. A bundle is a packaged deployment that groups one or more models together with their associated configurations, such as batch size and sequence length. 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 bundles

The table below lists the recommended bundle templates 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.

Model name	Bundle template	Bundle description	Bundle configuration
`DeepSeek-R1-0528` / `DeepSeek-V3.1`	deepseek-r1-v31-fp8-16k	Combination of: `DeepSeek-R1-0528` `DeepSeek-V3.1` Medium context length with low batch size	View Models: `DeepSeek-R1-0528` Seq Length: 16K, BS: 1 `DeepSeek-V3.1` Seq Length: 16K, BS: 1
`DeepSeek-V3-0324`	deepseek-r1-v3-fp8-16k	Combination of: `DeepSeek-R1-0528` `DeepSeek-V3-0324` Medium context length with low batch size	View Models: `DeepSeek-R1-0528` Seq Length: 16K, BS: 1 `DeepSeek-V3-0324` Seq Length: 16K, BS: 1
`E5-Mistral-7B-Instruct` / `Meta-Llama-3.1-8B-Instruct`	us-agentic-rag-1-1	Combination of: `gpt-oss-120b` `Llama-4-Maverick-17B-128E-Instruct` `Meta-Llama-3.1-8B-Instruct` `Meta-Llama-3.3-70B` (Target) `Meta-Llama-3.2-1B` (Draft) `E5-Mistral-7B-Instruct` 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
`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
`gpt-oss-120b`	`cd-dyt-gpt-oss-120b-32-64-128k` † `cd-dyt-gpt-oss-120b-8-32-64-128k` †	Homogeneous bundles with constrained decoding for `gpt-oss-120b`. `cd-dyt-gpt-oss-120b-8-32-64-128k` adds 8K context support.	View `cd-dyt-gpt-oss-120b-32-64-128k` Seq Length: 32K, BS: 2, 4, 6, 8 Seq Length: 64K, BS: 2, 4 Seq Length: 128K, BS: 2 `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
`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
`Meta-Llama-3.3-70B-Instruct`	70b-3dot3-ss-4-8-16-32-64-128k	Speculative decoding of: `Meta-Llama-3.3-70B` (Target) `Meta-Llama-3.2-1B` (Draft) 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
`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
`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
`Qwen3-235B`	qwen3-235b-8k qwen3-235b-16-32-64k qwen3-235b-128k	Homogeneous bundles containing `Qwen3-235B` configurations. qwen3-235b-8k: small context with higher batch size. qwen3-235b-16-32-64k: medium context lengths. qwen3-235b-128k: large context length.	View qwen3-235b-8k Seq Length: 8K, BS: 2, 8 qwen3-235b-16-32-64k Seq Length: 16K, BS: 2 Seq Length: 32K, BS: 2 Seq Length: 64K, BS: 2 qwen3-235b-128k Seq Length: 128K, BS: 2
`Whisper-Large-v3` / `Qwen3-32B`	qwen3-32b-whisper-e5-mistral	Combination of: `Qwen3-32B` `Whisper-Large-v3` `E5-Mistral-7B-Instruct` 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

† These bundles support sequence lengths from 8K–128K. If you require shorter context lengths (4K–16K) for gpt-oss-120b, contact your SambaNova representative.

Getting Started

Service Administration

Hardware Administration

Reference Architecture

Resources

Supported models and bundles

Deployment options

Finding models on your cluster

All supported models

Meta

Meta-Llama-3.3-70B-Instruct

Meta-Llama-3.1-8B-Instruct

Meta-Llama-3.1-405B-Instruct

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

MiniMax

MiniMax-M2.5

DeepSeek

DeepSeek-R1-0528

DeepSeek-R1-Distill-Llama-70B

DeepSeek-V3-0324

DeepSeek-V3.1

DeepSeek-V3.2

OpenAI

gpt-oss-120b

Whisper-Large-v3

Google

gemma-3-27b-it

gemma-3-12b-it

Alibaba Cloud

Qwen3-235B-A22B-Instruct-2507

Qwen3-235B

Qwen3-32B

Tokyotech-llm

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

Other

E5-Mistral-7B-Instruct

Recommended model bundles

​Deployment options

​Finding models on your cluster

​All supported models

​Meta

​Meta-Llama-3.3-70B-Instruct

​Meta-Llama-3.1-8B-Instruct

​Meta-Llama-3.1-405B-Instruct

​Llama-4-Maverick-17B-128E-Instruct

​MiniMax

​MiniMax-M2.5

​DeepSeek

​DeepSeek-R1-0528

​DeepSeek-R1-Distill-Llama-70B

​DeepSeek-V3-0324

​DeepSeek-V3.1

​DeepSeek-V3.2

​OpenAI

​gpt-oss-120b

​Whisper-Large-v3

​Google

​gemma-3-27b-it

​gemma-3-12b-it

​Alibaba Cloud

​Qwen3-235B-A22B-Instruct-2507

​Qwen3-235B

​Qwen3-32B

​Tokyotech-llm

​Llama-3.3-Swallow-70B-Instruct-v0.4

​Other

​E5-Mistral-7B-Instruct

​Recommended model bundles

Deployment options

Finding models on your cluster

All supported models

Meta

Meta-Llama-3.3-70B-Instruct

Meta-Llama-3.1-8B-Instruct

Meta-Llama-3.1-405B-Instruct

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

MiniMax

MiniMax-M2.5

DeepSeek

DeepSeek-R1-0528

DeepSeek-R1-Distill-Llama-70B

DeepSeek-V3-0324

DeepSeek-V3.1

DeepSeek-V3.2

OpenAI

gpt-oss-120b

Whisper-Large-v3

Google

gemma-3-27b-it

gemma-3-12b-it

Alibaba Cloud

Qwen3-235B-A22B-Instruct-2507

Qwen3-235B

Qwen3-32B

Tokyotech-llm

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

Other

E5-Mistral-7B-Instruct

Recommended model bundles