runpod · lavanya-gunreddi · Aug 14, 2026 · Aug 14, 2026 · Aug 14, 2026 · Aug 17, 2026
diff --git a/pods/choose-a-pod.mdx b/pods/choose-a-pod.mdx
@@ -1,55 +1,57 @@
 ---
 title: Choose a Pod
 description: "Select the right Pod by evaluating your resource requirements. Review setup, configuration, and operations guidance for Runpod Pods."
 sidebar_position: 3
 ---

 Selecting the right Pod configuration maximizes performance and cost efficiency for your workload. This guide helps you match your requirements to the right GPU, VRAM, and storage configuration.

 ## Quick selection by workload

 Start by identifying your primary workload type:
 
 | Workload | Recommended GPU tier | Minimum VRAM | Notes |
 |----------|---------------------|--------------|-------|
-| **LLM inference** (7B–13B params) | Mid-range (RTX 4090, L4) | 24 GB | Sufficient for most quantized models |
-| **LLM inference** (30B–70B params) | High-end (A100, H100) | 48–80 GB | May require multi-GPU setup |
+| **LLM inference** (7B–13B params) | Mid-range (RTX 4090, L4, PRO 6000 MIG 24GB) | 24 GB | Sufficient for most quantized models |
+| **LLM inference** (30B–70B params) | High-end (A100, H100, PRO 6000 MIG 48GB) | 48–80 GB | May require multi-GPU setup |
 | **LLM training/fine-tuning** | High-end (A100, H100) | 40–80 GB | Memory bandwidth critical |
-| **Image generation** (SDXL, Flux) | Mid-range (RTX 4090, L4) | 16–24 GB | Benefits from fast inference |
+| **Image generation** (SDXL, Flux) | Mid-range (RTX 4090, L4, PRO 6000 MIG 24GB) | 16–24 GB | Benefits from fast inference |
 | **Computer vision** | Entry to mid-range | 8–16 GB | Depends on model and batch size |
 | **3D rendering** | Mid-range with RT cores | 16–24 GB | RT cores accelerate ray tracing |
 | **Data processing** | CPU-focused or entry GPU | 8 GB+ | Prioritize CPU cores and RAM |
 
 For a full list of available GPUs and their specifications, see [GPU types](/references/gpu-types).
 
+<Note>RTX PRO 6000 Multi-Instance GPU (MIG) slices are partitioned GPU instances with dedicated memory and compute. Available on Secure Cloud only. All MIG slices use Blackwell architecture. Verify your CUDA version and framework versions support Blackwell before deploying.</Note>
+
 ## Estimate VRAM requirements
 
 VRAM is the most common bottleneck. Use these guidelines:

 **For LLMs:** Allocate approximately **2 GB of VRAM per billion parameters**. For example:
 - 7B model → ~14 GB VRAM
 - 13B model → ~26 GB VRAM
 - 70B model → ~140 GB VRAM (requires multi-GPU)

 <Tip>
 Quantization reduces VRAM requirements significantly. A 4-bit quantized 70B model can run on ~35 GB VRAM.
 </Tip>

 **For image models:** SDXL requires ~8 GB minimum, but 16–24 GB provides headroom for larger batch sizes and LoRA training.

 ### Resource calculators

 Use these tools to estimate your specific requirements:

 - [Hugging Face Model Memory Calculator](https://huggingface.co/spaces/hf-accelerate/model-memory-usage): Memory estimates for transformer models
 - [Can it run LLM?](https://huggingface.co/spaces/Vokturz/can-it-run-llm): Check if hardware can run specific language models
 - [VRAM Estimator](https://vram.asmirnov.xyz): GPU memory requirement approximations

 ## Storage configuration

 Choose storage based on your data persistence needs:

 | Storage type | Persists after stop? | Persists after delete? | Best for |
 |--------------|---------------------|------------------------|----------|
 | **Container disk** | No | No | OS, temporary files |
 | **Volume disk** | Yes | No | Working files, checkpoints |
@@ -63,13 +65,13 @@

 2. **Consider savings plans**: For extended usage, Runpod's [savings plans](/pods/pricing#savings-plans) reduce costs for committed usage.

 ## Secure Cloud vs Community Cloud

 | | Secure Cloud | Community Cloud |
 |---|--------------|-----------------|
 | **Infrastructure** | T3/T4 data centers | Peer-to-peer providers |
 | **Reliability** | High redundancy | Variable |
 | **Best for** | Production, sensitive data | Cost-sensitive workloads |
 | **Pricing** | Standard | Competitive |

 <Note>

diff --git a/references/gpu-types.mdx b/references/gpu-types.mdx
@@ -1,4 +1,4 @@
 ---
 title: "GPU types"
 description: "Explore the GPUs available on Runpod. Review supported options, requirements, and reference information for the Runpod platform."
 mode: "wide"
@@ -7,66 +7,68 @@

 For information on pricing, see [GPU pricing](https://www.runpod.io/pricing).

 ## GPU types

 This table lists all GPU types available on Runpod:
 {/* Table last generated: 2026-02-12 */}

 | GPU ID                                                  | Display Name      |   Memory (GB) |
 |---------------------------------------------------------|-------------------|---------------|
 | AMD Instinct MI300X OAM                                 | MI300X            |           192 |
 | NVIDIA A100 80GB PCIe                                   | A100 PCIe         |            80 |
 | NVIDIA A100-SXM4-80GB                                   | A100 SXM          |            80 |
 | NVIDIA A30                                              | A30               |            24 |
 | NVIDIA A40                                              | A40               |            48 |
 | NVIDIA B200                                             | B200              |           180 |
 | NVIDIA B300 SXM6 AC                                     | B300              |           288 |
 | NVIDIA GeForce RTX 3070                                 | RTX 3070          |             8 |
 | NVIDIA GeForce RTX 3080                                 | RTX 3080          |            10 |
 | NVIDIA GeForce RTX 3080 Ti                              | RTX 3080 Ti       |            12 |
 | NVIDIA GeForce RTX 3090                                 | RTX 3090          |            24 |
 | NVIDIA GeForce RTX 3090 Ti                              | RTX 3090 Ti       |            24 |
 | NVIDIA GeForce RTX 4070 Ti                              | RTX 4070 Ti       |            12 |
 | NVIDIA GeForce RTX 4080                                 | RTX 4080          |            16 |
 | NVIDIA GeForce RTX 4080 SUPER                           | RTX 4080 SUPER    |            16 |
 | NVIDIA GeForce RTX 4090                                 | RTX 4090          |            24 |
 | NVIDIA GeForce RTX 5080                                 | RTX 5080          |            16 |
 | NVIDIA GeForce RTX 5090                                 | RTX 5090          |            32 |
 | NVIDIA H100 80GB HBM3                                   | H100 SXM          |            80 |
 | NVIDIA H100 NVL                                         | H100 NVL          |            94 |
 | NVIDIA H100 PCIe                                        | H100 PCIe         |            80 |
 | NVIDIA H200                                             | H200 SXM          |           141 |
 | NVIDIA H200 NVL                                         | NVIDIA H200 NVL   |           143 |
 | NVIDIA L4                                               | L4                |            24 |
 | NVIDIA L40                                              | L40               |            48 |
 | NVIDIA L40S                                             | L40S              |            48 |
 | NVIDIA RTX 2000 Ada Generation                          | RTX 2000 Ada      |            16 |
 | NVIDIA RTX 4000 Ada Generation                          | RTX 4000 Ada      |            20 |
 | NVIDIA RTX 4000 SFF Ada Generation                      | RTX 4000 Ada SFF  |            20 |
 | NVIDIA RTX 5000 Ada Generation                          | RTX 5000 Ada      |            32 |
 | NVIDIA RTX 6000 Ada Generation                          | RTX 6000 Ada      |            48 |
 | NVIDIA RTX A2000                                        | RTX A2000         |             6 |
 | NVIDIA RTX A4000                                        | RTX A4000         |            16 |
 | NVIDIA RTX A4500                                        | RTX A4500         |            20 |
 | NVIDIA RTX A5000                                        | RTX A5000         |            24 |
 | NVIDIA RTX A6000                                        | RTX A6000         |            48 |
 | NVIDIA RTX PRO 4500 Blackwell                           | RTX PRO 4500      |            32 |
 | NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition | RTX PRO 6000 MaxQ |            96 |
+| NVIDIA RTX PRO 6000 Blackwell Server Edition MIG 1g.24gb        | PRO 6000 MIG 24GB  |            24 |
+| NVIDIA RTX PRO 6000 Blackwell Server Edition MIG 2g.48gb        | PRO 6000 MIG 48GB |            48 |
 | NVIDIA RTX PRO 6000 Blackwell Server Edition            | RTX PRO 6000      |            96 |
 | NVIDIA RTX PRO 6000 Blackwell Workstation Edition       | RTX PRO 6000 WK   |            96 |
 | Tesla V100-PCIE-16GB                                    | Tesla V100        |            16 |
 | Tesla V100-SXM2-16GB                                    | V100 SXM2         |            16 |
 | Tesla V100-SXM2-32GB                                    | V100 SXM2 32GB    |            32 |

 ## GPU pools

 The table below lists the GPU pools that you can use to define which GPUs are available to workers on an endpoint after deployment.

 Use GPU pools when defining requirements for repositories published to the [Runpod Hub](/hub/publishing-guide#runpod-configuration), or when specifying GPU requirements for an endpoint with the [Runpod GraphQL API](https://graphql-spec.runpod.io/).

 | Pool ID | GPUs Included                     | Memory (GB)   |
 | :-------------- | :-------------------------------- | :----- |
 | `AMPERE_16`     | A4000, A4500, RTX 4000, RTX 2000 | 16 GB  |
 | `AMPERE_24`     | L4, A5000, 3090                   | 24 GB  |
 | `ADA_24`        | 4090                              | 24 GB  |
 | `AMPERE_48`     | A6000, A40                        | 48 GB  |