{"id":9492,"date":"2026-11-13T15:35:07","date_gmt":"2026-11-13T15:35:07","guid":{"rendered":"https:\/\/directmacro.com\/blog\/?p=9492"},"modified":"2026-11-13T15:35:07","modified_gmt":"2026-11-13T15:35:07","slug":"dell-pg133g-review","status":"publish","type":"post","link":"https:\/\/directmacro.com\/blog\/post\/dell-pg133g-review","title":{"rendered":"Dell PG133G Review: Specifications, Performance, and Use Cases"},"content":{"rendered":"<div class=\"fusion-fullwidth fullwidth-box fusion-builder-row-1 fusion-flex-container has-pattern-background has-mask-background nonhundred-percent-fullwidth non-hundred-percent-height-scrolling\" style=\"--awb-border-radius-top-left:0px;--awb-border-radius-top-right:0px;--awb-border-radius-bottom-right:0px;--awb-border-radius-bottom-left:0px;--awb-flex-wrap:wrap;\" ><div class=\"fusion-builder-row fusion-row fusion-flex-align-items-flex-start fusion-flex-content-wrap\" style=\"max-width:1191.75px;margin-left: calc(-5% \/ 2 );margin-right: calc(-5% \/ 2 );\"><div class=\"fusion-layout-column fusion_builder_column fusion-builder-column-0 fusion_builder_column_1_1 1_1 fusion-flex-column\" style=\"--awb-bg-size:cover;--awb-width-large:100%;--awb-margin-top-large:0px;--awb-spacing-right-large:2.375%;--awb-margin-bottom-large:0px;--awb-spacing-left-large:2.375%;--awb-width-medium:100%;--awb-order-medium:0;--awb-spacing-right-medium:2.375%;--awb-spacing-left-medium:2.375%;--awb-width-small:100%;--awb-order-small:0;--awb-spacing-right-small:2.375%;--awb-spacing-left-small:2.375%;\"><div class=\"fusion-column-wrapper fusion-column-has-shadow fusion-flex-justify-content-flex-start fusion-content-layout-column\"><script type=\"application\/ld+json\">\n{\n  \"@context\": \"https:\/\/schema.org\",\n  \"@type\": \"FAQPage\",\n  \"mainEntity\": [{\n    \"@type\": \"Question\",\n    \"name\": \"What is the Dell PG133G?\",\n    \"acceptedAnswer\": {\n      \"@type\": \"Answer\",\n      \"text\": \"The Dell PG133G is Dell's part number for the NVIDIA L40S, a 48GB GDDR6, PCIe 4.0 x16, dual-slot, 350W data center GPU built on the Ada Lovelace architecture for AI, graphics, and media workloads.\"\n    }\n  },{\n    \"@type\": \"Question\",\n    \"name\": \"Is the Dell PG133G good for deep learning?\",\n    \"acceptedAnswer\": {\n      \"@type\": \"Answer\",\n      \"text\": \"Yes, for inference, fine-tuning, and small-to-medium training. As a GPU for deep learning, it offers strong FP8 and FP16 Tensor performance, but its lack of NVLink limits large-scale multi-GPU training.\"\n    }\n  },{\n    \"@type\": \"Question\",\n    \"name\": \"How much memory does the Dell PG133G have?\",\n    \"acceptedAnswer\": {\n      \"@type\": \"Answer\",\n      \"text\": \"It has 48GB of GDDR6 memory with ECC on a 384-bit interface, delivering 864 GB\/s of memory bandwidth.\"\n    }\n  },{\n    \"@type\": \"Question\",\n    \"name\": \"Does the L40S support NVLink or MIG?\",\n    \"acceptedAnswer\": {\n      \"@type\": \"Answer\",\n      \"text\": \"No. The L40S does not support NVLink or Multi-Instance GPU. Multi-GPU communication runs over PCIe, and virtualization uses NVIDIA vGPU software.\"\n    }\n  },{\n    \"@type\": \"Question\",\n    \"name\": \"What is the difference between the L40 and L40S?\",\n    \"acceptedAnswer\": {\n      \"@type\": \"Answer\",\n      \"text\": \"Both use the same Ada Lovelace GPU and 48GB of memory, but the L40S enables the Transformer Engine and roughly doubles FP8 and FP16 Tensor throughput, with a higher 350W power limit.\"\n    }\n  },{\n    \"@type\": \"Question\",\n    \"name\": \"Which Dell servers support the PG133G?\",\n    \"acceptedAnswer\": {\n      \"@type\": \"Answer\",\n      \"text\": \"GPU-optimized PowerEdge servers such as the R760xa and R750xa support up to four double-wide GPUs. Always confirm your exact model and riser configuration in Dell's GPU support matrix.\"\n    }\n  },{\n    \"@type\": \"Question\",\n    \"name\": \"What power connector does the Dell PG133G use?\",\n    \"acceptedAnswer\": {\n      \"@type\": \"Answer\",\n      \"text\": \"It uses a single 16-pin power connector and draws up to 350W, so you need the correct Dell GPU power cable for your server.\"\n    }\n  }]\n}\n<\/script><div class=\"fusion-content-boxes content-boxes columns row fusion-columns-1 fusion-columns-total-1 fusion-content-boxes-1 content-boxes-icon-with-title content-left .table-of-content-wrapper\" style=\"--awb-item-margin-top:1px;--awb-item-margin-bottom:1px;--awb-margin-top:1px;--awb-margin-bottom:1px;--awb-hover-accent-color:var(--awb-color7);--awb-circle-hover-accent-color:var(--awb-color7);\" data-animationOffset=\"top-into-view\"><div style=\"--awb-backgroundcolor:rgba(255,255,255,0);\" class=\"fusion-column content-box-column content-box-column content-box-column-1 col-lg-12 col-md-12 col-sm-12 fusion-content-box-hover content-box-column-last content-box-column-last-in-row\"><div class=\"col content-box-wrapper content-wrapper link-area-box content-icon-wrapper-yes icon-hover-animation-none\" data-animationOffset=\"top-into-view\"><div class=\"heading heading-with-icon icon-left\"><div class=\"icon\"><span style=\"height:42px;width:42px;line-height:22px;border-color:rgba(255,255,255,0);border-width:1px;border-style:solid;background-color:var(--awb-color8);box-sizing:content-box;border-radius:50%;\"><i style=\"border-color:var(--awb-color8);border-width:1px;background-color:var(--awb-color7);box-sizing:content-box;height:40px;width:40px;line-height:40px;border-radius:50%;position:relative;top:auto;left:auto;margin:0;border-radius:50%;font-size:20px;\" aria-hidden=\"true\" class=\"fontawesome-icon fa-list-ul fas circle-yes\"><\/i><\/span><\/div><p class=\"content-box-heading\" style=\"--body_typography-font-size:24px;line-height:29px;\">Table of Contents<\/p><\/div><div class=\"fusion-clearfix\"><\/div><div class=\"content-container\">\n<hr \/>\n<\/div><\/div><\/div><div class=\"fusion-clearfix\"><\/div><\/div><div class=\"awb-toc-el awb-toc-el--1\" data-awb-toc-id=\"1\" data-awb-toc-options=\"{&quot;allowed_heading_tags&quot;:{&quot;h2&quot;:0},&quot;ignore_headings&quot;:&quot;&quot;,&quot;ignore_headings_words&quot;:&quot;&quot;,&quot;enable_cache&quot;:&quot;yes&quot;,&quot;highlight_current_heading&quot;:&quot;no&quot;,&quot;hide_hidden_titles&quot;:&quot;yes&quot;,&quot;limit_container&quot;:&quot;post_content&quot;,&quot;select_custom_headings&quot;:&quot;&quot;,&quot;icon&quot;:&quot;fa-angle-double-right fas&quot;,&quot;counter_type&quot;:&quot;custom_icon&quot;}\" style=\"--awb-margin-bottom:1px;--awb-item-font-family:&#039;Bookman Old Style&#039;, serif;--awb-item-font-style:italic;--awb-item-font-weight:700;--awb-item-overflow:hidden;--awb-item-white-space:nowrap;--awb-item-text-overflow:ellipsis;\"><div class=\"awb-toc-el__content\"><\/div><\/div><div class=\"fusion-text fusion-text-1\"><h2>Key Takeaways<\/h2>\n<p><strong>Short on time?<\/strong> These six points summarize the key findings of our Dell PG133G review before you dive into the details.<\/p>\n<ul>\n<li><strong>Universal Accelerator:<\/strong> The Dell PG133G is a Dell-branded NVIDIA L40S AI accelerator that handles LLM inference, fine-tuning, generative AI, 3D rendering, and video transcoding on one card.<\/li>\n<li><strong>Strong FP8 Throughput:<\/strong> Fourth-generation Tensor Cores with the Transformer Engine deliver up to 733 TFLOPS of dense FP8 compute (1,466 TFLOPS with sparsity).<\/li>\n<li><strong>48GB of ECC Memory:<\/strong> The 48GB GDDR6 frame buffer on a 384-bit bus comfortably serves 7B to 34B parameter models and large rendering scenes.<\/li>\n<li><strong>Standard Server Fit:<\/strong> Its PCIe 4.0 x16, dual-slot, 350W design drops into GPU-ready Dell PowerEdge servers without liquid cooling or SXM baseboards.<\/li>\n<li><strong>Know Its Limits:<\/strong> There is no NVLink, no MIG, and no meaningful FP64, so it is not the right choice for large-scale training or double-precision HPC.<\/li>\n<li><strong>Best Value Play:<\/strong> For inference-heavy and mixed AI-plus-graphics fleets, it often delivers better cost per token than HBM-based cards.<\/li>\n<\/ul>\n<h2>Introduction<\/h2>\n<p>Enterprise AI teams no longer ask whether they need GPU acceleration. They ask which card gives them the best mix of memory, throughput, power draw, and server compatibility without forcing a full platform redesign. That is exactly where the <a href=\"https:\/\/directmacro.com\/dell-pg133g-graphics-card.html\">Dell PG133G graphics card<\/a> comes in. Dell sells this card under part number PG133G, and it is the NVIDIA L40S: a 48GB GDDR6, 350W, dual-slot, full-height full-length PCIe 4.0 accelerator built on the Ada Lovelace architecture.<\/p>\n<p><img decoding=\"async\" class=\"aligncenter wp-image-9494 size-full\" src=\"https:\/\/directmacro.com\/blog\/wp-content\/uploads\/2026\/11\/Dell-PG133G-Introduction.jpg\" alt=\"Dell PG133G Introduction\" width=\"602\" height=\"336\" srcset=\"https:\/\/directmacro.com\/blog\/wp-content\/uploads\/2026\/11\/Dell-PG133G-Introduction-200x112.jpg 200w, https:\/\/directmacro.com\/blog\/wp-content\/uploads\/2026\/11\/Dell-PG133G-Introduction-300x167.jpg 300w, https:\/\/directmacro.com\/blog\/wp-content\/uploads\/2026\/11\/Dell-PG133G-Introduction-400x223.jpg 400w, https:\/\/directmacro.com\/blog\/wp-content\/uploads\/2026\/11\/Dell-PG133G-Introduction-600x335.jpg 600w, https:\/\/directmacro.com\/blog\/wp-content\/uploads\/2026\/11\/Dell-PG133G-Introduction.jpg 602w\" sizes=\"(max-width: 602px) 100vw, 602px\" \/><\/p>\n<p>In this Dell PG133G review, we break down the specifications, real-world performance, and the workloads where this NVIDIA data center GPU makes sense. We also cover server fit, power and cooling, memory and storage planning, refurbished buying tips, and how it stacks up against the A40, L40, A100, and H100. The goal is simple: help AI infrastructure teams, data center administrators, and IT buyers decide whether the PG133G belongs in their next build.<\/p>\n<\/div><div class=\"fusion-text fusion-text-2\"><h2>What Is the Dell PG133G? A Quick Dell PG133G Review Overview<\/h2>\n<p>The PG133G is Dell&#8217;s part number for the NVIDIA L40S, and many channel catalogs list it as the &#8220;Dell Tesla L40S.&#8221; NVIDIA retired the <a href=\"https:\/\/directmacro.com\/knowledge-base\/what-are-nvidia-tesla-gpus-used-for\/\">Tesla brand<\/a> years ago, but resellers still use the name to signal a passively cooled data center card rather than a workstation board. In practical terms, this is the same silicon NVIDIA ships to every major OEM, including the <a href=\"https:\/\/directmacro.com\/lenovo-4x67a90669-graphics-card.html\">Lenovo ThinkSystem L40S<\/a>, and Dell validates and labels it for PowerEdge platforms.<\/p>\n<p><img decoding=\"async\" class=\"aligncenter wp-image-9495 size-full\" src=\"https:\/\/directmacro.com\/blog\/wp-content\/uploads\/2026\/11\/What-Is-the-Dell-PG133G.jpg\" alt=\"What Is the Dell PG133G\" width=\"602\" height=\"318\" srcset=\"https:\/\/directmacro.com\/blog\/wp-content\/uploads\/2026\/11\/What-Is-the-Dell-PG133G-200x106.jpg 200w, https:\/\/directmacro.com\/blog\/wp-content\/uploads\/2026\/11\/What-Is-the-Dell-PG133G-300x158.jpg 300w, https:\/\/directmacro.com\/blog\/wp-content\/uploads\/2026\/11\/What-Is-the-Dell-PG133G-400x211.jpg 400w, https:\/\/directmacro.com\/blog\/wp-content\/uploads\/2026\/11\/What-Is-the-Dell-PG133G-600x317.jpg 600w, https:\/\/directmacro.com\/blog\/wp-content\/uploads\/2026\/11\/What-Is-the-Dell-PG133G.jpg 602w\" sizes=\"(max-width: 602px) 100vw, 602px\" \/><\/p>\n<p>NVIDIA positions the L40S as a universal NVIDIA data center GPU. NVIDIA designed it to run generative AI, LLM inference, light-to-moderate training, neural graphics, and media workloads on the same hardware. That flexibility is the core theme of this Dell PG133G review: instead of buying separate cards for AI and visualization, many teams can standardize on one SKU.<\/p>\n<ul>\n<li><strong>Quick Answer:<\/strong> The Dell PG133G is an NVIDIA L40S 48GB GDDR6 PCIe 4.0 x16 data center GPU with 18,176 CUDA cores, 568 Tensor Cores, 142 RT Cores, 864 GB\/s of memory bandwidth, and a 350W maximum power draw in a dual-slot FHFL form factor.<\/li>\n<\/ul>\n<\/div><div class=\"fusion-text fusion-text-3\"><h2><span lang=\"en-PK\" style=\"font-family: 'Arial Black',sans-serif;\">Dell PG133G Specifications<\/span><\/h2>\n<p><img decoding=\"async\" class=\"aligncenter wp-image-9496 size-full\" src=\"https:\/\/directmacro.com\/blog\/wp-content\/uploads\/2026\/11\/Dell-PG133G-Specifications.jpg\" alt=\"Dell PG133G Specifications\" width=\"602\" height=\"336\" srcset=\"https:\/\/directmacro.com\/blog\/wp-content\/uploads\/2026\/11\/Dell-PG133G-Specifications-200x112.jpg 200w, https:\/\/directmacro.com\/blog\/wp-content\/uploads\/2026\/11\/Dell-PG133G-Specifications-300x167.jpg 300w, https:\/\/directmacro.com\/blog\/wp-content\/uploads\/2026\/11\/Dell-PG133G-Specifications-400x223.jpg 400w, https:\/\/directmacro.com\/blog\/wp-content\/uploads\/2026\/11\/Dell-PG133G-Specifications-600x335.jpg 600w, https:\/\/directmacro.com\/blog\/wp-content\/uploads\/2026\/11\/Dell-PG133G-Specifications.jpg 602w\" sizes=\"(max-width: 602px) 100vw, 602px\" \/><\/p>\n<p>The table below summarizes the key specifications buyers should verify against any listing for the Dell PG133G graphics card.<\/p>\n<figure class=\"wp-block-table is-style-stripes\">\n<table>\n<thead>\n<tr>\n<th>Specification<\/th>\n<th>Dell PG133G (NVIDIA L40S)<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td><strong>GPU Architecture<\/strong><\/td>\n<td>NVIDIA Ada Lovelace (AD102)<\/td>\n<\/tr>\n<tr>\n<td><strong>GPU Memory<\/strong><\/td>\n<td>48GB GDDR6 with ECC<\/td>\n<\/tr>\n<tr>\n<td><strong>Memory Interface \/ Bandwidth<\/strong><\/td>\n<td>384-bit \/ 864 GB\/s<\/td>\n<\/tr>\n<tr>\n<td><strong>CUDA Cores<\/strong><\/td>\n<td>18,176<\/td>\n<\/tr>\n<tr>\n<td><strong>Tensor Cores<\/strong><\/td>\n<td>568 (4th generation, Transformer Engine)<\/td>\n<\/tr>\n<tr>\n<td><strong>RT Cores<\/strong><\/td>\n<td>142 (3rd generation)<\/td>\n<\/tr>\n<tr>\n<td><strong>FP32<\/strong><\/td>\n<td>91.6 TFLOPS<\/td>\n<\/tr>\n<tr>\n<td><strong>TF32 Tensor Core<\/strong><\/td>\n<td>183 \/ 366 TFLOPS (dense \/ sparse)<\/td>\n<\/tr>\n<tr>\n<td><strong>BF16 \/ FP16 Tensor Core<\/strong><\/td>\n<td>362 \/ 733 TFLOPS (dense \/ sparse)<\/td>\n<\/tr>\n<tr>\n<td><strong>FP8 Tensor Core<\/strong><\/td>\n<td>733 \/ 1,466 TFLOPS (dense \/ sparse)<\/td>\n<\/tr>\n<tr>\n<td><strong>RT Core Performance<\/strong><\/td>\n<td>212 TFLOPS<\/td>\n<\/tr>\n<tr>\n<td><strong>L2 Cache<\/strong><\/td>\n<td>96MB<\/td>\n<\/tr>\n<tr>\n<td><strong>Media Engines<\/strong><\/td>\n<td>3x NVENC (AV1), 3x NVDEC (AV1), 4x NVJPEG<\/td>\n<\/tr>\n<tr>\n<td><strong>Interface<\/strong><\/td>\n<td>PCI Express 4.0 x16 (64 GB\/s bidirectional)<\/td>\n<\/tr>\n<tr>\n<td><strong>Display Outputs<\/strong><\/td>\n<td>4x DisplayPort 1.4a<\/td>\n<\/tr>\n<tr>\n<td><strong>Power Connector \/ Max Power<\/strong><\/td>\n<td>1x 16-pin \/ 350W<\/td>\n<\/tr>\n<tr>\n<td><strong>Form Factor \/ Cooling<\/strong><\/td>\n<td>Dual-slot, FHFL (4.4 in. x 10.5 in.), passive<\/td>\n<\/tr>\n<tr>\n<td><strong>Multi-GPU Interconnect<\/strong><\/td>\n<td>None (no NVLink)<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/figure>\n<p>Two lines in that table shape data center GPU performance the most for AI buyers. First, FP8 support with the Transformer Engine roughly doubles throughput over FP16 for transformer models. Second, the 864 GB\/s bandwidth is high for GDDR6 but well below HBM cards, which shapes where this AI accelerator shines and where it does not.<\/p>\n<\/div><div class=\"fusion-text fusion-text-4\"><h2>Architecture Deep Dive: What Makes the L40S Different<\/h2>\n<p>To understand why the Dell PG133G graphics card performs the way it does, it helps to look under the hood. The L40S combines three building blocks that set it apart from older NVIDIA data center GPU models: fourth-generation Tensor Cores with the Transformer Engine, a large ECC-protected memory pool, and a full graphics and media pipeline. Each one shapes the workloads the card handles best, as the sections below explain.<\/p>\n<p><img decoding=\"async\" class=\"aligncenter wp-image-9497 size-full\" src=\"https:\/\/directmacro.com\/blog\/wp-content\/uploads\/2026\/11\/Architecture-Deep-Dive.jpg\" alt=\"Architecture Deep Dive\" width=\"602\" height=\"318\" srcset=\"https:\/\/directmacro.com\/blog\/wp-content\/uploads\/2026\/11\/Architecture-Deep-Dive-200x106.jpg 200w, https:\/\/directmacro.com\/blog\/wp-content\/uploads\/2026\/11\/Architecture-Deep-Dive-300x158.jpg 300w, https:\/\/directmacro.com\/blog\/wp-content\/uploads\/2026\/11\/Architecture-Deep-Dive-400x211.jpg 400w, https:\/\/directmacro.com\/blog\/wp-content\/uploads\/2026\/11\/Architecture-Deep-Dive-600x317.jpg 600w, https:\/\/directmacro.com\/blog\/wp-content\/uploads\/2026\/11\/Architecture-Deep-Dive.jpg 602w\" sizes=\"(max-width: 602px) 100vw, 602px\" \/><\/p>\n<h3>1.\u00a0 Fourth-Generation Tensor Cores and the Transformer Engine<\/h3>\n<p>The L40S uses the same Ada Lovelace Tensor Core generation found in NVIDIA&#8217;s <a href=\"https:\/\/directmacro.com\/pny-vcnrtx6000ada-pb-graphics-card.html\">professional RTX lineup<\/a>, but with the Transformer Engine enabled. The Transformer Engine dynamically selects FP8 or FP16 precision layer by layer, so inference frameworks such as TensorRT-LLM and vLLM can run models in FP8 with minimal accuracy loss. This is the single biggest reason the L40S outperforms the older A40 and even the standard L40 on transformer workloads.<\/p>\n<h3>2.\u00a0 48GB GDDR6 With ECC<\/h3>\n<p>ECC protects the 48GB frame buffer, which matters for long-running inference services and production rendering jobs where silent memory errors are unacceptable. A 96MB L2 cache helps offset the bandwidth gap versus HBM by keeping more working data close to the cores. That combination makes the card a dependable GPU for deep learning inference batches and diffusion pipelines.<\/p>\n<h3>3.\u00a0 RT Cores, Media Engines, and Display Outputs<\/h3>\n<p>Unlike a pure compute AI accelerator such as the A100 or H100, the L40S keeps a full graphics pipeline. It has 142 third-generation RT Cores for ray tracing, DLSS 3 support, and three NVENC and three NVDEC engines with AV1. The card includes four DisplayPort 1.4a outputs, although most data center and vGPU deployments disable them and access the card remotely. If you need local display output, confirm the card&#8217;s display mode setting before deployment.<\/p>\n<p>Security also gets attention. The L40S supports secure boot with a hardware root of trust, and NVIDIA lists it as NEBS Level 3 ready, which is useful for telecom and edge data centers with strict environmental standards.<\/p>\n<h2>Data Center GPU Performance in Real Workloads<\/h2>\n<p>Specifications only tell part of the story, so this part of our Dell PG133G review focuses on results. Here is how data center GPU performance on the PG133G translates into the workloads buyers actually run.<\/p>\n<h3>\u00b7\u00a0 LLM Inference and Fine-Tuning<\/h3>\n<p>Inference is where the L40S earns its reputation as an AI accelerator. NVIDIA rates it at up to five times the inference performance of the <a href=\"https:\/\/directmacro.com\/nvidia-699-2g133-0200-c00-graphics-card.html\">previous-generation A40<\/a>, largely thanks to FP8. In practice, a single card serves 7B and 13B parameter models at FP16 with room for large KV caches, and 30B to 34B class models fit comfortably in FP8. A 70B model needs 4-bit quantization or two to four cards with tensor parallelism over PCIe.<\/p>\n<p>For fine-tuning, the 48GB buffer supports LoRA and QLoRA on mid-sized models without much friction. NVIDIA has also claimed meaningful training gains over the A100 on some generative AI workloads, but treat that as a best case. As a GPU for deep learning, the L40S is excellent for fine-tuning and small-to-medium training jobs, yet its lack of NVLink limits how efficiently it scales across many GPUs.<\/p>\n<h3>\u00b7\u00a0 Generative AI, Rendering, and Omniverse<\/h3>\n<p>Image and video generation pipelines such as Stable Diffusion XL benefit from both the Tensor Cores and the large frame buffer. The RT Cores deliver 212 TFLOPS of ray-tracing throughput, which makes the card a strong fit for NVIDIA Omniverse, digital twins, product visualization, and final-frame rendering in tools like V-Ray, Redshift, and Arnold GPU. Few accelerators can switch between AI inference and photorealistic rendering this smoothly.<\/p>\n<h3>\u00b7 Video Transcoding and Streaming<\/h3>\n<p>With three NVENC and three NVDEC engines plus AV1 support, this NVIDIA data center GPU goes beyond AI: it handles high-density transcoding, cloud gaming, and video analytics. Media companies can pair encoding with AI tasks like captioning, upscaling, or content moderation on the same card, which simplifies the stack and improves utilization.<\/p>\n<\/div><div class=\"fusion-text fusion-text-5\"><h2>Who Should Buy the Dell PG133G Graphics Card?<\/h2>\n<p>The Dell PG133G graphics card is a strong match for teams whose workloads are inference-heavy or mixed. It fits buyers who want a flexible NVIDIA data center GPU that delivers maximum value per PCIe slot.<\/p>\n<ol>\n<li><strong>AI Infrastructure Teams:<\/strong> Organizations serving chatbots, copilots, RAG pipelines, and embeddings at scale will see excellent throughput per dollar with FP8.<\/li>\n<li><strong>Machine Learning Engineers:<\/strong> Practitioners who need a GPU for deep learning to fine-tune 7B to 34B models and run experiments that fit in 48GB of memory.<\/li>\n<li><strong>Data Center Administrators:<\/strong> Admins who standardize on one GPU SKU across AI, VDI, and rendering clusters inside existing Dell PowerEdge fleets.<\/li>\n<li><strong>Media and Visualization Studios:<\/strong> Teams that combine rendering, Omniverse, and AV1 transcoding with generative AI.<\/li>\n<li><strong>Hardware Resellers and Integrators:<\/strong> Partners who build turnkey inference appliances that need a broadly compatible, air-cooled AI accelerator.<\/li>\n<\/ol>\n<h2>Who Should Skip It?<\/h2>\n<p>No AI accelerator fits every job. In our enterprise GPU review experience, these buyers should consider alternatives:<\/p>\n<ol>\n<li><strong>Large-Scale Model Training:<\/strong> Teams pre-training large foundation models need HBM bandwidth and NVLink, which point to H100, H200, or newer platforms.<\/li>\n<li><strong>Double-Precision HPC:<\/strong> Scientific codes that depend on FP64 will perform poorly, because the L40S offers minimal FP64 throughput.<\/li>\n<li><strong>Workstation Users:<\/strong> Desktop users without server airflow should choose an actively cooled RTX professional card, such as the <a href=\"https:\/\/directmacro.com\/dell-0chtg8-graphics-cards.html\">Dell RTX A6000<\/a>, instead.<\/li>\n<li><strong>Low-Power Edge Deployments:<\/strong> Sites with tight power budgets may prefer the 72W NVIDIA L4 or the 70W <a href=\"https:\/\/directmacro.com\/dell-0hckjm-graphics-cards.html\">Dell Tesla T4<\/a> for lightweight inference.<\/li>\n<\/ol>\n<h2>Is the PG133G Suitable for Virtualization and VDI?<\/h2>\n<p>Yes, and virtualization is one of the strongest use cases in this Dell PG133G review. The L40S supports NVIDIA virtual GPU software, including NVIDIA RTX Virtual Workstation for graphics-heavy users and NVIDIA AI Enterprise for virtualized AI workloads on VMware vSphere and other supported hypervisors. You can split the 48GB frame buffer into multiple vGPU profiles to serve several designers or engineers from a single card.<\/p>\n<p><img decoding=\"async\" class=\"aligncenter wp-image-9498 size-full\" src=\"https:\/\/directmacro.com\/blog\/wp-content\/uploads\/2026\/11\/PG133G-Suitable-for-Virtualization-and-VDI.jpg\" alt=\"PG133G Suitable for Virtualization and VDI\" width=\"602\" height=\"318\" srcset=\"https:\/\/directmacro.com\/blog\/wp-content\/uploads\/2026\/11\/PG133G-Suitable-for-Virtualization-and-VDI-200x106.jpg 200w, https:\/\/directmacro.com\/blog\/wp-content\/uploads\/2026\/11\/PG133G-Suitable-for-Virtualization-and-VDI-300x158.jpg 300w, https:\/\/directmacro.com\/blog\/wp-content\/uploads\/2026\/11\/PG133G-Suitable-for-Virtualization-and-VDI-400x211.jpg 400w, https:\/\/directmacro.com\/blog\/wp-content\/uploads\/2026\/11\/PG133G-Suitable-for-Virtualization-and-VDI-600x317.jpg 600w, https:\/\/directmacro.com\/blog\/wp-content\/uploads\/2026\/11\/PG133G-Suitable-for-Virtualization-and-VDI.jpg 602w\" sizes=\"(max-width: 602px) 100vw, 602px\" \/><\/p>\n<\/div><div class=\"fusion-text fusion-text-6\"><p>One limitation to note: the L40S does not support Multi-Instance GPU (MIG). Partitioning relies on time-sliced vGPU rather than hardware-isolated instances. For high-density, lightweight VDI, the NVIDIA A16 may be more cost-efficient, but for power users running CAD, simulation, and AI in virtual desktops, the PG133G is an excellent fit. Plan vGPU licensing into the budget from day one.<\/p>\n<h2>Can It Accelerate Databases and Analytics?<\/h2>\n<p>The Dell PG133G graphics card is not a traditional database component, but it adds real value to GPU-accelerated analytics. Vector databases such as Milvus, GPU-accelerated Apache Spark with RAPIDS, and libraries like cuDF and cuML can use the 48GB buffer to speed up similarity search, ETL, and feature engineering. For retrieval-augmented generation, the same server can host the embedding model, the vector index, and the LLM. Classic OLTP databases, however, gain little from GPUs, so size those systems around CPU, memory, and storage instead.<\/p>\n<h2>Server, Power, and Cooling Requirements<\/h2>\n<p>Buying the right GPU is only half the job, so this Dell PG133G review also covers deployment. The PG133G is a 350W passively cooled AI accelerator, so the server it goes into must provide the right slot space, enough power, and strong airflow to keep it running at full speed. Below, we cover the three areas to plan before installation: server compatibility, power and cabling, and thermal design.<\/p>\n<p><img decoding=\"async\" class=\"aligncenter wp-image-9499 size-full\" src=\"https:\/\/directmacro.com\/blog\/wp-content\/uploads\/2026\/11\/PG133G-Server.jpg\" alt=\"PG133G Server\" width=\"602\" height=\"318\" srcset=\"https:\/\/directmacro.com\/blog\/wp-content\/uploads\/2026\/11\/PG133G-Server-200x106.jpg 200w, https:\/\/directmacro.com\/blog\/wp-content\/uploads\/2026\/11\/PG133G-Server-300x158.jpg 300w, https:\/\/directmacro.com\/blog\/wp-content\/uploads\/2026\/11\/PG133G-Server-400x211.jpg 400w, https:\/\/directmacro.com\/blog\/wp-content\/uploads\/2026\/11\/PG133G-Server-600x317.jpg 600w, https:\/\/directmacro.com\/blog\/wp-content\/uploads\/2026\/11\/PG133G-Server.jpg 602w\" sizes=\"(max-width: 602px) 100vw, 602px\" \/><\/p>\n<h3>\u00b7\u00a0 Compatible Dell PowerEdge Servers<\/h3>\n<p>Like any NVIDIA data center GPU in this class, the PG133G is a passive, double-wide, full-length card, so it belongs in GPU-optimized chassis. Dell designed PowerEdge platforms such as the R760xa and R750xa to host up to four double-wide GPUs, and several other current PowerEdge models support the L40S in specific riser configurations. Always confirm your exact server, riser, and BIOS level against Dell&#8217;s GPU support matrix before ordering. Browse our <a href=\"https:\/\/directmacro.com\/pc-servers\/servers.html\">compatible servers<\/a> to match the card with a validated platform.<\/p>\n<h3>\u00b7 Power and 16-Pin Cabling<\/h3>\n<p>At up to 350W per card, a four-GPU server can draw 1,400W from accelerators alone. Use redundant power supplies sized for the full configuration, typically 2,400W or higher in dense builds, and make sure you have the correct Dell 16-pin GPU power cable for your server model. Mismatched cables cause many failed installations, so ask our team for the matching cable and riser kit when you order.<\/p>\n<h3>\u00b7 Airflow and Thermal Planning<\/h3>\n<p>Because the card has no fan, it relies on chassis airflow, and poor cooling is the fastest way to throttle data center GPU performance. Set server fan profiles for GPU cooling, use blanking panels, and verify that rack inlet temperatures stay within Dell&#8217;s specifications. Our guide to <a href=\"https:\/\/directmacro.com\/blog\/post\/what-is-optimal-cpu-and-gpu-temp\">optimal GPU temperatures<\/a> explains safe operating ranges. In high-density racks, plan for 3kW to 5kW or more per GPU server.<\/p>\n<\/div><div class=\"fusion-text fusion-text-7\"><h2>How Much System RAM and Storage Make Sense?<\/h2>\n<p>Data center GPU performance depends on how fast the system feeds the card data. If you undersize system memory or storage, the Dell PG133G graphics card sits idle while models and datasets load. The guidance below helps you balance RAM and storage against the card&#8217;s 48GB frame buffer, whether you are building a single-GPU node or a four-GPU inference server.<\/p>\n<p><img decoding=\"async\" class=\"aligncenter wp-image-9500 size-full\" src=\"https:\/\/directmacro.com\/blog\/wp-content\/uploads\/2026\/11\/Dell-PG133G-Ram-And-Storage.jpg\" alt=\"Dell PG133G Ram And Storage\" width=\"602\" height=\"318\" srcset=\"https:\/\/directmacro.com\/blog\/wp-content\/uploads\/2026\/11\/Dell-PG133G-Ram-And-Storage-200x106.jpg 200w, https:\/\/directmacro.com\/blog\/wp-content\/uploads\/2026\/11\/Dell-PG133G-Ram-And-Storage-300x158.jpg 300w, https:\/\/directmacro.com\/blog\/wp-content\/uploads\/2026\/11\/Dell-PG133G-Ram-And-Storage-400x211.jpg 400w, https:\/\/directmacro.com\/blog\/wp-content\/uploads\/2026\/11\/Dell-PG133G-Ram-And-Storage-600x317.jpg 600w, https:\/\/directmacro.com\/blog\/wp-content\/uploads\/2026\/11\/Dell-PG133G-Ram-And-Storage.jpg 602w\" sizes=\"(max-width: 602px) 100vw, 602px\" \/><\/p>\n<h3><strong>1.\u00a0 <\/strong>System Memory<\/h3>\n<p>Many buyers who choose a GPU for deep learning overlook memory planning, but it directly affects GPU utilization. A good rule of thumb is to provision at least twice as much system RAM as total GPU memory. For a single 48GB card, start at 128GB. For a four-GPU inference node with 192GB of combined frame buffer, 512GB of DDR5 is a sensible baseline, and 1TB helps when you load multiple models, run large data pipelines, or use CPU offloading. Populate all memory channels on both sockets, for example with <a href=\"https:\/\/directmacro.com\/hynix-hmct04aeera137n-server-memory.html\">128GB DDR5 RDIMMs<\/a>, to maximize bandwidth.<\/p>\n<h3><strong>2.\u00a0 <\/strong>Storage Configuration<\/h3>\n<p>NVMe is the right default for any AI accelerator node. Use a mirrored pair of boot drives, then add 3.84TB to 15.36TB NVMe SSDs for model weights, datasets, and checkpoints. Fast local storage shortens model load times and keeps the GPUs fed during fine-tuning. For larger clusters, pair local NVMe cache with a shared parallel file system or object storage. To learn how faster drives keep GPUs busy, read our guide to <a href=\"https:\/\/directmacro.com\/blog\/post\/next-gen-pcie-gen5-ssds-for-ai-and-big-data-acceleration\">PCIe Gen5 SSDs for AI<\/a>.<\/p>\n<\/div><div class=\"fusion-text fusion-text-8\"><h2><span lang=\"en-PK\" style=\"font-family: 'Arial Black',sans-serif;\">What Networking Configuration Should You Plan For?<\/span><\/h2>\n<p><img decoding=\"async\" class=\"aligncenter wp-image-9501 size-full\" src=\"https:\/\/directmacro.com\/blog\/wp-content\/uploads\/2026\/11\/Dell-PG133G-Networking-Configuration.jpg\" alt=\"Dell PG133G Networking Configuration\" width=\"602\" height=\"318\" srcset=\"https:\/\/directmacro.com\/blog\/wp-content\/uploads\/2026\/11\/Dell-PG133G-Networking-Configuration-200x106.jpg 200w, https:\/\/directmacro.com\/blog\/wp-content\/uploads\/2026\/11\/Dell-PG133G-Networking-Configuration-300x158.jpg 300w, https:\/\/directmacro.com\/blog\/wp-content\/uploads\/2026\/11\/Dell-PG133G-Networking-Configuration-400x211.jpg 400w, https:\/\/directmacro.com\/blog\/wp-content\/uploads\/2026\/11\/Dell-PG133G-Networking-Configuration-600x317.jpg 600w, https:\/\/directmacro.com\/blog\/wp-content\/uploads\/2026\/11\/Dell-PG133G-Networking-Configuration.jpg 602w\" sizes=\"(max-width: 602px) 100vw, 602px\" \/><\/p>\n<p>Networking has a direct effect on data center GPU performance. Since the L40S has no NVLink, multi-GPU and multi-node communication travels over PCIe and the network. For single-node inference servers, a <a href=\"https:\/\/directmacro.com\/mellanox-mcx621102an-adat.html\">dual-port 25GbE ConnectX-6 adapter<\/a> usually handles API traffic and storage access. For distributed fine-tuning or clusters that share a storage fabric, step up to 100GbE or 200GbE with RoCE, or InfiniBand, using adapters such as the <a href=\"https:\/\/directmacro.com\/mellanox-mcx623106an-cdat.html\">100GbE ConnectX-6 Dx<\/a>. A common pattern is one high-speed NIC per two GPUs, placed on the same CPU socket to reduce latency. NVIDIA&#8217;s L40S reference designs also pair the GPU with BlueField DPUs for secure, offloaded networking.<\/p>\n<h2><span lang=\"en-PK\" style=\"font-family: 'Arial Black',sans-serif;\">Dell PG133G vs. Similar Data Center GPUs<\/span><\/h2>\n<p><img decoding=\"async\" class=\"aligncenter wp-image-9502 size-full\" src=\"https:\/\/directmacro.com\/blog\/wp-content\/uploads\/2026\/11\/Dell-PG133G-vs.-Similar-Data-Center-GPUs.jpg\" alt=\"Dell PG133G vs. Similar Data Center GPUs\" width=\"602\" height=\"318\" srcset=\"https:\/\/directmacro.com\/blog\/wp-content\/uploads\/2026\/11\/Dell-PG133G-vs.-Similar-Data-Center-GPUs-200x106.jpg 200w, https:\/\/directmacro.com\/blog\/wp-content\/uploads\/2026\/11\/Dell-PG133G-vs.-Similar-Data-Center-GPUs-300x158.jpg 300w, https:\/\/directmacro.com\/blog\/wp-content\/uploads\/2026\/11\/Dell-PG133G-vs.-Similar-Data-Center-GPUs-400x211.jpg 400w, https:\/\/directmacro.com\/blog\/wp-content\/uploads\/2026\/11\/Dell-PG133G-vs.-Similar-Data-Center-GPUs-600x317.jpg 600w, https:\/\/directmacro.com\/blog\/wp-content\/uploads\/2026\/11\/Dell-PG133G-vs.-Similar-Data-Center-GPUs.jpg 602w\" sizes=\"(max-width: 602px) 100vw, 602px\" \/><\/p>\n<p>This part of our Dell PG133G review compares the card with other popular NVIDIA data center GPU options. The table below highlights the data center GPU performance differences that drive most purchase decisions.<\/p>\n<figure class=\"wp-block-table is-style-stripes\">\n<table>\n<thead>\n<tr>\n<th>Similar GPUs<\/th>\n<th>Memory \/ Bandwidth<\/th>\n<th>FP8 (Sparse)<\/th>\n<th>Power<\/th>\n<th>NVLink<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td><strong>L40S (Dell PG133G)<\/strong><\/td>\n<td>48GB GDDR6 \/ 864 GB\/s<\/td>\n<td>1,466 TFLOPS<\/td>\n<td>350W<\/td>\n<td>No<\/td>\n<\/tr>\n<tr>\n<td><strong>NVIDIA L40<\/strong><\/td>\n<td>48GB GDDR6 \/ 864 GB\/s<\/td>\n<td>724 TFLOPS<\/td>\n<td>300W<\/td>\n<td>No<\/td>\n<\/tr>\n<tr>\n<td><strong>NVIDIA A40<\/strong><\/td>\n<td>48GB GDDR6 \/ 696 GB\/s<\/td>\n<td>Not supported<\/td>\n<td>300W<\/td>\n<td>Yes (bridge)<\/td>\n<\/tr>\n<tr>\n<td><strong>NVIDIA A100 80GB PCIe<\/strong><\/td>\n<td>80GB HBM2e \/ 1,935 GB\/s<\/td>\n<td>Not supported<\/td>\n<td>300W<\/td>\n<td>Yes (bridge)<\/td>\n<\/tr>\n<tr>\n<td><strong>NVIDIA H100 NVL<\/strong><\/td>\n<td>94GB HBM3 \/ 3.9 TB\/s<\/td>\n<td>3,341 TFLOPS<\/td>\n<td>350\u2013400W<\/td>\n<td>Yes (bridge)<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/figure>\n<p>Here is what those numbers mean in practice when you compare the PG133G with each alternative:<\/p>\n<ul>\n<li><strong> L40:<\/strong> Same memory and core count, but the L40S doubles FP8 and FP16 throughput with the Transformer Engine. For AI, the L40S is the clear pick.<\/li>\n<li><strong> A40:<\/strong> The L40S is a generational leap, with FP8 support and far higher FP32 and Tensor throughput. The A40 makes sense only as a budget graphics or VDI option.<\/li>\n<li><strong> A100 80GB:<\/strong> The <a href=\"https:\/\/directmacro.com\/blog\/post\/nvidia-a100-in-2025\">A100<\/a> wins on memory capacity, bandwidth, FP64, and MIG. The L40S often wins on inference cost and adds graphics and media features.<\/li>\n<li><strong> H100 NVL:<\/strong> The <a href=\"https:\/\/directmacro.com\/blog\/post\/nvidia-h100-a-new-revolution-to-gpus-artificial-intelligence\">H100 NVL<\/a> is dramatically faster for training and large models, but it costs much more. The L40S delivers better value as a GPU for deep learning inference on mid-sized models.<\/li>\n<\/ul>\n<p>If you are comparing across generations, our <a href=\"https:\/\/directmacro.com\/blog\/post\/gpu-hierarchy-benchmarks\">GPU hierarchy benchmarks guide<\/a> offers broader context, and our <a href=\"https:\/\/directmacro.com\/blog\/post\/hp-rtx-6000-ada-review\">HP RTX 6000 Ada review<\/a> covers the workstation card built on the same Ada Lovelace silicon.<\/p>\n<\/div><div class=\"fusion-text fusion-text-9\"><h2><span lang=\"en-PK\" style=\"font-family: 'Arial Black',sans-serif;\">Is Refurbished Hardware Worth Considering?<\/span><\/h2>\n<p><img decoding=\"async\" class=\"aligncenter wp-image-9503 size-full\" src=\"https:\/\/directmacro.com\/blog\/wp-content\/uploads\/2026\/11\/Is-Refurbished-Hardware-Worth-Considering-3.jpg\" alt=\"Is Refurbished Hardware Worth Considering\" width=\"602\" height=\"318\" srcset=\"https:\/\/directmacro.com\/blog\/wp-content\/uploads\/2026\/11\/Is-Refurbished-Hardware-Worth-Considering-3-200x106.jpg 200w, https:\/\/directmacro.com\/blog\/wp-content\/uploads\/2026\/11\/Is-Refurbished-Hardware-Worth-Considering-3-300x158.jpg 300w, https:\/\/directmacro.com\/blog\/wp-content\/uploads\/2026\/11\/Is-Refurbished-Hardware-Worth-Considering-3-400x211.jpg 400w, https:\/\/directmacro.com\/blog\/wp-content\/uploads\/2026\/11\/Is-Refurbished-Hardware-Worth-Considering-3-600x317.jpg 600w, https:\/\/directmacro.com\/blog\/wp-content\/uploads\/2026\/11\/Is-Refurbished-Hardware-Worth-Considering-3.jpg 602w\" sizes=\"(max-width: 602px) 100vw, 602px\" \/><\/p>\n<p>For many buyers, yes. A refurbished Dell PG133G graphics card is one of the most cost-effective ways to add an AI accelerator to an existing fleet. Manufacturers build each NVIDIA data center GPU for continuous duty, and a properly tested refurbished or open-box L40S can lower acquisition cost significantly while delivering identical performance. The key is buying from a supplier that tests every card under load, verifies memory integrity, and backs the sale with a <a href=\"https:\/\/directmacro.com\/warranty\">warranty<\/a>.<\/p>\n<p>Refurbished cards make the most sense for inference clusters, development environments, proof-of-concept builds, capacity expansion in existing fleets, and teams that need an affordable GPU for deep learning. For regulated or mission-critical production, weigh the warranty terms carefully and confirm support coverage and the <a href=\"https:\/\/directmacro.com\/rma\">RMA process<\/a>.<\/p>\n<h2>What to Check Before You Buy?<\/h2>\n<p>Before you place an order, confirm the following points to avoid costly compatibility and supply issues.<\/p>\n<ol>\n<li><strong>Part Number and Model:<\/strong> Confirm the listing says PG133G and NVIDIA L40S, not the L40 or A40.<\/li>\n<li><strong>Server Compatibility:<\/strong> Check your PowerEdge model, riser, slot spacing, and BIOS against Dell&#8217;s GPU support matrix.<\/li>\n<li><strong>Power and Cables:<\/strong> Verify PSU capacity and that you have the correct 16-pin GPU power cable.<\/li>\n<li><strong>Cooling:<\/strong> Ensure the chassis provides the airflow a passive 350W card requires.<\/li>\n<li><strong>Software Stack:<\/strong> Confirm driver, CUDA, and framework versions, plus any vGPU or NVIDIA AI Enterprise licensing needs.<\/li>\n<li><strong>Condition and Warranty:<\/strong> Ask about testing, condition grade, <a href=\"https:\/\/directmacro.com\/return-policy\">return policy<\/a>, and warranty length.<\/li>\n<li><strong>Lead Time:<\/strong> Enterprise GPUs can face supply constraints, so plan around availability and <a href=\"https:\/\/directmacro.com\/bulk-quote\">request a bulk quote<\/a> for multi-card orders.<\/li>\n<\/ol>\n<h2>Pros and Cons<\/h2>\n<p>Here is a quick, balanced summary of our Dell PG133G review, showing where the card excels and where it falls short.<\/p>\n<p><strong>Pros<\/strong><\/p>\n<ul>\n<li><strong>Performance:<\/strong> Excellent FP8 inference throughput for transformer models.<\/li>\n<li><strong>Memory:<\/strong> 48GB ECC GDDR6 serves a wide range of model sizes.<\/li>\n<li><strong>Versatility:<\/strong> Combines AI, ray tracing, and AV1 media engines on one card.<\/li>\n<li><strong>Deployment:<\/strong> Air-cooled PCIe design fits standard GPU servers.<\/li>\n<\/ul>\n<p><strong>Cons<\/strong><\/p>\n<ul>\n<li><strong>Scaling:<\/strong> No NVLink or MIG limits multi-GPU training and hard partitioning.<\/li>\n<li><strong>Bandwidth:<\/strong> GDDR6 bandwidth trails HBM-based accelerators.<\/li>\n<li><strong>Precision:<\/strong> Minimal FP64 support rules out double-precision HPC.<\/li>\n<\/ul>\n<h2>What Does Direct Macro Recommend?<\/h2>\n<p>Based on this Dell PG133G review and our hands-on work sourcing and configuring enterprise GPUs for customers, Direct Macro recommends the Dell PG133G for organizations that want a high-performance, flexible AI accelerator without the cost and infrastructure demands of HBM-based platforms. It is our go-to suggestion for production LLM inference, RAG deployments, generative AI services, and mixed AI-plus-visualization clusters running on Dell PowerEdge servers.<\/p>\n<p>Our practical guidance, drawn from the same criteria we use in every enterprise GPU review: start with two to four cards per GPU-optimized server, provision at least 512GB of RAM for four-GPU nodes, use NVMe for model storage, and plan for 25GbE to 100GbE networking based on cluster size. You can view current pricing and availability on the Dell PG133G product page, or explore our full range of <a href=\"https:\/\/directmacro.com\/audio-video\/videos-graphics\/graphics-cards\/dell.html\">Dell graphics cards<\/a>. Our team can also help you validate compatibility before you order.<\/p>\n<h2>Final Thoughts: Is the Dell PG133G Worth It?<\/h2>\n<p>To wrap up this Dell PG133G review: the card is one of the most versatile AI accelerators available for enterprise data centers today. It delivers strong data center GPU performance for inference and fine-tuning, adds graphics and media capabilities that compute-only cards lack, and fits standard air-cooled servers. For most enterprise AI teams focused on deploying models rather than building them from scratch, the PG133G offers an outstanding balance of performance, memory, power, and cost. If you need a proven NVIDIA data center GPU for deep learning inference, this is the card to shortlist.<\/p>\n<p><strong>Ready to add the PG133G to your AI infrastructure?<\/strong> <a href=\"https:\/\/directmacro.com\/contact\">Contact our sales team<\/a> to confirm server compatibility before you buy. If you need help after delivery, reach our support specialists for setup and troubleshooting guidance.<\/p>\n<h2>Frequently Asked Questions<\/h2>\n<ol>\n<li><strong> What is the Dell PG133G?<\/strong><\/li>\n<\/ol>\n<p>The Dell PG133G is Dell&#8217;s part number for the NVIDIA L40S, a 48GB GDDR6, PCIe 4.0 x16, dual-slot, 350W data center GPU built on the Ada Lovelace architecture for AI, graphics, and media workloads.<\/p>\n<ol start=\"2\">\n<li><strong> Is the Dell PG133G good for deep learning?<\/strong><\/li>\n<\/ol>\n<p>Yes, for inference, fine-tuning, and small-to-medium training. As a GPU for deep learning, it offers strong FP8 and FP16 Tensor performance, but its lack of NVLink limits large-scale multi-GPU training.<\/p>\n<ol start=\"3\">\n<li><strong> How much memory does the Dell PG133G have?<\/strong><\/li>\n<\/ol>\n<p>It has 48GB of GDDR6 memory with ECC on a 384-bit interface, delivering 864 GB\/s of memory bandwidth.<\/p>\n<ol start=\"4\">\n<li><strong> Does the L40S support NVLink or MIG?<\/strong><\/li>\n<\/ol>\n<p>No. The L40S does not support NVLink or Multi-Instance GPU. Multi-GPU communication runs over PCIe, and virtualization uses NVIDIA vGPU software.<\/p>\n<ol start=\"5\">\n<li><strong> What is the difference between the L40 and L40S?<\/strong><\/li>\n<\/ol>\n<p>Both use the same Ada Lovelace GPU and 48GB of memory, but the L40S enables the Transformer Engine and roughly doubles FP8 and FP16 Tensor throughput, with a higher 350W power limit.<\/p>\n<ol start=\"6\">\n<li><strong> Which Dell servers support the PG133G?<\/strong><\/li>\n<\/ol>\n<p>GPU-optimized PowerEdge servers such as the R760xa and R750xa support up to four double-wide GPUs. Always confirm your exact model and riser configuration in Dell&#8217;s GPU support matrix.<\/p>\n<ol start=\"7\">\n<li><strong> What power connector does the Dell PG133G use?<\/strong><\/li>\n<\/ol>\n<p>It uses a single 16-pin power connector and draws up to 350W, so you need the correct Dell GPU power cable for your server.<\/p>\n<\/div><\/div><\/div><\/div><\/div>\n","protected":false},"excerpt":{"rendered":"<p>The Dell PG133G is a Dell-branded NVIDIA L40S AI accelerator that handles LLM inference, fine-tuning, generative AI, 3D rendering, and video transcoding on one card.<\/p>\n","protected":false},"author":19,"featured_media":9493,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[774],"tags":[851,852,848,849,297,850],"class_list":["post-9492","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-reviews","tag-48gb-gddr6","tag-data-center-gpu","tag-dell-pg133g","tag-dell-poweredge-gpu","tag-nvidia-gpu","tag-nvidia-l40s"],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v21.8 (Yoast SEO v28.0) - https:\/\/yoast.com\/product\/yoast-seo-premium-wordpress\/ -->\n<title>Dell PG133G Review: L40S 48GB Specifications, and Performance<\/title>\n<meta name=\"description\" content=\"Dell PG133G Review: Explore NVIDIA L40S 48GB specs, AI performance, rendering, and enterprise server compatibility\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" 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