RTX PRO 6000 vs L40S: is 96 GB worth the step up?

The RTX PRO 6000 vs L40S choice is mostly about memory. NVIDIA's RTX PRO 6000 Blackwell Server Edition has 96 GB of GDDR7 at about 1.6 TB/s. The L40S, an Ada Lovelace card, has 48 GB of GDDR6 at 864 GB/s. Both are PCIe cards with RT cores, built for a mix of rendering, generative media and AI inference. We rent the RTX PRO 6000 at $1,390 a month and the L40S at $890.

RTX PRO 6000 vs L40S: the short answer

Choose the RTX PRO 6000 when the work does not fit in 48 GB: heavy render scenes, video generation, 70B-class language models or large batches. It also brings Blackwell's FP4 support and MIG. Choose the L40S when your jobs fit in 48 GB with headroom. It handles the same kinds of work for $500 a month less, and three L40S cards cost less than two RTX PRO 6000s when you need more parallel workers rather than more memory per card.

Memory: 96 GB of GDDR7 vs 48 GB of GDDR6

For language models, weights take roughly 2 bytes per parameter in BF16, 1 in FP8 and 0.5 in 4-bit formats, plus 10–30% for KV cache, activations and overhead. Image and video models follow the same arithmetic for their transformer, plus text encoders and the VAE.

Spare memory is not idle memory. It sets how many requests you can batch, how long a context you can serve and how many generations run side by side. Take a 70B model in 4-bit, with 80 layers and 8 KV heads as in Llama 3 70B, and an FP8 KV cache at about 0.16 MB per token. The L40S's 13 GB of headroom holds roughly 80,000 tokens of context across all requests; the RTX PRO 6000's 61 GB holds roughly 370,000, before overhead. In image pipelines, 96 GB lets you keep a base model, refiners, ControlNets and upscalers resident together instead of swapping them in and out between jobs.

Rendering and generative media

GPU renderers want the whole scene, geometry and textures, in GPU memory. When it does not fit, the renderer either fails or falls back to slower out-of-core modes that page data from system RAM. A 48 GB card handles a great deal of look-dev and shot work. Dense geometry, large texture sets and many 8K maps are where 96 GB pays off. Both cards have RT cores for hardware ray tracing; see rendering and VFX on dedicated GPUs for typical setups.

Video generation is where the gap is widest. Memory grows with resolution and clip length, because the model attends across the latent tokens of every frame. With 96 GB you can keep larger video models in BF16, generate longer clips and avoid CPU offloading, which slows every step. For still images at SDXL or Flux scale, 48 GB is usually enough, and the less expensive card is often the right one. Our generative media page covers both cases.

Inference: bandwidth, FP8 and FP4

When serving language models, memory bandwidth sets the pace of token generation, since every new token reads the weights and KV cache from memory. The RTX PRO 6000's 1,597 GB/s is about 1.85 times the L40S's 864 GB/s. On a model that fits both cards, that ratio is the most relevant hardware difference for decode-heavy serving.

On compute, NVIDIA lists the L40S at 733 TFLOPS of dense FP8, 1,466 with sparsity, and quotes 2 PFLOPS of FP8 with sparsity for the RTX PRO 6000: about 1.4 times as much on paper. The bigger difference is FP4. Blackwell supports FP4 in its tensor cores, and NVIDIA quotes 4 PFLOPS of FP4 with sparsity for the RTX PRO 6000. Ada's lowest-precision floating-point format is FP8, so on the L40S 4-bit weights reduce memory use but the math runs at higher precision.

NVIDIA also lists MIG for the RTX PRO 6000, with up to four isolated instances per card, which helps when one GPU serves several small models. We rent the whole card and never partition it ourselves; if you plan to use MIG, mention it in your request so we can confirm it for your configuration.

Scaling beyond one GPU

Neither card has NVLink. Multi-GPU work runs over PCIe, Gen5 on the RTX PRO 6000 and Gen4 on the L40S. That suits data-parallel work: render farms, batch generation and one model replica per GPU. It suits training one model split across many GPUs much less; our NVLink vs PCIe guide explains when the interconnect matters.

We rent the L40S as a single-GPU plan only. The RTX PRO 6000 also comes as an 8-GPU server: 768 GB of GPU memory, two EPYC 9555 CPUs with 128 cores in total, 1.5 TB of DDR5 and 2× 100 GbE, for $10,490 a month or about $1.80 per GPU-hour. Eight single RTX PRO 6000 plans would cost $11,120, so the server is the better deal once you need a full node.

Cost per month and value

The RTX PRO 6000 costs $1,390 a month, about $1.90 per GPU-hour; the L40S costs $890, about $1.22. Per gigabyte of GPU memory, the RTX PRO 6000 is the better value: about $14.48 a month against $18.54. Per card, the L40S is $500 a month less.

So the real question is whether you use the extra memory. If your jobs fit in 48 GB with room to spare, two L40S plans at $1,780 give you two independent workers for $390 more than one RTX PRO 6000. If they do not fit, the lower price buys you nothing: you end up splitting work or accepting slower out-of-core modes. Billing is monthly with no cancellation fee, so you can start on one card and switch at the end of a paid month. Compare totals with the GPU cost calculator.

How to decide

• Measure peak memory on your heaviest job with nvidia-smi. If it stays under about 40 GB, the L40S is enough.
• Above 40 GB, or planning bigger models and longer clips: choose the RTX PRO 6000.
• Need FP4 inference: choose the RTX PRO 6000, since Ada does not support it.
• Many small independent jobs: several L40S plans give you more cards for the money.

For language models, the VRAM calculator estimates memory needs before you order.