L40S

Muse Glimmer 30B is the best use of this card: 18.7 GB of the 48 GB available, an estimated 35 tokens per second, comfortable for chat, and 131,072 tokens of context still available.

How this is chosen, since no benchmark is quoted: models are compared by size, a mixture-of-experts model counting at the geometric mean of its total and active parameters (a rule of thumb, not a measurement). The pick is the biggest class that fits with memory to spare and answers at 25 tokens per second or better, and within that class the most recent general-purpose release; coding and reasoning specialists are listed above but not recommended by default.

Qwen3.6 35B-A3B (MoE) is the largest model the card will hold, at 22.4 GB, with room for 131,072 tokens of context. It is a mixture-of-experts model reading only 3B parameters per token, so it is the faster of the two at an estimated 348 tokens per second; what it gives up is the depth of a dense model that reads all of its weights for every token.

It also fits at Q8_0, in about 33.5 GB, which is worth taking whenever the memory allows: Q8 is near-lossless where Q4 costs a little accuracy.

Quantising the KV cache to Q8 roughly doubles what you can hold, at a quality cost most people never notice: Granite 4.2 30B on this card goes from 104,448 to 131,072 tokens. Capped at 128k tokens, or at the model's own native window where that is smaller, marked "model limit": free memory beyond that point buys nothing. Rows that hold far more context than their size suggests are hybrid, sliding-window or latent-attention models, which cache only a fraction of what a standard transformer does; each model page shows the working.

The first model out of reach is Llama 3.3 70B: about 45.8 GB at Q4_K_M and 8k context, against 48 GB of usable memory. That is close enough to be worth a caveat: it is inside the raw 48 GB and only misses the 95% line this site uses for a fit. On a card with a display attached, treat it as no. On a headless card, with a shorter context, it runs. Dropping to Q3_K_M would need about 37.7 GB, which fits, though Q3 loses enough quality that a smaller model at Q4 is usually the better trade. Offloading the remainder to system RAM works and is ten to fifty times slower; it is a way to see a model run, not a way to use one.

There is nothing to decide here: you rent it. Use it as the reference when comparing a 48 GB rental against buying an RTX 6000 Ada.

There is no purchase price to reason about, only an hourly one, and it differs by provider and by week. Put the figure you are actually looking at, with your electricity rate and your real monthly hours, into the build vs rent calculator, which already knows this card draws 350 W under load. It returns the month owning becomes cheaper, or tells you it never does. Hours per month decides it far more often than the price does.

Rented, an L40S costs from $0.79 an hour (RunPod Community, third-party host), or $1.09 from a provider that runs the machine (RunPod), on demand, before tax, read 19 October 2026. Every provider's price is in the cloud GPU price guide.

• RTX 6000 Ada: 48 GB · 960 GB/s · best fit Muse Glimmer 30B at ~39 tok/s. Forty-eight gigabytes in a normal computer, at 300 watts, without the noise.
• A100: 80 GB · 2,039 GB/s · best fit Llama 3.3 70B at ~35 tok/s. The old datacenter workhorse, still fast where it counts, and cheap to rent.
• RTX PRO 6000 Blackwell: 96 GB · 1,792 GB/s · best fit Llama 3.3 70B at ~31 tok/s. Ninety-six gigabytes and 5090-class bandwidth on one card.

All 39 cards are compared side by side on the GPU index, and the same numbers from the model's point of view are on each model fit table. To check one specific pairing rather than read a table, use the can-I-run-it checker.