What LLMs can an RTX PRO 6000 Blackwell run?

96 GB VRAM 1,792 GB/s 600 W 2025 Workstation card

RTX PRO 6000 Blackwell (96 GB) addresses 96 GB at 1,792 GB/s. Of the 16 open models tracked on this site, it runs 16 at Q4_K_M with 8k of context. The largest is gpt-oss 120B, needing about 71.7 GB and generating an estimated 424 tokens per second — faster than you can read.

Ninety-six gigabytes and 5090-class bandwidth on one card.

What an RTX PRO 6000 Blackwell runs, model by model

Every model at Q4_K_M with 8k of context, the setting most people actually use. This card has 91.2 GB to spend once the 5% safety margin comes off its 96 GB, and it reads that memory at 1,792 GB/s. Those two numbers decide the last two columns: the first says what loads, the second says how fast it answers. Both are estimates from stated formulas, and the VRAM calculator shows the working.

On this card that means anything at or under 91.2 GB counts as fitting, and anything above 81.6 GB is marked tight: it loads, but there is little left for the conversation, and a desktop on the same 96 GB can push it over.

The model to actually run on it

gpt-oss 120B is the best use of this card: 71.7 GB of the 96 GB available, an estimated 424 tokens per second, faster than you can read, and 131,072 tokens of context still available. It is a mixture-of-experts model, so all 117B parameters sit in memory but only 5.1B are read per token — which is why it is quick for its size.

If quality matters more than parameter count, Llama 3.3 70B fits at Q8_0 in about 81 GB. Q8 is near-lossless; a smaller model at Q8 often beats a larger one squeezed into Q3.

How much context actually fits

Model size is the question people ask; context is the one that bites. The KV cache grows linearly with the conversation, so a model that loads comfortably can still run out of memory halfway through a long document. On 96 GB, the context you get is whatever the weights leave behind — which is why two cards that run the same model can feel completely different in use. These are the largest contexts this card holds, at Q4_K_M weights.

Quantising the KV cache to Q8 roughly doubles what you can hold — gpt-oss 120B on this card goes from 131,072 to 131,072 tokens — at a quality cost most people never notice. Capped at 128k tokens here; a model's own architecture may stop lower, and sliding-window models such as Gemma 3 use less KV than the formula assumes.

Where this card stops

Nothing in this list is out of reach. Every model tracked here fits at Q4_K_M with 8k of context, so the ceiling you will meet is context length and batch size, not parameter count.

The honest take

This is the first single card that runs a 70B model at Q8, or a 120B mixture-of-experts model at its native precision, without splitting anything. It pairs 96 GB with 1792 GB/s, the same bandwidth as a 5090, so unlike previous big-memory workstation cards it is not slow for its size. The workstation edition draws 600 W; a 300 W Max-Q variant trades clocks for a machine you can sit next to.

What it is good at

• 96 GB removes almost every capacity decision at the sizes open models currently ship in.
• 1792 GB/s means large models still generate at a comfortable pace, not a crawl.
• One card, one memory space: no tensor parallelism to configure, no split-brain debugging.

What it is not

• 600 W in the workstation edition, which dictates the power supply and the room.
• Priced as professional hardware; the money buys capacity, and the same capacity rents by the hour.
• Overkill for anything under 70B, where a 5090 does the same work at the same speed.

The thing people get wrong: It shares the 5090's 1792 GB/s. For any model that fits on both cards, they generate at the same speed — you are paying purely for the extra 64 GB of address space, not for a faster card.

Buy or rent

Buy only if you need 96 GB regularly and locally. For the occasional 70B run, renting the same class of card for a handful of hours a month is not close.

Workstation cards are sold on quotes rather than shelf prices, so there is no single number to publish. Put the figure you are actually looking at, with your electricity rate and your real monthly hours, into the build vs rent calculator — it already knows this card draws 600 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.

Nodegrove is the rented side of that comparison: a workspace that stays saved, with a GPU attached only while you are using it. It is the right answer when your hours are low or your needs change; buying is the right answer when they are high and stable.

Compared with the alternatives

• RTX 5090 — 32 GB · 1,792 GB/s · best fit Qwen3 32B at ~66 tok/s. The first consumer card whose memory bandwidth changes what is comfortable.
• RTX 6000 Ada — 48 GB · 960 GB/s · best fit Qwen3 32B at ~35 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.

All 13 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.

Questions

Can an RTX PRO 6000 Blackwell run a 70B model?

Yes. Llama 3.3 70B at Q4_K_M with 8k of context needs about 45.8 GB, inside the 96 GB this card can address, and it generates an estimated 31 tokens per second — comfortable for chat.

What is the best model to run on an RTX PRO 6000 Blackwell?

gpt-oss 120B. At Q4_K_M and 8k context it needs about 71.7 GB of the 96 GB available and generates an estimated 424 tokens per second, which is faster than you can read. If quality matters more than size, Llama 3.3 70B fits at Q8 in about 81 GB.

How many tokens per second does an RTX PRO 6000 Blackwell generate?

It depends entirely on the size of the model, because generating a token means reading every active weight out of memory. At 1,792 GB/s and 70% efficiency, this card produces an estimated 270 tokens per second on an 8B model at Q4, and about 424 on the largest model it holds, gpt-oss 120B. Reading speed is roughly 5 to 8 words per second, so anything above 25 feels immediate.

Is 96 GB enough for running LLMs locally?

It runs 16 of the 16 models tracked here at Q4_K_M with 8k of context, up to 117B parameters. The honest test is not the model list but the context: gpt-oss 120B on this card holds about 131,072 tokens before memory runs out.

RTX PRO 6000 Blackwell or RTX 5090 for local models?

This card holds more: 96 GB against 32 GB, so it runs 16 of these models to the RTX 5090's 13. On speed, they are identical, and bandwidth is what sets chat speed.

Can the RTX PRO 6000 run a 70B model at full precision?

Not at FP16, which would need roughly 140 GB for the weights alone. At Q8, which is near-lossless in practice, a 70B model fits with room for a long context — and that is the first time a single card has been able to do it.

RTX PRO 6000 or RTX 5090?

They read memory at the same speed, 1,792 GB/s, so for any model that fits both they generate at the same rate. The only thing the extra money buys is the 96 GB address space, which matters if you run 70B models or very long contexts and is wasted if you do not.

• Memory: weights (parameters × bytes per parameter) + KV cache (2 × layers × KV heads × head dim × context × bytes) + 0.5 GB runtime + 4% of weights. Full derivation in the VRAM calculator.
• Speed: 70% × bandwidth ÷ active weight bytes, single stream, no batching, prompt processing excluded. See the tokens-per-second estimator.
• Card specification: 96 GB, 1,792 GB/s, 600 W — manufacturer figures.
• Model architecture values come from each model's published config; the self-hosted LLM guide explains what each one changes.