RTX 5090 vs RTX PRO 6000 for Local LLMs: 32GB vs 96GB

Overview

RTX 5090 vs RTX PRO 6000 for local LLMs comes down to memory: the RTX PRO 6000 has 96 GB of GDDR7 and the RTX 5090 has 32 GB, so buy the PRO 6000 only if your target model needs more than 32 GB on one card, and the 5090 if everything you run fits in 32 GB. Both numbers come from NVIDIA’s own pages, linked below. Speed in tokens per second is unknown on this page, because neither NVIDIA page publishes an LLM figure, and prices are unknown because neither page lists one: check current retailer price. The fit math follows methodology; the 1 to 2 GB overhead is an assumption, not a measurement.

How do the RTX 5090 and RTX PRO 6000 compare on paper?

Rows are what each NVIDIA page states; “not listed” means the page does not say, so the value is unknown here.

The bandwidth gap cannot be filled in: NVIDIA lists 1792 GB/sec only for the PRO 6000, so whether the cards differ in memory speed is unknown here. The PRO 6000 page also describes MIG with up to four isolated instances, but it attributes that to the RTX PRO 6000 Max-Q variant, so whether it applies to the card in the table is unknown here.

Is the RTX PRO 6000 worth it over an RTX 5090 for LLMs?

It is worth it when a model you want does not fit in 32 GB, and not otherwise. Capacity is the only measurable difference this page can source: 96 - 32 = 64 GB more memory. If your models already fit in 32 GB (see rtx-5090-llm), the extra memory buys no fit and no speed that either NVIDIA page documents. Price per GB is price divided by capacity, and with both prices unknown the cost-per-GB question has no number here; divide the current retailer price by 32 and by 96 yourself.

What fits in 96 GB vs 32 GB of VRAM?

Formulas: weights_GB = params x bits / 8 / 1e9; KV bytes = 2 x layers x kv_heads x head_dim x context x bytes_per_value; plus a labelled 1 to 2 GB overhead assumption. Bits per weight are from the llama.cpp quantize README: Q4_K_M 4.8944, Q8_0 8.5008. Qwen2.5-72B has 72.7B parameters on its model card, and its config.json gives 80 layers, 8 KV heads, hidden size 8192 over 64 heads (head dimension 128) and bfloat16, so KV is 2 x 80 x 8 x 128 x 2 = 327,680 bytes per token.

All values are decimal GB. Q4_K_M: 72.7e9 x 4.8944 / 8 / 1e9 = 44.4779. Q8_0: 72.7e9 x 8.5008 / 8 / 1e9 = 77.2510. The 96 GB card holds a 72B model at Q8_0 with 32,768 tokens of context, with 96 - 89.99 = 6.01 GB or more to spare, and does not hold the same model at Q8_0 with the full 131,072-token context. The 32 GB card holds none of these rows. Llama 3.1 70B Q4_K_M is 43.1 GB in the README table, which also exceeds 32 GB; the full walk-through is on vram-for-70b-model.

Can either card run gpt-oss-120b?

Only the PRO 6000 holds it whole. The gpt-oss-120b card lists 117B parameters with 5.1B active and says the model runs on “a single 80GB GPU”. The repo’s safetensors files total 65,248,893,184 bytes, which is 65.2489 GB, as worked on gpt-oss-vram-requirements. On 96 GB that leaves 96 - 65.2489 = 30.7511 GB for cache and overhead; on 32 GB the weights are over by 65.2489 - 32 = 33.2489 GB, so the 5090 needs expert offload to system RAM (moe-cpu-offload). Speed of either route is unknown here.

1x RTX PRO 6000 or 3x RTX 5090?

Three 5090s add up to the same capacity on paper, 3 x 32 = 96 GB, but it is split across three cards. Facts from the NVIDIA pages: the 5090 page says NVLink is not supported, and its power figure is 575 W per card, so three cards are 3 x 575 = 1725 W of graphics power against 600 W for one PRO 6000. Fitting the weights across cards is not the same as one pool: how a runtime splits layers, what it costs in PCIe traffic, and how three cards pack into a case are not covered by any source read for this page, so the speed difference is unknown. Slots, PSU and lane planning are on multi-gpu-pcie-and-psu and multi-gpu-llm. The honest trade: one card is simpler and lower in power; three cards depend on prices that this page does not know.

What about the RTX PRO 5000 72GB?

NVIDIA’s RTX PRO 5000 page lists 48 GB and 72 GB GDDR7 configurations with ECC, 1,344 GB/sec bandwidth and 300 W max power, dual slot. Against the PRO 6000 that is 96 - 72 = 24 GB less memory. gpt-oss-120b at a short context still fits 72 GB: 65.2489 + 0.3020 KV (upper bound at 4,096 tokens, from gpt-oss-vram-requirements) + 1 to 2 = 66.55 to 67.55 GB, leaving 4.45 to 5.45 GB; at its 131,072-token maximum the total is 75.91 to 76.91 GB and does not fit. Qwen2.5-72B Q8_0 at 8,192 context, 80.94 to 81.94 GB, does not fit 72 GB. The 48 GB variant overlaps 48gb-vram-llm.

RTX 6000 Pro owners, do you regret it?

This page cannot answer that. The question is asked in community threads, but opinions are not a primary source and no owner survey is cited here. The sourced reasons you might regret it are the ones above: if your models fit in 32 GB you paid for capacity you do not use, and the PRO 6000 is a 600 W part whose price is unknown here. For the unified-memory alternative see dgx-spark-vs-strix-halo.

Questions

Is the RTX PRO 6000 better than the RTX 5090 for LLMs?

It holds more: 96 GB GDDR7 with ECC against 32 GB, per NVIDIA’s pages. That is the only advantage this page can source. Speed is unknown, since neither page publishes an LLM tokens-per-second figure.

How much VRAM does the RTX PRO 6000 have?

96 GB of GDDR7 with ECC, with 1792 GB/sec memory bandwidth and 600 W max power consumption, per NVIDIA’s RTX PRO 6000 page read on 2026-10-13.

Can the RTX PRO 6000 run a 70B model?

Yes. Qwen2.5-72B at Q8_0 is 77.2510 GB of weights, and with 32,768 tokens of KV cache (10.7374 GB) plus 1 to 2 GB assumed overhead the total is 88.99 to 89.99 GB, under 96 GB. A 32 GB RTX 5090 cannot hold it.

Is 1x RTX PRO 6000 better than 3x RTX 5090?

Both reach 96 GB of total memory on paper. One card is 600 W; three are 1725 W of graphics power and the 5090 does not support NVLink. Relative speed and price are unknown here.

What is the price of the RTX PRO 6000 vs the RTX 5090?

Unknown. Neither NVIDIA page lists a price. Check current retailer price, then divide by 96 and by 32 for cost per GB.

Does 96GB fit gpt-oss-120b?

Yes. Its weights are 65.2489 GB, leaving 30.7511 GB on a 96 GB card. A 72 GB RTX PRO 5000 fits it only at short context.

Sources read on 2026-10-13

• RTX 5090: 32 GB GDDR7, 512-bit, 21760 CUDA cores, 575 W, 1000 W system power, NVLink no, bandwidth and price not listed.
• RTX PRO 6000: 96 GB GDDR7 ECC, 1792 GB/sec, 600 W, dual slot, PCIe Gen 5, MIG up to four instances (named for the Max-Q variant); CUDA cores, interface and price not listed.
• RTX PRO 5000: 48 GB and 72 GB GDDR7 ECC, 1,344 GB/sec, 300 W, dual slot.
• Qwen2.5-72B model card and config.json: 72.7B parameters, 80 layers, 8 KV heads, hidden size 8192, 64 attention heads, bfloat16, 131,072 context.
• gpt-oss-120b model card: 117B parameters, 5.1B active, single 80GB GPU, MXFP4; file total and config as read on gpt-oss-vram-requirements.
• llama.cpp quantize README: Q4_K_M 4.8944 and Q8_0 8.5008 bits per weight; Llama 3.1 70B Q4_K_M 43.1 GB.

Related

• rtx-5090-llm covers the 32 GB card alone.
• 48gb-vram-llm covers the 48 GB class.
• multi-gpu-llm and multi-gpu-pcie-and-psu cover several cards.
• gpt-oss-vram-requirements and vram-for-70b-model size the two models used above.
• dgx-spark-vs-strix-halo covers unified-memory boxes.
• methodology is the citation and fit-math rule.