RTX PRO 6000 vs RTX 5090 for Local 70B LLMs: 96GB vs 32GB
Same 1,792 GB/s memory system, three times the VRAM. Which card you need depends on the 70B quant and context you run every day.
By Mike Perry, Founder & Editor-in-Chief · Published 2026-10-25 · Updated 2026-10-25 · 11 min read
RTX PRO 6000 vs RTX 5090 for 70B LLMs: 96GB runs Llama 3.3 70B Q4 at 32 tok/s on one card; the 32GB 5090 needs 3-bit or a second GPU. Fit, speed, prices.
Quick Answer
For local 70B inference the RTX PRO 6000 Blackwell is the single-card answer: its 96 GB of GDDR7 holds Llama 3.3 70B at Q4_K_M and generates 32.0 tok/s at 4K context per Hardware Corner. The 32 GB RTX 5090 only fits a 3-bit IQ3_XXS build, which ran at 52.8 tok/s per ComputingForGeeks; a 4-bit 70B needs two 5090s.
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The RTX PRO 6000 vs RTX 5090 decision for local 70B LLMs is not a speed question. Both cards use the same Blackwell memory system, 512-bit GDDR7 at 1,792 GB/s per NVIDIA's RTX PRO 6000 page and StorageReview's spec comparison, so when a model fits on both, they generate tokens at nearly the same rate. The decision is capacity: 96 GB against 32 GB decides which 70B quantization you can run, how much context you can keep, and whether you need a second card.
Key takeaways
VRAM is the whole story. 96 GB vs 32 GB, with identical 1,792 GB/s bandwidth, per NVIDIA and Hardware Corner.
Llama 3.3 70B Q4_K_M on one PRO 6000: 32.0 tok/s at 4K context, 16.6 tok/s at 128K, per Hardware Corner.
One RTX 5090: Llama 3.3 70B fits only at about 3 bits; IQ3_XXS ran at 52.8 tok/s per ComputingForGeeks.
Two RTX 5090s: a 4-bit Llama 3.3 70B ran at 26.85 tok/s in Ollama per DatabaseMart.
Prices have moved a lot in 2026. NVIDIA's marketplace listed the PRO 6000 at $16,000 (out of stock) in August per Let's Data Science, while used 5090s averaged $6,782 in October per ComputingForGeeks.
Which models fit on an RTX 5090?
Dense 70B+ models need about 42.5 GB for their Q4 weights; on the RTX 5090, the weights do not fit, so the remaining layers run on the CPU from system RAM and system-RAM bandwidth sets the speed. That is the Q4 figure. A 3-bit quant of the same model is about a fifth smaller and can fit where Q4 does not, so for the model and quant this article covers, its own figures decide. SpecPicks tracks 3 community runs of that size on this card, median 17.8 tok/s. RTX 5090 carries 32 GB of VRAM. The weights column is the range of real Q4_K_M
files on Hugging Face for the models in each size class (about 0.6 GB per billion parameters),
and the runtime plus a usable context window wants about 2 GB on top; every tokens-per-second figure is a
median over community-reported Q4 runs SpecPicks tracks for this card, with the run count and
the source beside it.
Showing the model sizes this article covers and the band either side. Every size from 3B to 70B+, for every card SpecPicks tracks, is in the local-LLM GPU table.
Model size
Weights at Q4
Fits in 32 GB?
Measured
Left for context
Source
30-35B (Qwen 3 32B, QwQ 32B)The step change. A 24 GB card holds this entirely in VRAM; below that it is CPU offload.
The rows that matter for LLM inference are the first two. Token generation on a dense model is bound by how fast the card can stream the weights out of VRAM, and both cards stream at the same rate. The PRO 6000's extra 2,304 CUDA cores (24,064 vs 21,760, per the sources above) help prompt processing, not much else. The 96 GB is what you are paying for.
Does a 70B model fit on the RTX PRO 6000 or the RTX 5090?
File sizes below are the GGUF weights published by bartowski for Llama 3.3 70B Instruct and Qwen2.5 72B Instruct. The KV cache and the CUDA context need room on top of the weights, so a file that is only a gigabyte or two under the card's capacity is marginal in practice. Throughput is listed only where a source measured it.
Two things stand out. First, the PRO 6000 is the only one of the three configurations that can hold an 8-bit 70B at all; Q8_0 at 74.98 GB overflows even a 64 GB pair of 5090s. Second, the one 70B number that favours the 5090, 52.8 tok/s, comes from a 3-bit quant about two-thirds the size of Q4_K_M (27.47 GB vs 42.52 GB, per bartowski). Smaller weights stream faster, so that figure is not a like-for-like win over the PRO 6000's 4-bit result. It buys speed with precision, and ComputingForGeeks notes the card hit its 575 W limit on that run.
The DatabaseMart rows use Ollama's default 4-bit builds (the exact variant is not named) on Ollama 0.13.5 for the PRO 6000 and 0.6.5 for the dual-5090 box, so treat the gap between them as indicative rather than exact.
How fast is each card when the model fits on both?
The cleanest comparison comes from models that fit both cards, run by the same publisher on the same tooling. Hardware Corner measured both GPUs with llama.cpp at 4K context:
Generation is a wash, with the 5090 slightly ahead on two of three rows, and prefill goes to the PRO 6000 on all three, consistent with its higher core count. StorageReview's UL Procyon run shows the same pattern from a different tool: 226.4 tok/s on Llama 3 for the PRO 6000 against 214.3 tok/s for the 5090 (StorageReview). If your models are 32B or smaller, the 5090 delivers essentially PRO 6000 speed.
What happens to 70B speed at long context?
This is where 96 GB pays off a second time. Prefill (reading the prompt) dominates RAG and long-document work; generation dominates chat. Hardware Corner's Llama 3.3 70B Q4_K sweep on the PRO 6000 shows both falling as context grows:
No source in this synthesis reports a single RTX 5090 running a 70B at long context, and the arithmetic explains why: a 27.47 GB IQ3_XXS file leaves only a few gigabytes of a 32 GB card for the KV cache. For a 70B with 32K or more of context on one card, the PRO 6000 is the only option of the two.
Is one RTX PRO 6000 better than two RTX 5090s?
Two 5090s give 64 GB, enough for a 4-bit 70B, and the pairing ran Llama 3.3 70B at 26.85 tok/s and Qwen2.5 72B at 24.15 tok/s in Ollama (DatabaseMart). The same publisher's PRO 6000 run produced 31.96 and 29.15 tok/s on the same two models (DatabaseMart). Ollama splits layers across cards, so each token still waits on both GPUs in turn; tensor-parallel runtimes such as vLLM narrow that gap, as ComputingForGeeks found on a dual-3090 rig where tensor split lifted Llama 3.3 70B Q4_K_M from 19.5 to 31.9 tok/s (ComputingForGeeks).
The pair's real costs are power and plumbing. Two 575 W boards add up to 1,150 W of GPU power by NVIDIA's own rating (NVIDIA), which pushes the build into 1,600 W PSU territory, such as the Seasonic Prime TX-1600. The PRO 6000 is one 600 W card, and llm-tracker's power-limit sweep found its generation speed on a 7B model essentially unchanged from 600 W down to 400 W (278.95 vs 279.04 tok/s) (llm-tracker), so it can be capped hard in a quiet workstation.
What do the used and eBay markets look like?
Both cards traded far above launch price in 2026, which changes the math from the launch-day "$8,565 vs $1,999" framing.
RTX PRO 6000 Blackwell: NVIDIA's own marketplace went from $13,250 in June 2026 (Tom's Hardware) to $16,000 and out of stock in August (Let's Data Science). With first-party supply thin, eBay is the practical channel: check current RTX PRO 6000 Blackwell listings on eBay, and confirm whether a listing is the 600 W Workstation Edition or the blower-style Max-Q (sold by PNY, Amazon listing) before you bid.
At those cited figures, two used 5090s (2 × $6,782 = $13,564) cost less than one PRO 6000 at NVIDIA's August listing, but not by a margin that covers the second PSU, the bigger case and the lost Q8 option. For used purchases, ask for the original invoice: workstation cards carry vendor warranties that may not transfer, and a GPU that ran a mining or inference farm will have spent its life at the power limit.
Who should buy the RTX PRO 6000, and who should buy the RTX 5090?
Get the RTX PRO 6000 Blackwell if:
You want a 70B at Q6 or Q8, or a 4-bit 70B with 32K+ context, on one card.
You also want room above 70B. llm-tracker lists Qwen3 235B (MoE) at Q4_K_M at 18.5 tok/s on a single card (llm-tracker).
Power, noise or slot count matter, and one 600 W card is easier to live with than two 575 W cards.
Get one RTX 5090 if:
Your real workload is 32B-class models, where it matches the PRO 6000 on generation speed (61.4 vs 54.9 tok/s on Qwen3 32B per Hardware Corner).
You accept a 3-bit 70B with short context as a stretch goal, not the daily driver.
You also game, since it is a consumer card with GeForce drivers.
Get two RTX 5090s if: a 4-bit 70B at mid-20s tok/s is enough, you already own a 1,600 W platform with the slots, and you can source the cards near the used average.
Get neither if: a 4-bit 70B is your ceiling and budget matters most. A dual-3090 rig ran Llama 3.3 70B Q4_K_M at 31.9 tok/s with tensor split (ComputingForGeeks), comparable to the PRO 6000's 4-bit results.
Bottom line
For local 70B inference the RTX PRO 6000 Blackwell is the cleaner machine: one card, 96 GB, Llama 3.3 70B at 4-bit around 32 tok/s and an 8-bit option nothing else here can hold. The RTX 5090 is the same speed with a third of the memory, a superb 32B card that only reaches 70B through 3-bit quants or a second GPU. Pick by the largest model and context you need to run every day, not by headline tok/s.
As an Amazon Associate, SpecPicks earns from qualifying purchases; we also earn on qualifying eBay purchases via the eBay Partner Network. Prices shown were last tracked at crawl time and may vary — check the listing for the current price.
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Frequently asked questions
Can an RTX 5090 run Llama 3.3 70B?
Only at about 3 bits per weight. Llama 3.3 70B Q4_K_M is 42.52 GB per bartowski's GGUF listing, larger than the card's 32 GB, while the 27.47 GB IQ3_XXS build fits and ran at 52.8 tok/s per ComputingForGeeks (October 2026). It leaves little room for long context; a 4-bit 70B needs CPU offload or a second 5090.
How fast is the RTX PRO 6000 Blackwell on a 70B model?
Hardware Corner measured Llama 3.3 70B Q4_K at 32.0 tok/s generation and 1,689 tok/s prefill at 4K context with llama.cpp, falling to 16.6 tok/s at 128K context. llm-tracker reports 34.05 tok/s (tg128) on a Llama 3.3 70B Q4_K_M, and StorageReview measured 31.74 tok/s in LM Studio.
Is the RTX PRO 6000 faster than the RTX 5090 for LLMs?
Not when a model fits on both. Both cards have 1,792 GB/s of memory bandwidth, and Hardware Corner's same-tooling runs show near parity: Qwen3 32B Q4_K generated 54.9 tok/s on the PRO 6000 and 61.4 tok/s on the 5090. The PRO 6000 is ahead on prompt processing and, above all, on capacity: 96 GB vs 32 GB.
Is one RTX PRO 6000 better than two RTX 5090s for 70B?
For most single-user 70B work, yes. DatabaseMart's Ollama runs put Llama 3.3 70B at 31.96 tok/s on one PRO 6000 versus 26.85 tok/s on two 5090s, and only the PRO 6000 can hold a Q8_0 70B (74.98 GB). Two 5090s draw up to 1,150 W of board power by NVIDIA's ratings, against 600 W for the PRO 6000.
How much does the RTX PRO 6000 Blackwell cost in 2026?
Prices rose sharply through 2026. The card launched around $8,565 in March 2025 (PCMag, via Let's Data Science); NVIDIA's marketplace listed it at $13,250 in June 2026 per Tom's Hardware and at $16,000, out of stock, in August 2026 per Let's Data Science. eBay is often the practical channel while first-party stock is thin.
Can a 96GB RTX PRO 6000 run Llama 3.3 70B at 8-bit?
Yes. The Q8_0 GGUF of Llama 3.3 70B is 74.98 GB per bartowski, which leaves about 21 GB of the 96 GB card for the KV cache and runtime. Neither one RTX 5090 (32 GB) nor two (64 GB combined) can hold it. No source cited here reports an 8-bit 70B tok/s figure for the card.
