TL;DR: A used Quadro RTX 8000 is the cheapest single card that holds 48GB of CUDA VRAM — $1,975–$2,650 used in September 2026, and it runs Llama 3.3 70B Q4_K_M fully resident at ~10.4 tok/s in llama.cpp. The catch is the 2018 Turing silicon: vLLM’s modern attention backends won’t run on it, and MoE models decode at a third of a used RTX 3090’s speed.

	Used Quadro RTX 8000	Used RTX A6000	2× used RTX 3090
Best for	Cheapest one-card 70B, llama.cpp-only workflows	One-card 70B with a current-gen software path	Best 48GB per dollar in raw speed
Price / Cost	$1,975–$2,650 (Sep 2026)	$2,600–$3,800	~$1,950–$2,700 for the pair
The catch	Turing: no FA2/vLLM path, slow MoE, oldest supported arch	+$700–$1,100 for the same VRAM	~700W, two 3-slot cards, config work

Honest take: Buy the RTX 8000 only if your entire plan is dense 70B chat through llama.cpp on one quiet blower card — it does that for less money than anything else. If you’ll ever want vLLM, fast MoE models, fine-tuning, or resale value, pay up for the used A6000 or build the 3090 pair.

Check your exact model + context combination against any card in this article with the VRAM calculator before spending anything.

The Quadro RTX 8000 launched in 2018 at $10,000 as NVIDIA’s flagship workstation card. Eight years later, enterprise fleets have dumped enough of them onto eBay that GPUDojo’s September 2026 tracking starts used pricing at $1,975 — which makes it, by a comfortable margin, the cheapest way to put 48GB of CUDA VRAM behind a single PCIe slot. That’s the same capacity as a used RTX A6000 for roughly $700–$1,100 less, and it’s why the card keeps surfacing in r/LocalLLaMA build threads as the budget answer to “what’s the cheapest single card that holds a 70B?”

This is the salvage-tier workstation entry in our used-GPU series — the RTX A6000, RTX 3090, and Tesla P40 verdicts cover the neighboring rungs. The RTX 8000’s question is different from the A6000’s, though. The A6000 asks whether one card is worth a premium over two. The RTX 8000 asks how much 2018 architecture you can live with in 2026.

What $1,975–$2,650 actually buys

The RTX 8000 is the full TU102 Turing die with a double-height memory loadout (VideoCardz spec sheet, IT Creations listing specs):

Spec	Quadro RTX 8000	Used RTX A6000	Used RTX 3090
Architecture	Turing (2018), sm_75	Ampere (2020), sm_86	Ampere (2020), sm_86
VRAM	48GB GDDR6 (ECC)	48GB GDDR6 (ECC)	24GB GDDR6X
Memory bandwidth	672 GB/s	768 GB/s	936 GB/s
CUDA cores	4,608	10,752	10,496
Board power	295W	300W	350W
Power input	8-pin + 6-pin PCIe	EPS 8-pin (CPU-style)	2–3× 8-pin PCIe
Size / cooling	2-slot blower	2-slot blower	3-slot open-air (most)
NVLink	Yes — 2 cards pool 96GB	Yes — 96GB	Yes (24+24)
Used price, Sep 2026	$1,975–$2,650	$2,600–$3,800	$972 sold avg / ~$1,343 ask

Pricing detail, because condition varies wildly on cards this old: GPUDojo lists from $1,975 as of September 2026; live eBay listings run $2,050 (a buyer-reported purchase), $2,138, $2,299 open-box, and $2,625 refurbished with a warranty. One active listing shows 73 units sold, so there’s real liquidity here — these were deployed in render farms and CAD workstations by the thousand. As with every card in this series: ask for nvidia-smi -q output before paying, and note that the passive server variant (no fan, 250W) sometimes lists cheaper — it needs server-grade forced airflow and will thermal-throttle in a normal tower.

Two things on that spec table deserve attention before any benchmark. First, the power story is genuinely pleasant: 295W through a standard 8-pin + 6-pin, so it runs on the PSU and cables you already own — no EPS-connector adapter hunt like the A6000’s, no 600W transient spikes like a 3090’s. Second, 4,608 CUDA cores is less than half of what the Ampere cards carry, and Turing’s tensor cores predate BF16 support entirely. Capacity is 2026-class; compute is not.

The one-card 70B: measured numbers

The reason anyone buys this card is a dense 70B fully resident. Llama 3.3 70B at Q4_K_M is 42.5GB of weights — into 48GB with room for an 8K KV cache, no offload, no second card.

Hardware Corner’s hands-on test (June 2025, single card) measured:

• Llama 3.3 70B Q4_K_M, llama.cpp: 10.4 tok/s generation, 188 tok/s prompt processing
• Llama 3.3 70B 5.0bpw, ExLlamaV2: 12.8 tok/s at short context — falling to 5.1 tok/s at 8K context
• Owners running NVLinked pairs report 12–14 tok/s on 70B Q5 quants with row-split, flash attention, and MMQ kernels tuned in koboldcpp

Loading it looks like this — the line to watch is the layer count:

$ ./llama-server -m Llama-3.3-70B-Instruct-Q4_K_M.gguf -ngl 99 -c 8192 -fa on
...
load_tensors: offloaded 81/81 layers to GPU
llama_kv_cache: CUDA0 KV buffer size = 2560.00 MiB

Context on those numbers. The same-suite A6000 measurement is 14.6 tok/s on the same model and quant — so the RTX 8000 gives up about 29% of 70B speed for the $700–$1,100 discount, tracking the bandwidth gap (672 vs 768 GB/s). Dual RTX 3090s running the same 70B with llama.cpp’s default layer split land at 7–10 tok/s in our 48GB tier guide — layer split serializes execution across the pair, so the single slow card still beats two fast ones on this one workload. At 10.4 tok/s the RTX 8000 sits just above comfortable reading speed (~7–10 tok/s). It’s usable 70B chat. It is not agentic-workflow speed.

The Turing tax: where 2018 silicon actually hurts

Here’s the section that separates this card from the A6000, and it’s the part most for-sale listings won’t tell you.

MoE models — the ones that made local AI fast in 2026 — decode slowly on Turing. Hardware Corner’s same test run measured Qwen3 30B-A3B (Q4_K_M, llama.cpp) at 34.2 tok/s at 8K context, collapsing to 11.2 tok/s at 32K. A used RTX 3090 runs the same class of model at 107 tok/s. A 3B-active MoE should fly on any modern card — on Turing it merely walks, because the sparse-expert kernels and attention paths that made 2026’s MoE models fast are tuned for Ampere and newer. If your daily drivers are Qwen3.6-35B-A3B or Gemma 4’s MoE variants, a $972 3090 is roughly 3× faster than this $2,000 card.

vLLM is effectively off the table. FlashAttention-2 requires Ampere (sm_80) or newer, FlashInfer doesn’t support Turing, and the Triton fallback kernels need 96KB of shared memory per SM where Turing’s hardware ceiling is 64KB — which is why Gemma 4 currently cannot run on Turing in vLLM through any attention backend. If you were planning a multi-user endpoint, that plan needs an Ampere card.

Quant ecosystems are moving past it. Hardware Corner couldn’t run ExLlamaV2’s newer 3-bit “V3” quantizations at all — those kernels require Ampere or newer. GGUF via llama.cpp remains fully supported (llama.cpp’s own flash-attention implementation does run on Turing), and that’s the honest framing: buy this card and you are buying a llama.cpp/koboldcpp appliance. That covers Ollama and LM Studio too, since both ride llama.cpp — but the performance-frontier paths (vLLM, ExLlama V3, NVFP4, FP8) are all Ampere-or-newer.

No BF16 means fine-tuning is a fight. Turing tensor cores accelerate FP16, not BF16 — and current QLoRA recipes default to BF16 compute. Forcing FP16 works for some configs and overflows on others. If training is any part of the plan, this is the wrong card; see our QLoRA cost guide for what actually earns its keep there.

The support clock is ticking, but hasn’t struck. NVIDIA ended mainstream driver support for Maxwell, Pascal, and Volta in late 2025 — Turing is now the oldest architecture still on the current driver branch, and Quadro-line Turing continues past the 580-series cutoff with CUDA 13 still targeting sm_75. Realistically you’re buying a card with a software horizon measured in a couple of years, not five. Price that in: the A6000’s Ampere architecture shares a generation with the 3090s that dominate the used market, and will be the last thing dropped.

NVLink: 96GB for under $4,500, with an asterisk

Like the A6000, the RTX 8000 supports 2-way NVLink: two cards plus a Quadro RTX NVLink bridge pool 96GB. Two used RTX 8000s plus a bridge lands around $4,200–$5,500 — the cheapest 96GB of CUDA VRAM in existence, at less than a third of a single RTX PRO 6000’s $16,000.

The asterisk is everything in the previous section, doubled. A 96GB Turing pair holds a 120B-class MoE that it then decodes at Turing MoE speed, through llama.cpp only. As a capacity play for one patient user it’s genuinely interesting; as a performance play it isn’t one.

What to actually buy

Prices as of September 2026, all taken from the comparison above:

Your situation	The machine	Price	Where
Dense 70B chat on one quiet card, llama.cpp is enough, lowest cost	Used Quadro RTX 8000 48GB	$1,975–$2,650	Check price
One-card 70B with vLLM, fine-tuning, and years of software runway	Used RTX A6000 48GB	$2,600–$3,800	Check price
Fastest 48GB per dollar, comfortable with a two-card build	2× used RTX 3090	~$1,950–$2,700	Check price
Everything you actually run fits in 24GB	One used RTX 3090	~$972–$1,343	Check price
Occasional 70B jobs — don’t buy anything yet	Rented A6000/A40, ~$0.21–$0.49/hr	pay per hour	RunPod

If you’re undecided between tiers, the GPU buyer’s guide walks the whole budget ladder.

Verdict: buy or skip?

Buy the used RTX 8000 if:

• You want the absolute cheapest single card that runs a dense 70B fully resident, and ~10 tok/s reading-speed chat is the goal
• Your stack is llama.cpp, Ollama, LM Studio, or koboldcpp — and will stay that way
• The 2-slot blower and standard PCIe power matter: it drops into an office workstation a 3090 physically won’t fit

Skip it if:

• MoE models are your daily drivers — a $972 used RTX 3090 is ~3× faster on them and half the price
• You want vLLM, batch serving, fine-tuning, or anything on the FP8/NVFP4 frontier — Turing is locked out of all of it
• You’re buying “for the future”: this is the oldest architecture still supported, and the discount vs an A6000 is rent paid on borrowed software time

The pattern across our whole used-GPU series holds here: capacity ages well, architecture doesn’t. The RTX 8000’s 48GB is as useful as it was at $10,000. Everything wrapped around that memory is what you’re getting the 80% discount on.

FAQ

Is the Quadro RTX 8000 good for local LLMs in 2026? For one specific job, yes: dense 70B models fully resident on a single card via llama.cpp, at ~10.4 tok/s measured. For MoE models, vLLM serving, or fine-tuning it’s a poor buy — those paths are slow or unsupported on Turing.

How much does a used Quadro RTX 8000 cost in September 2026? $1,975–$2,650 depending on condition, per GPUDojo tracking and live eBay listings — roughly $700–$1,100 less than a used RTX A6000 with the same 48GB.

Quadro RTX 8000 vs RTX A6000 — which should I buy? The A6000 is 29% faster on 70B decode (14.6 vs 10.4 tok/s), supports vLLM/FlashAttention-2/BF16, and has a longer support horizon. The RTX 8000 is only the better buy if the price gap funds something else you need and llama.cpp covers your whole workflow.

Can two RTX 8000s run 96GB models? Yes — 2-way NVLink pools 96GB, and llama.cpp splits across the pair regardless. It’s the cheapest 96GB of CUDA VRAM available (~$4,200–$5,500 with bridge), but expect Turing-speed MoE decode and a llama.cpp-only software menu.

Does the RTX 8000 work with Ollama and LM Studio? Yes. Both run on llama.cpp, which fully supports Turing including its own flash-attention implementation. It’s vLLM, FlashInfer, and ExLlamaV2’s V3 quants that don’t.

Recommended Gear

• NVIDIA Quadro RTX 8000 48GB — the cheapest one-card 48GB path
• NVIDIA RTX A6000 48GB — same VRAM, modern software runway
• Used RTX 3090 24GB — the value alternative, one or a pair
• Quadro RTX NVLink Bridge — the later 96GB upgrade

Sources

• I Tried the 48GB RTX 8000 for Local LLMs — Hardware Corner
• Quadro RTX 8000 48GB Used Price, September 2026 — GPUDojo
• NVIDIA Quadro RTX 8000 48GB listings — eBay
• NVIDIA Quadro RTX 8000 specifications — VideoCardz
• Quadro RTX 8000 datasheet — NVIDIA
• Quadro RTX 8000 power/connector specs (295W, 8-pin + 6-pin) — IT Creations
• Gemma 4 on Turing GPUs: all vLLM attention backends hit shared-memory limits — vLLM issue #38918
• GPU-Benchmarks-on-LLM-Inference (A6000 70B llama.cpp numbers) — XiongjieDai, GitHub
• Running 70B models locally: exact VRAM by quantization — InsiderLLM
• NVIDIA ends driver support for Maxwell, Pascal, Volta — Tom’s Hardware
• Turing/Quadro RTX series continues past 580-branch driver cutoff — TechPowerUp
• RTX 3090 used price & fair asking range — ResalePrices

Last updated September 10, 2026. Used-GPU prices move weekly; verify current listings before buying.