Best AI Models for NVIDIA Quadro RTX 8000 (48 GB)

Runs 27–31B and 35B MoE models well; 70B barely fits and is slow (11 tok/s).

48 GB GDDR6672 GB/s65 TFLOPS FP16260 WTuring 2018

Cheapest single-card 48 GB with Tensor Cores, but Turing-era — no FP8/BF16 acceleration and slower than Ampere+ workstation cards.

What LLMs can the Quadro RTX 8000 run?

#	Model	Speed	Quant · VRAM
1	Qwen3.8 27BAlibabaHow to run → Q4_K_M · 17.9 GB · 25.1 t/s	25.1 tok/s	Q4_K_M · 17.9 GB
2	Qwen3.6 27BAlibaba Q4_K_M · 17.9 GB · 25.1 t/s	25.1 tok/s	Q4_K_M · 17.9 GB
3	Gemma 4 31B ITGoogle Q4_K_M · 21.7 GB · 21.4 t/s	21.4 tok/s	Q4_K_M · 21.7 GB
4	Gemma 4 26B A4B ITGoogle Q4_K_M · 16.5 GB · 126 t/s	126 tok/s	Q4_K_M · 16.5 GB
5	NVIDIA Nemotron 3.5 Lightning 30B A3B BF16NVIDIA Q4_K_M · 19.3 GB · 162 t/s	162 tok/s	Q4_K_M · 19.3 GB

#	Model	Speed	Quant · VRAM
1	Qwen3.8 27BAlibabaHow to run → Q4_K_M · 17.9 GB · 25.1 t/s	25.1 tok/s	Q4_K_M · 17.9 GB
2	Gemma 4 31B ITGoogle Q4_K_M · 21.7 GB · 21.4 t/s	21.4 tok/s	Q4_K_M · 21.7 GB
3	Qwen3.6 35B A3BAlibaba Q4_K_M · 22.0 GB · 113 t/s	113 tok/s	Q4_K_M · 22.0 GB
4	Gemma 4 26B A4B ITGoogle Q4_K_M · 16.5 GB · 126 t/s	126 tok/s	Q4_K_M · 16.5 GB
5	NVIDIA Nemotron 3.5 Lightning 30B A3B BF16NVIDIA Q4_K_M · 19.3 GB · 162 t/s	162 tok/s	Q4_K_M · 19.3 GB

#	Model	Speed	Quant · VRAM
1	Qwen3.8 27BAlibabaHow to run → Q4_K_M · 17.9 GB · 25.1 t/s	25.1 tok/s	Q4_K_M · 17.9 GB
2	Gemma 4 31B ITGoogle Q4_K_M · 21.7 GB · 21.4 t/s	21.4 tok/s	Q4_K_M · 21.7 GB
3	Qwen3.5 27BAlibaba Q4_K_M · 17.9 GB · 25.1 t/s	25.1 tok/s	Q4_K_M · 17.9 GB
4	Gemma 4 26B A4B ITGoogle Q4_K_M · 16.5 GB · 126 t/s	126 tok/s	Q4_K_M · 16.5 GB
5	NVIDIA Nemotron 3.5 Lightning 30B A3B BF16NVIDIA Q4_K_M · 19.3 GB · 162 t/s	162 tok/s	Q4_K_M · 19.3 GB

#	Model	Speed	Math Score	Quant · VRAM
1	Qwen3.8 27BAlibabaHow to run → Q4_K_M · 17.9 GB · 25.1 t/s	25.1 tok/s		Q4_K_M · 17.9 GB
2	Qwen3.6 27BAlibaba Q4_K_M · 17.9 GB · 25.1 t/s	25.1 tok/s		Q4_K_M · 17.9 GB
3	Qwen3.6 35B A3BAlibaba Q4_K_M · 22.0 GB · 113 t/s	113 tok/s		Q4_K_M · 22.0 GB
4	Gemma 4 31B ITGoogle Q4_K_M · 21.7 GB · 21.4 t/s	21.4 tok/s	overall	Q4_K_M · 21.7 GB
5	NVIDIA Nemotron 3.5 Lightning 30B A3B BF16NVIDIA Q4_K_M · 19.3 GB · 162 t/s	162 tok/s		Q4_K_M · 19.3 GB

#	Model	Speed ↓	Quant · VRAM
1	GPT OSS 20BOpenAIHow to run → Q4_K_M · 13.1 GB · 138 t/s	138 tok/s	Q4_K_M · 13.1 GB
2	NVIDIA Nemotron 3.5 Lightning 30B A3B BF16NVIDIA Q4_K_M · 19.3 GB · 162 t/s	162 tok/s	Q4_K_M · 19.3 GB
3	Qwen3 30B A3B Thinking 2507Alibaba Q4_K_M · 19.4 GB · 136 t/s	136 tok/s	Q4_K_M · 19.4 GB
4	Qwen3 30B A3B Instruct 2507Alibaba Q4_K_M · 19.4 GB · 136 t/s	136 tok/s	Q4_K_M · 19.4 GB
5	Gemma 4 26B A4B ITGoogle Q4_K_M · 16.5 GB · 126 t/s	126 tok/s	Q4_K_M · 16.5 GB

#	Model	Speed	Quant · VRAM
1	Qwen3.8 27BAlibabaHow to run → Q4_K_M · 17.9 GB · 25.1 t/s	25.1 tok/s	Q4_K_M · 17.9 GB
2	Qwen3.6 27BAlibaba Q4_K_M · 17.9 GB · 25.1 t/s	25.1 tok/s	Q4_K_M · 17.9 GB
3	Gemma 4 31B ITGoogle Q4_K_M · 21.7 GB · 21.4 t/s	21.4 tok/s	Q4_K_M · 21.7 GB
4	Gemma 4 26B A4B ITGoogle Q4_K_M · 16.5 GB · 126 t/s	126 tok/s	Q4_K_M · 16.5 GB
5	MiniCPM V 4.7 35B A3BOpenBMB Q4_K_M · 22.1 GB · 128 t/s	128 tok/s	Q4_K_M · 22.1 GB

See all 368 models on the Quadro RTX 8000 →Best quant that fits · speeds estimated on this card

About the Quadro RTX 8000

The Quadro RTX 8000 runs 27–31B and 35B MoE models well. Turing generation, released 2018.

GenerationTuringreleased 2018

Quadro RTX 8000 strengths and weaknesses

Strengths

Runs 35B MoE models: Qwen3.6 35B A3B at 113 tok/s in 22 GB
Room for long context: Qwen3.8 27B at Q4 uses 17.9 GB, leaving 30.1 GB free

Weaknesses

120B MoE models don't fit: GPT OSS 120B needs 70.8 GB at Q4 vs 48 GB here
Slow prompt reading: A 4K-token prompt on Qwen3.8 27B waits 7.7 s for the first token
No FP8 or NVFP4: Turing predates both; use GGUF, AWQ or GPTQ builds

Quadro RTX 8000 tokens per second by model size

Output speed depends on memory bandwidth (672 GB/s). Grey rows need more than 48 GB.

9BQwen3.5 9B · Q4_K_M · 6.6 GB
68.7tok/s
14BQwen3 14B · Q4_K_M · 11.9 GB
46.0tok/s
27–31BQwen3.8 27B · Q4_K_M · 17.9 GB
25.1tok/s
35B MoEQwen3.6 35B A3B · Q4_K_M · 22.0 GB
113tok/s
70BLlama 3.3 70B Instruct · IQ4_XS · 46.4 GB tight · IQ4_XS
10.5tok/s
120B MoEGPT OSS 120B · not at 4-bit
needs 70.8 GB

Estimated speeds: Q4_K_M, 4K context, one user. How we estimate

All 1216 models on the Quadro RTX 8000

Featured only

Best quant that fits, ordered by llmrun Score. Fit grades how well it runs here, S best. How ranking works

LLM models compatible with NVIDIA Quadro RTX 8000 — ranked by Score, recency and Fit
Model	Quant	VRAM	Speed	Context	Fit
Carbon 8B8.3BChat Q4_K_M·79.1 t/s	Q4_K_M	7.4 GB15%	79.1 t/s	33K	S87, Runs great
Supra Mini V6 1M1MChat Q4_K_M·1456.0 t/s	Q4_K_M	0.3 GB1%	1456.0 t/s	1K	S100, Runs great
UniScientist 30B A3B30.5BChat Q4_K_M·136.1 t/s	Q4_K_M	19.4 GB40%	136.1 t/s	131K	S96, Runs great
Qwen3 30B A3B Abliterated30.5BChat Q4_K_M·136.1 t/s	Q4_K_M	19.4 GB40%	136.1 t/s	41K	S96, Runs great
Dolphin 2.9.1 Yi 1.5 34B34.4BChat Q4_K_M·20.4 t/s	Q4_K_M	22.9 GB48%	20.4 t/s	8K	B60, Decent
GPT S 1.4M1MChat BF16·1409.0 t/s	BF16	0.3 GB1%	1409.0 t/s	0K	S100, Runs great
LFM2.5 1.2B Instruct Uncensored1.2BChat Q4_K_M·408.2 t/s	Q4_K_M	1.2 GB3%	408.2 t/s	128K	S100, Runs great
OvisOCR2853MVisionFunctions Q4_K_M·507.9 t/s	Q4_K_M	0.9 GB2%	507.9 t/s	262K	S100, Runs great
GaMS3 12B Instruct11.8BChat Q4_K_M·53.9 t/s	Q4_K_M	8.9 GB19%	53.9 t/s	131K	A81, Runs well
Josiefied Qwen3.5 0.8B Gabliterated V1853MChat BF16·212.0 t/s	BF16	2.1 GB4%	212.0 t/s	262K	S100, Runs great
Gemma4 12B Mtp Assistant12BChat Q4_K_M·55.2 t/s	Q4_K_M	10.8 GB22%	55.2 t/s	—	A82, Runs well
CyberPal2.0 20B20.9BChatReasoning Q4_K_M·137.9 t/s	Q4_K_M	13.1 GB27%	137.9 t/s	131K	S96, Runs great
Ouro 1.4B1.4BChatReasoning BF16·122.4 t/s	BF16	6.4 GB13%	122.4 t/s	66K	S95, Runs great
Malaysian TTS 0.6B V1629MChatFunctions Q4_K_M·546.0 t/s	Q4_K_M	1.6 GB3%	546.0 t/s	33K	S100, Runs great
Gemma 3 12B IT Heretic v212.2BChat Q4_K_M·52.2 t/s	Q4_K_M	9.2 GB19%	52.2 t/s	131K	A81, Runs well

Quadro RTX 8000 specs, power and PSU

Specs

Architecture: Turing
VRAM: 48 GB GDDR6
Memory bandwidth: 672.0 GB/s
FP16 (dense): 65.25 TFLOPS
CUDA cores: 4,608
Tensor cores: 576
Compute capability: 7.5
TDP: 260 W
Released: 2018-12-01
MSRP: $9,999

Quadro RTX 8000 FAQ

What is the largest model the Quadro RTX 8000 can run?

Qwen2.5 72B Instruct (72.7B) is the largest at 4-bit or better: at Q4_K_S it uses 46.6 GB of 48 GB, and runs at about 10.4 tok/s.

Can the Quadro RTX 8000 run Qwen3.8 27B?

Yes. Qwen3.8 27B fits at Q4_K_M, using 17.9 GB of 48 GB, and it is the best model that runs well on this card.

Can the Quadro RTX 8000 run 120B MoE models?

No. GPT OSS 120B needs 70.8 GB at Q4, more than the 48 GB on this card.

How many tokens per second does the Quadro RTX 8000 give?

About 25.1 tok/s on Qwen3.8 27B at Q4_K_M, with the first token after 7.8 s on a 4K-token prompt.

What power supply does the Quadro RTX 8000 need?

Plan a 550 W PSU (2× the 260 W TDP).

Is 48 GB enough for coding and agents?

Yes for coding: Qwen3.8 27B, the best of the 10 coding models that fit, runs at about 25 tok/s. For agents, not yet: no agent-scored model fits in less than 96 GB.

Best coding LLMs 48 GB VRAM GPUs with 48 GB or more