Best AI Models for NVIDIA RTX A6000 (48 GB)

Runs 27–31B and 35B MoE models well; 70B barely fits and is slow (12 tok/s).

48 GB GDDR6768 GB/s155 TFLOPS FP16300 WAmpere 2020

What LLMs can the RTX A6000 run?

#	Model	Speed	Quant · VRAM
1	Qwen3.8 27BAlibabaHow to run → Q4_K_M · 17.9 GB · 28.7 t/s	28.7 tok/s	Q4_K_M · 17.9 GB
2	Qwen3.6 27BAlibaba Q4_K_M · 17.9 GB · 28.7 t/s	28.7 tok/s	Q4_K_M · 17.9 GB
3	Gemma 4 31B ITGoogle Q4_K_M · 21.7 GB · 24.5 t/s	24.5 tok/s	Q4_K_M · 21.7 GB
4	Gemma 4 26B A4B ITGoogle Q4_K_M · 16.5 GB · 140 t/s	140 tok/s	Q4_K_M · 16.5 GB
5	NVIDIA Nemotron 3.5 Lightning 30B A3B BF16NVIDIA Q4_K_M · 19.3 GB · 174 t/s	174 tok/s	Q4_K_M · 19.3 GB

#	Model	Speed	Quant · VRAM
1	Qwen3.8 27BAlibabaHow to run → Q4_K_M · 17.9 GB · 28.7 t/s	28.7 tok/s	Q4_K_M · 17.9 GB
2	Gemma 4 31B ITGoogle Q4_K_M · 21.7 GB · 24.5 t/s	24.5 tok/s	Q4_K_M · 21.7 GB
3	Qwen3.6 35B A3BAlibaba Q4_K_M · 22.0 GB · 125 t/s	125 tok/s	Q4_K_M · 22.0 GB
4	Gemma 4 26B A4B ITGoogle Q4_K_M · 16.5 GB · 140 t/s	140 tok/s	Q4_K_M · 16.5 GB
5	NVIDIA Nemotron 3.5 Lightning 30B A3B BF16NVIDIA Q4_K_M · 19.3 GB · 174 t/s	174 tok/s	Q4_K_M · 19.3 GB

#	Model	Speed	Quant · VRAM
1	Qwen3.8 27BAlibabaHow to run → Q4_K_M · 17.9 GB · 28.7 t/s	28.7 tok/s	Q4_K_M · 17.9 GB
2	Gemma 4 31B ITGoogle Q4_K_M · 21.7 GB · 24.5 t/s	24.5 tok/s	Q4_K_M · 21.7 GB
3	Qwen3.5 27BAlibaba Q4_K_M · 17.9 GB · 28.7 t/s	28.7 tok/s	Q4_K_M · 17.9 GB
4	Gemma 4 26B A4B ITGoogle Q4_K_M · 16.5 GB · 140 t/s	140 tok/s	Q4_K_M · 16.5 GB
5	NVIDIA Nemotron 3.5 Lightning 30B A3B BF16NVIDIA Q4_K_M · 19.3 GB · 174 t/s	174 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 · 28.7 t/s	28.7 tok/s		Q4_K_M · 17.9 GB
2	Qwen3.6 27BAlibaba Q4_K_M · 17.9 GB · 28.7 t/s	28.7 tok/s		Q4_K_M · 17.9 GB
3	Qwen3.6 35B A3BAlibaba Q4_K_M · 22.0 GB · 125 t/s	125 tok/s		Q4_K_M · 22.0 GB
4	Gemma 4 31B ITGoogle Q4_K_M · 21.7 GB · 24.5 t/s	24.5 tok/s	overall	Q4_K_M · 21.7 GB
5	NVIDIA Nemotron 3.5 Lightning 30B A3B BF16NVIDIA Q4_K_M · 19.3 GB · 174 t/s	174 tok/s		Q4_K_M · 19.3 GB

#	Model	Speed ↓	Quant · VRAM
1	GPT OSS 20BOpenAIHow to run → Q4_K_M · 13.1 GB · 154 t/s	154 tok/s	Q4_K_M · 13.1 GB
2	NVIDIA Nemotron 3.5 Lightning 30B A3B BF16NVIDIA Q4_K_M · 19.3 GB · 174 t/s	174 tok/s	Q4_K_M · 19.3 GB
3	Qwen3.5 4BAlibaba Q4_K_M · 3.4 GB · 153 t/s	153 tok/s	Q4_K_M · 3.4 GB
4	Qwen3 30B A3B Thinking 2507Alibaba Q4_K_M · 19.4 GB · 148 t/s	148 tok/s	Q4_K_M · 19.4 GB
5	Gemma 4 26B A4B ITGoogle Q4_K_M · 16.5 GB · 140 t/s	140 tok/s	Q4_K_M · 16.5 GB

#	Model	Speed	Quant · VRAM
1	Qwen3.8 27BAlibabaHow to run → Q4_K_M · 17.9 GB · 28.7 t/s	28.7 tok/s	Q4_K_M · 17.9 GB
2	Qwen3.6 27BAlibaba Q4_K_M · 17.9 GB · 28.7 t/s	28.7 tok/s	Q4_K_M · 17.9 GB
3	Gemma 4 31B ITGoogle Q4_K_M · 21.7 GB · 24.5 t/s	24.5 tok/s	Q4_K_M · 21.7 GB
4	Gemma 4 26B A4B ITGoogle Q4_K_M · 16.5 GB · 140 t/s	140 tok/s	Q4_K_M · 16.5 GB
5	Muse Glimmer 30BMeta Q4_K_M · 19.6 GB · 27.2 t/s	27.2 tok/s	Q4_K_M · 19.6 GB

See all 370 models on the RTX A6000 →Best quant that fits · speeds estimated on this card

About the RTX A6000

The RTX A6000 runs 27–31B and 35B MoE models well. Ampere generation, released 2020.

GenerationAmperereleased 2020

RTX A6000 strengths and weaknesses

Strengths

Runs 35B MoE models: Qwen3.6 35B A3B at 125 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
No FP8 or NVFP4: Ampere predates both; use GGUF, AWQ or GPTQ builds

RTX A6000 tokens per second by model size

Output speed depends on memory bandwidth (768 GB/s). Grey rows need more than 48 GB.

9BQwen3.5 9B · Q4_K_M · 6.6 GB
78.5tok/s
14BQwen3 14B · Q4_K_M · 11.9 GB
52.5tok/s
27–31BQwen3.8 27B · Q4_K_M · 17.9 GB
28.7tok/s
35B MoEQwen3.6 35B A3B · Q4_K_M · 22.0 GB
125tok/s
70BLlama 3.3 70B Instruct · IQ4_XS · 46.4 GB tight · IQ4_XS
12.0tok/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 1232 models on the RTX A6000

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 RTX A6000 — ranked by Score, recency and Fit
Model	Quant	VRAM	Speed	Context	Fit
Qwen2 7B7.6BChat Q4_K_M·100.0 t/s	Q4_K_M	5.8 GB12%	100.0 t/s	131K	S91, Runs great
Qwen2 0.5B494MChat Q4_K_M·805.2 t/s	Q4_K_M	0.8 GB2%	805.2 t/s	131K	S100, Runs great
Midnight Miqu 70B V1.569.0BChat Q4_K_M·11.8 t/s	Q4_K_M	47.1 GB98%	11.8 t/s	33K	D23, Barely runs
Internlm 7B7BChat FP16·32.4 t/s	FP16	15.4 GB32%	32.4 t/s	2K	A72, Runs well
Qwen2 1.5B1.5BChat Q4_K_M·390.0 t/s	Q4_K_M	1.7 GB4%	390.0 t/s	131K	S100, Runs great
Qwen 14B Chat14.2BChat Q4_K_M·53.4 t/s	Q4_K_M	10.6 GB22%	53.4 t/s	8K	A81, Runs well
Qwen2.5 Math 7B7.6BChatMathFunctions Q4_K_M·100.0 t/s	Q4_K_M	5.1 GB11%	100.0 t/s	4K	S91, Runs great
Yi 34B Chat34.4BChat Q4_K_M·23.3 t/s	Q4_K_M	21.9 GB46%	23.3 t/s	4K	B64, Decent
Mistral 7B Instruct v0.17.2BChat Q4_K_M·101.7 t/s	Q4_K_M	6.8 GB14%	101.7 t/s	33K	S92, Runs great
Internlm2 Chat 7B7.7BChat Q4_K_M·95.8 t/s	Q4_K_M	7.1 GB15%	95.8 t/s	33K	S90, Runs great
Qwen2.5 14B14.8BChatFunctions Q4_K_M·52.2 t/s	Q4_K_M	12.4 GB26%	52.2 t/s	131K	A81, Runs well
Phi 3 Medium 4k Instruct14.0BChatCode Q4_K_M·54.9 t/s	Q4_K_M	9.5 GB20%	54.9 t/s	4K	A82, Runs well
Covenant 72B72.7BChat Q4_K_M·11.2 t/s	Q4_K_M	44.6 GB93%	11.2 t/s	2K	C30, Usable
Qwen 7B Chat7.7BChat Q4_K_M·97.9 t/s	Q4_K_M	7.6 GB16%	97.9 t/s	33K	S91, Runs great
C4ai Command A 03 2025111.1BChatFunctions IQ2_M·12.1 t/s	IQ2_M	46.6 GB97%	12.1 t/s	—	D24, Barely runs

Rent NVIDIA RTX A6000 in the cloud

Hourly rate on demand for one RTX A6000.

Vast.aiMarketplace
from$0.44/hr
Deploy

Buying one ($4,649) costs as much as ~10,500 hours of rental, about 44 months at 8 hours a day.

Cost per million tokens

Output-token cost on an RTX A6000 rented at $0.44/hr, one request at a time. Batch several users on the same card and the cost per token drops several-fold.

Model	Quant	Speed	Per 1M output
NVIDIA Nemotron 3.5 Lightning 30B A3B BF16	Q4_K_M	174 tok/s	$0.71$0.0083 input
Gemma 4 26B A4B IT	Q4_K_M	140 tok/s	$0.88$0.016 input
Qwen3.6 35B A3B	Q4_K_M	125 tok/s	$0.99$0.017 input
Qwen3.8 27B	Q4_K_M	29 tok/s	$4.29$0.098 input
Muse Glimmer 30B	Q4_K_M	27 tok/s	$4.52$0.11 input

RTX A6000 specs, power and PSU

Specs

Architecture: Ampere
VRAM: 48 GB GDDR6
Memory bandwidth: 768.0 GB/s
FP16 (dense): 154.80 TFLOPS
CUDA cores: 10,752
Tensor cores: 336
Compute capability: 8.6
TDP: 300 W
Released: 2020-10-05
MSRP: $4,649

RTX A6000 FAQ

What is the largest model the RTX A6000 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 11.9 tok/s.

Can the RTX A6000 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 RTX A6000 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 RTX A6000 give?

About 28.7 tok/s on Qwen3.8 27B at Q4_K_M, with the first token after 3.3 s on a 4K-token prompt.

What power supply does the RTX A6000 need?

Plan a 600 W PSU (2× the 300 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 29 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