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	MiniCPM V 4.7 35B A3BOpenBMB Q4_K_M · 22.1 GB · 141 t/s	141 tok/s	Q4_K_M · 22.1 GB

See all 368 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 1216 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
Llm Jp 4.1 33B Thinking33.2BChatFunctions Q4_K_M·24.0 t/s	Q4_K_M	24.5 GB51%	24.0 t/s	66K	B64, Decent
Qwen3.6 35B A3b Crown Halo Mtp Dynamic35BChat Q4_K_M·151.4 t/s	Q4_K_M	27.0 GB56%	151.4 t/s	—	S97, Runs great
GPT OSS 20B RichardErkhov Heresy20.9BChatFunctions Q4_K_M·153.6 t/s	Q4_K_M	13.1 GB27%	153.6 t/s	131K	S97, Runs great
Nanbeige4.1 3B3.9BChat Q4_K_M·178.9 t/s	Q4_K_M	3.7 GB8%	178.9 t/s	262K	S99, Runs great
Gemma 4 E2B IT Uncensored5.1BChat Q4_K_M·145.5 t/s	Q4_K_M	3.5 GB7%	145.5 t/s	131K	S97, Runs great
Qwen3.5 122B A10B125.1BVisionFunctions IQ2_M·88.2 t/s	IQ2_M	42.7 GB89%	88.2 t/s	262K	A77, Runs well
MemT 1.0 Nano35.1BChatFunctions Q4_K_M·21.5 t/s	Q4_K_M	27.1 GB56%	21.5 t/s	—	B62, Decent
NVIDIA Nemotron 3 Nano 30B A3B BF1631.6BChatFunctions Q4_K_M·138.0 t/s	Q4_K_M	19.3 GB40%	138.0 t/s	262K	S96, Runs great
Huihui MiniCPM5 1B Abliterated1.1BChat Q4_K_M·504.2 t/s	Q4_K_M	1.3 GB3%	504.2 t/s	131K	S100, Runs great
LLaMA3.1 8B Instruct DFlash UltraChat1.0BChat BF16·204.6 t/s	BF16	2.7 GB6%	204.6 t/s	41K	S100, Runs great
Tiny Random Qwen32MChatFunctions Q4_K_M·1664.0 t/s	Q4_K_M	0.3 GB1%	1664.0 t/s	33K	S100, Runs great
NVIDIA Nemotron 3.5 Super VL 120B A12B BF16124.5BVisionFunctions IQ2_XXS·100.9 t/s	IQ2_XXS	43.2 GB90%	100.9 t/s	—	A78, Runs well
JoyAI LLM Flash49.3BChat Q4_K_M·189.6 t/s	Q4_K_M	30.6 GB64%	189.6 t/s	131K	S99, Runs great
LFM2.5 1.2B JP 2026061.2BChat Q4_K_M·466.5 t/s	Q4_K_M	1.2 GB3%	466.5 t/s	128K	S100, Runs great
Gemma 4 E4B IT OBLITERATED8.0BChatFunctions Q4_K_M·93.8 t/s	Q4_K_M	5.6 GB12%	93.8 t/s	131K	S90, Runs great

Rent NVIDIA RTX A6000 in the cloud

Hourly rate on demand for one RTX A6000.

Vast.aiMarketplace
from$0.47/hr
Deploy

Buying one ($4,649) costs as much as ~9,800 hours of rental, about 41 months at 8 hours a day.

Cost per million tokens

Output-token cost on an RTX A6000 rented at $0.47/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	$0.76$0.0089 input
Gemma 4 26B A4B IT	Q4_K_M	140 tok/s	$0.94$0.017 input
Qwen3.6 35B A3B	Q4_K_M	125 tok/s	$1.05$0.018 input
Qwen3.8 27B	Q4_K_M	29 tok/s	$4.58$0.10 input
Muse Glimmer 30B	Q4_K_M	27 tok/s	$4.83$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