Rent Nvidia RTX 6000D.

Name: Nvidia RTX 6000D
Brand: Nvidia
SKU: nvidia-rtx-6000d

Blackwell 84GB VRAM 600W 2025 From $1.29/hr

Per hour

$1.29

Per day

$30.96

Per week

$216.72

Per month

$929

Price history

Daily median across providers.

Where to rent it

All providers carrying this GPU.

Provider	$/hr	Range	Offers	30d trend	Updated
CloudGPU	$1.29/hr	$1.29/hr–$1.29/hr	167	·	about 1 hour	Launch ↗

Buy vs rent

Should you rent or own?

Your usage

Hours per day Electricity rate ($/kWh)

Rent on cloud

Per day

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Per month

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at $1.29/hr cheapest provider rate

Own on-prem

Electricity per day

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600W TDP

AI models that fit See all 228 →

Run these on this GPU.

MoonshotAI: Kimi K2.7 Code 8× · int4
inclusionAI: Ling-2.6-1T 8× · int4
inclusionAI: Ring-2.6-1T 8× · int4

AI models

Models that run on this GPU.

Open-weights models that fit this card for inference, with the fewest cards and the precision that load each one. Computed daily from the model's size and the card's memory. Showing the 60 largest of 228 models that fit.

Renting for inference?

Pair these models with the cheapest provider on the rental table. See rental rates on the Overview tab →

FAQ

AI models on this GPU.

What AI models can I run on a Nvidia RTX 6000D?

The list above shows open-weights models whose memory need fits this card: on one card when the model uses at least a quarter of its 84 GB, or on 2, 4 or 8 of them when it is too large for one. Each row gives the fewest cards and the precision (FP16, FP8 or INT4) that load it.

What's the VRAM minimum to run a model on the Nvidia RTX 6000D?

We count the weights (parameters × bytes per weight: FP16 = 2, FP8 = 1, INT4 = 0.5), add the KV cache for an 8K-token context, and add 15% for the runtime. A model fits when that is within 90% of the cards' memory. A 70B model needs about 164 GB at FP16 and about 43 GB at INT4. A longer context needs more.

How does quantization (FP16 vs FP8 vs INT4) affect what fits?

Lower-precision quantization shrinks the memory footprint nearly linearly with the bit count. The trade-off is output quality: FP16 is the reference, FP8 is usually indistinguishable for most prompts, INT8 introduces small quality losses, INT4 is noticeably degraded on reasoning-heavy tasks but fine for chat. The badge on each row tells you which level the recommendation assumes.

Can I fine-tune on the Nvidia RTX 6000D or only do inference?

The rows here are for inference. Full fine-tuning needs several times more memory than inference at the same model size, because gradients, optimizer state and activations all live in memory. LoRA and QLoRA cut that a lot. The fine-tune cost tool sizes a run for you.

How are these GPU counts worked out?

We compute them every day from the model's parameter count and the card's memory, with the formula above. They are estimates, not tests we ran. Tokens per second, where shown, is an estimate from the card's memory bandwidth on one card.

Rent Nvidia RTX 6000D.

Daily median across providers.

All providers carrying this GPU.

Should you rent or own?

Run these on this GPU.

Models that run on this GPU.

MoonshotAI: Kimi K2.7 Code

inclusionAI: Ling-2.6-1T

inclusionAI: Ring-2.6-1T

Xiaomi: MiMo-V2.6-Pro

Xiaomi: MiMo-V2.5-Pro

Kimi K2.6

MoonshotAI: Kimi K2 0905

Kimi K2.5

Kimi K2 Thinking

Kimi K2

Inkling FP4

Meta: Muse Spark Open

Tencent: Hy4 preview

DeepSeek: DeepSeek V4.1 Flash 1017

DeepSeek: DeepSeek V4.1 Flash

GLM-5

GLM-5.1

glm-5.3

Z.ai: GLM 5.2

DeepSeek: DeepSeek V3.2

DeepSeek: DeepSeek V3.1 Terminus

DeepSeek: DeepSeek V3 0324

DeepSeek: DeepSeek V3.2 Speciale

DeepSeek-V3.1

Nex AGI: DeepSeek V3.1 Nex N1

DeepSeek: DeepSeek V3.2 Exp

DeepSeek V3

DeepSeek: R1 0528

Deep Cogito: Cogito v2.1 671B

DeepSeek R1

StepFun: Step 5 Preview

NVIDIA: Nemotron 3 Ultra

Reflection: Beam 1.0 501B A23B

Qwen: Qwen3 Coder 480B A35B

Minimax M1 80K

MiniMax-Text-01

MiniMax: MiniMax M1

MiniMax: MiniMax M3

Baidu: ERNIE 4.5 VL 424B A47B

Nous: Hermes 3 405B Instruct

Nous: Hermes 4 405B

Llama 3.1 405B

Meta: Llama 4 Maverick

Arcee AI: Trinity Large Thinking

Qwen: Qwen3.5 397B A17B

Nex AGI: Nex-N2-Pro

Nex AGI: Nex-N2.5-Pro

Hunyuan-Large

GLM-4.7

GLM-4.6

GLM-4.5

Z.ai: GLM 5.3 Flash

Xiaomi: MiMo-V2.5

Xiaomi: MiMo-V2.6-Flash

Xiaomi: MiMo-V2-Flash

DeepSeek: DeepSeek V4 Flash Vision Exp

DeepSeek: DeepSeek V4 Flash 0731

Baidu: ERNIE 4.5 300B A47B

Tencent: Hy3 preview

Tencent: Hy3

AI models on this GPU.