workstation
Louer Nvidia RTX 6000 Ada.
Ada Lovelace
48GB VRAM
300W
From $0.56/hr
Per hour
$0.56
Per day
$13.44
Per week
$94.08
Per month
$403
Provider spread
5 providers ·
up to 65% cheaper at the low end
Cheapest · $0.56/hr on TensorDock
Median $1.10/hr
Most expensive · $1.59/hr on Lambda
Price history
Daily median across providers.
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Where to rent it
All providers carrying this GPU.
Buy vs rent
Should you rent or own?
Your usage
Rent on cloud
Per day
—
Per month
—
at $0.56/hr cheapest provider rate
Own on-prem
Electricity per day
—
300W TDP
Rent breakeven on $6,800 MSRP
— days
· — months
Workloads
Suitable workloads.
AI models that fit
See all 57 →
Run these on this GPU.
- Llama 4 Scout 17B 16E Instruct Fp8 Lora 1× · fp16
- Llama 4 Maverick Instruct (17Bx128E) FP8 1× · fp16
- Llama 4 Scout (17Bx16E) 1× · fp16
Cloud instances
See all 3 →
Hyperscaler bundles.
Pre-configured on 3 clouds — from $1.10/hr total
($1.10/hr per GPU).
FAQ
Frequently asked.
What AI models can I run on a Nvidia RTX 6000 Ada?
The grid above lists every open-weights model with a recommended GPU configuration for this card. Each row tells you the minimum GPU count and the quantization level (FP16, FP8, INT8, INT4) needed to load the model in 48GB of VRAM.
What's the VRAM minimum to run a model on the Nvidia RTX 6000 Ada?
Rule of thumb: a model needs roughly (parameters × bytes-per-weight × 1.2) of VRAM to load, plus headroom for the KV cache during inference. FP16 = 2 bytes/weight, FP8/INT8 = 1 byte, INT4 = 0.5 bytes. A 70B model at FP16 needs ~168GB; at INT4 it drops to ~42GB and fits a single high-VRAM card.
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 6000 Ada or only do inference?
Fine-tuning needs 4–8× more VRAM than inference at the same model size — gradients, optimizer state, and activations all live in memory. LoRA / QLoRA cut that overhead dramatically (often 4–10×). The notes column flags whether a row is an inference-only recommendation or includes a fine-tuning path.
Where do these GPU-count recommendations come from?
We curate them from official model cards, community benchmark threads (r/LocalLLaMA, HuggingFace forum), and known-good configurations published by the model makers. Each recommendation has been verified to load at the stated quantization on the listed GPU count — though throughput and context-length still vary by workload.
Cloud instance options
Pre-configured instances on hyperscalers.
Whole-instance bundles (GPU + vCPU + RAM + disk) on the major clouds. Per-GPU rate often drops as the count rises. View = spec page · Launch = sign up (affiliate).
Cheapest bundle
$1.10/hr
Lowest $/hr per GPU
$1.10/hr
Providers
3
Instance shapes
3
| Provider | Instance | GPUs | vCPU | RAM | Disk | $/hr | $/hr per GPU | |
|---|---|---|---|---|---|---|---|---|
| rtx6000-ada-on-demand | 1× | — | — | — | $1.10/hr | $1.10/hr | ||
| gpu-rtx6000x1 | 1× | — | — | — | $1.49/hr | $1.49/hr | ||
| gpu_1x_rtx6000_ada | 1× | — | — | — | $1.59/hr | $1.59/hr |
Looking for the cheapest rate?
Hyperscaler bundles include managed networking + SLAs. Raw per-GPU rental on P2P marketplaces is typically 3–10× cheaper.
See raw rental rates on the Overview tab →
FAQ
Cloud instances — common questions.
What AI models can I run on a Nvidia RTX 6000 Ada?
The grid above lists every open-weights model with a recommended GPU configuration for this card. Each row tells you the minimum GPU count and the quantization level (FP16, FP8, INT8, INT4) needed to load the model in 48GB of VRAM.
What's the VRAM minimum to run a model on the Nvidia RTX 6000 Ada?
Rule of thumb: a model needs roughly (parameters × bytes-per-weight × 1.2) of VRAM to load, plus headroom for the KV cache during inference. FP16 = 2 bytes/weight, FP8/INT8 = 1 byte, INT4 = 0.5 bytes. A 70B model at FP16 needs ~168GB; at INT4 it drops to ~42GB and fits a single high-VRAM card.
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 6000 Ada or only do inference?
Fine-tuning needs 4–8× more VRAM than inference at the same model size — gradients, optimizer state, and activations all live in memory. LoRA / QLoRA cut that overhead dramatically (often 4–10×). The notes column flags whether a row is an inference-only recommendation or includes a fine-tuning path.
Where do these GPU-count recommendations come from?
We curate them from official model cards, community benchmark threads (r/LocalLLaMA, HuggingFace forum), and known-good configurations published by the model makers. Each recommendation has been verified to load at the stated quantization on the listed GPU count — though throughput and context-length still vary by workload.
AI models
FP16
FP16
FP16
FP16
FP16
FP8
FP8
FP8
FP8
FP8
FP8
FP8
FP8
FP8
FP8
FP8
FP8
FP8
FP8
INT4
INT4
INT4
INT4
INT4
INT4
INT4
INT4
INT4
INT4
INT4
INT4
INT4
INT4
INT4
INT4
INT4
INT4
INT4
INT4
INT4
INT4
INT4
INT4
INT4
INT4
INT4
INT4
INT4
INT4
INT4
INT4
INT4
INT4
INT4
INT4
INT4
INT4
Models that run on this GPU.
GPU-count + quantization recommendations covering fine-tuning, inference, and run-it-yourself scenarios on the Nvidia RTX 6000 Ada.
Llama 4 Scout 17B 16E Instruct Fp8 Lora
by Meta AI
Required GPUs
1× Nvidia RTX 6000 Ada
Total VRAM
48 GB
Parameters
17B
Context
10.5M tokens
Llama 4 Maverick Instruct (17Bx128E) FP8
by Meta AI
Required GPUs
1× Nvidia RTX 6000 Ada
Total VRAM
48 GB
Parameters
17B
Context
1M tokens
Llama 4 Scout (17Bx16E)
by Meta AI
Required GPUs
1× Nvidia RTX 6000 Ada
Total VRAM
48 GB
Parameters
17B
Context
262K tokens
Llama 4 Scout Instruct (17Bx16E)
by Meta AI
Required GPUs
1× Nvidia RTX 6000 Ada
Total VRAM
48 GB
Parameters
17B
Context
1M tokens
Gemma 3 27B
by Google DeepMind
Required GPUs
1× Nvidia RTX 6000 Ada
Total VRAM
48 GB
Parameters
27B (27B active)
Context
128K tokens
Qwen 2.5 Coder 32B
by Alibaba (Qwen Team)
Required GPUs
1× Nvidia RTX 6000 Ada
Total VRAM
48 GB
Parameters
33B
Context
128K tokens
DeepSeek R1 Distill Qwen 32B
by DeepSeek
Required GPUs
1× Nvidia RTX 6000 Ada
Total VRAM
48 GB
Parameters
33B
Context
128K tokens
Qwen 2.5 32B
by Alibaba (Qwen Team)
Required GPUs
1× Nvidia RTX 6000 Ada
Total VRAM
48 GB
Parameters
33B
Context
128K tokens
Qwen 3 32B
by Alibaba (Qwen Team)
Required GPUs
1× Nvidia RTX 6000 Ada
Total VRAM
48 GB
Parameters
33B
Context
128K tokens
LLaVA 34B
by LLaVA Project
Required GPUs
1× Nvidia RTX 6000 Ada
Total VRAM
48 GB
Parameters
34B
Context
4K tokens
Code Llama 34B
by Meta AI
Required GPUs
1× Nvidia RTX 6000 Ada
Total VRAM
48 GB
Parameters
34B
Context
16K tokens
DeepSeek Coder 33B
by DeepSeek
Required GPUs
1× Nvidia RTX 6000 Ada
Total VRAM
48 GB
Parameters
33B
Context
16K tokens
Yi-34B
by 01.AI
Required GPUs
1× Nvidia RTX 6000 Ada
Total VRAM
48 GB
Parameters
34B
Context
32K tokens
Qwen: Qwen3.6 35B A3B
by Alibaba (Qwen Team)
Required GPUs
1× Nvidia RTX 6000 Ada
Total VRAM
48 GB
Parameters
35B
Context
262K tokens
Qwen: Qwen3.5-35B-A3B
by Alibaba (Qwen Team)
Required GPUs
1× Nvidia RTX 6000 Ada
Total VRAM
48 GB
Parameters
35B
Context
262K tokens
TheDrummer: Skyfall 36B V2
by Thedrummer
Required GPUs
1× Nvidia RTX 6000 Ada
Total VRAM
48 GB
Parameters
36B
Context
33K tokens
Holo3 35B A3b
by Hcompany
Required GPUs
1× Nvidia RTX 6000 Ada
Total VRAM
48 GB
Parameters
35B
Context
262K tokens
Deepseek Coder 33B Instruct
by DeepSeek
Required GPUs
1× Nvidia RTX 6000 Ada
Total VRAM
48 GB
Parameters
33B
Context
16K tokens
Qwen3.6 35B A3b Fp8
by Alibaba (Qwen Team)
Required GPUs
1× Nvidia RTX 6000 Ada
Total VRAM
48 GB
Parameters
35B
Context
262K tokens
Llama 3.3 70B
by Meta AI
Required GPUs
1× Nvidia RTX 6000 Ada
Total VRAM
48 GB
Parameters
70B
Context
128K tokens
Qwen 2.5 72B
by Alibaba (Qwen Team)
Required GPUs
1× Nvidia RTX 6000 Ada
Total VRAM
48 GB
Parameters
73B
Context
128K tokens
DeepSeek R1 Distill Llama 70B
by DeepSeek
Required GPUs
1× Nvidia RTX 6000 Ada
Total VRAM
48 GB
Parameters
70B
Context
128K tokens
Llama 3.1 70B
by Meta AI
Required GPUs
1× Nvidia RTX 6000 Ada
Total VRAM
48 GB
Parameters
70B
Context
128K tokens
Code Llama 70B
by Meta AI
Required GPUs
1× Nvidia RTX 6000 Ada
Total VRAM
48 GB
Parameters
70B
Context
16K tokens
Hermes 3 70B
by Nous Research
Required GPUs
1× Nvidia RTX 6000 Ada
Total VRAM
48 GB
Parameters
70B
Context
128K tokens
Xiaomi: MiMo-V2.5-Pro
by Xiaomi
Required GPUs
1× Nvidia RTX 6000 Ada
Total VRAM
48 GB
Parameters
65B
Context
1M tokens
Writer: Palmyra X5
by Writer
Required GPUs
1× Nvidia RTX 6000 Ada
Total VRAM
48 GB
Parameters
70B
Context
1M tokens
Nous: Hermes 4 70B
by Nous Research
Required GPUs
1× Nvidia RTX 6000 Ada
Total VRAM
48 GB
Parameters
70B
Context
131K tokens
Morph: Morph V3 Large
by Morph
Required GPUs
1× Nvidia RTX 6000 Ada
Total VRAM
48 GB
Parameters
70B
Context
262K tokens
Qwen: Qwen2.5 VL 72B Instruct
by Alibaba (Qwen Team)
Required GPUs
1× Nvidia RTX 6000 Ada
Total VRAM
48 GB
Parameters
72B
Context
131K tokens
Sao10K: Llama 3.1 70B Hanami x1
by Sao10k
Required GPUs
1× Nvidia RTX 6000 Ada
Total VRAM
48 GB
Parameters
70B
Context
16K tokens
Sao10K: Llama 3.3 Euryale 70B
by Sao10k
Required GPUs
1× Nvidia RTX 6000 Ada
Total VRAM
48 GB
Parameters
70B
Context
131K tokens
Magnum v4 72B
by Anthracite Org
Required GPUs
1× Nvidia RTX 6000 Ada
Total VRAM
48 GB
Parameters
72B
Context
33K tokens
Sao10K: Llama 3.1 Euryale 70B v2.2
by Sao10k
Required GPUs
1× Nvidia RTX 6000 Ada
Total VRAM
48 GB
Parameters
70B
Context
131K tokens
Sao10k: Llama 3 Euryale 70B v2.1
by Sao10k
Required GPUs
1× Nvidia RTX 6000 Ada
Total VRAM
48 GB
Parameters
70B
Context
8K tokens
Meta: Llama 3 70B Instruct
by Meta AI
Required GPUs
1× Nvidia RTX 6000 Ada
Total VRAM
48 GB
Parameters
70B
Context
8K tokens
Meta Llama 3.3 70B Instruct Turbo
by Meta AI
Required GPUs
1× Nvidia RTX 6000 Ada
Total VRAM
48 GB
Parameters
70B
Context
131K tokens
Meta Llama 3.1 70B Instruct Turbo
by Meta AI
Required GPUs
1× Nvidia RTX 6000 Ada
Total VRAM
48 GB
Parameters
70B
Context
131K tokens
nim/meta/llama-3.1-70b-instruct
by Meta AI
Required GPUs
1× Nvidia RTX 6000 Ada
Total VRAM
48 GB
Parameters
70B
Context
16K tokens
nim/nvidia/llama-3.1-nemotron-70b-instruct
by Nvidia
Required GPUs
1× Nvidia RTX 6000 Ada
Total VRAM
48 GB
Parameters
70B
Context
16K tokens
Cogito V1 Preview Llama 70B
by Deepcogito
Required GPUs
1× Nvidia RTX 6000 Ada
Total VRAM
48 GB
Parameters
70B
Context
131K tokens
Cogito V1 Preview Llama 70B Turbo
by Deepcogito
Required GPUs
1× Nvidia RTX 6000 Ada
Total VRAM
48 GB
Parameters
70B
Context
131K tokens
Qwen 2 (72B)
by Alibaba (Qwen Team)
Required GPUs
1× Nvidia RTX 6000 Ada
Total VRAM
48 GB
Parameters
72B
Context
33K tokens
Qwen2.5 72B
by Alibaba (Qwen Team)
Required GPUs
1× Nvidia RTX 6000 Ada
Total VRAM
48 GB
Parameters
72B
Context
131K tokens
Llama 3.1 70B
by Meta AI
Required GPUs
1× Nvidia RTX 6000 Ada
Total VRAM
48 GB
Parameters
70B
Context
131K tokens
Meta Llama 3 70B Instruct Turbo
by Meta AI
Required GPUs
1× Nvidia RTX 6000 Ada
Total VRAM
48 GB
Parameters
70B
Context
8K tokens
nim/meta/llama-3.3-70b-instruct
by Meta AI
Required GPUs
1× Nvidia RTX 6000 Ada
Total VRAM
48 GB
Parameters
70B
Context
16K tokens
Llama 3.1 Nemotron 70B Instruct HF
by Nvidia
Required GPUs
1× Nvidia RTX 6000 Ada
Total VRAM
48 GB
Parameters
70B
Context
33K tokens
meta-llama/Llama-3.3-70B-Instruct
by Meta AI
Required GPUs
1× Nvidia RTX 6000 Ada
Total VRAM
48 GB
Parameters
70B
Context
131K tokens
Llama 3.3 70B Instruct FP8 Lora
by Meta AI
Required GPUs
1× Nvidia RTX 6000 Ada
Total VRAM
48 GB
Parameters
70B
Context
131K tokens
Qwen2.5 72B Instruct
by Alibaba (Qwen Team)
Required GPUs
1× Nvidia RTX 6000 Ada
Total VRAM
48 GB
Parameters
72B
Context
33K tokens
Qwen2 72B Instruct
by Togethercomputer
Required GPUs
1× Nvidia RTX 6000 Ada
Total VRAM
48 GB
Parameters
72B
Context
33K tokens
Qwen2.5 72B Instruct Turbo
by Alibaba (Qwen Team)
Required GPUs
1× Nvidia RTX 6000 Ada
Total VRAM
48 GB
Parameters
72B
Context
131K tokens
Qwen2-VL (72B) Instruct
by Alibaba (Qwen Team)
Required GPUs
1× Nvidia RTX 6000 Ada
Total VRAM
48 GB
Parameters
72B
Context
33K tokens
Hermes-3-Llama-3.1-70B
by Nous Research
Required GPUs
1× Nvidia RTX 6000 Ada
Total VRAM
48 GB
Parameters
70B
Context
131K tokens
L3.1-70B-Euryale-v2.2
by Sao10k
Required GPUs
1× Nvidia RTX 6000 Ada
Total VRAM
48 GB
Parameters
70B
Context
131K tokens
Meta-Llama-3.1-70B-Instruct
by Meta AI
Required GPUs
1× Nvidia RTX 6000 Ada
Total VRAM
48 GB
Parameters
70B
Context
131K tokens
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 6000 Ada?
The grid above lists every open-weights model with a recommended GPU configuration for this card. Each row tells you the minimum GPU count and the quantization level (FP16, FP8, INT8, INT4) needed to load the model in 48GB of VRAM.
What's the VRAM minimum to run a model on the Nvidia RTX 6000 Ada?
Rule of thumb: a model needs roughly (parameters × bytes-per-weight × 1.2) of VRAM to load, plus headroom for the KV cache during inference. FP16 = 2 bytes/weight, FP8/INT8 = 1 byte, INT4 = 0.5 bytes. A 70B model at FP16 needs ~168GB; at INT4 it drops to ~42GB and fits a single high-VRAM card.
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 6000 Ada or only do inference?
Fine-tuning needs 4–8× more VRAM than inference at the same model size — gradients, optimizer state, and activations all live in memory. LoRA / QLoRA cut that overhead dramatically (often 4–10×). The notes column flags whether a row is an inference-only recommendation or includes a fine-tuning path.
Where do these GPU-count recommendations come from?
We curate them from official model cards, community benchmark threads (r/LocalLLaMA, HuggingFace forum), and known-good configurations published by the model makers. Each recommendation has been verified to load at the stated quantization on the listed GPU count — though throughput and context-length still vary by workload.