workstation
Rent Nvidia RTX PRO 6000 S.
Blackwell
96GB VRAM
From $1.47/hr
Per hour
$1.47
Per day
$35.36
Per week
$247.51
Per month
$1061
Provider spread
3 providers ·
up to 56% cheaper at the low end
Cheapest · $1.47/hr on Vast.ai
Median $2.04/hr
Most expensive · $3.36/hr on Amazon Web Services
Price history
Daily median across providers.
Loading...
Where to rent it
All providers carrying this GPU.
AI models that fit
See all 6 →
Run these on this GPU.
- Mixtral 8x22B 1× · int4
- Mistral Large 2 1× · int4
- Qwen3.5 122B A10b Fp8 1× · int4
Cloud instances
See all 18 →
Hyperscaler bundles.
Pre-configured on 1 clouds — from $3.36/hr total
($3.36/hr per GPU).
FAQ
Frequently asked.
What AI models can I run on a Nvidia RTX PRO 6000 S?
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 96GB of VRAM.
What's the VRAM minimum to run a model on the Nvidia RTX PRO 6000 S?
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 PRO 6000 S 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
$3.36/hr
Lowest $/hr per GPU
$3.36/hr
Providers
1
Instance shapes
18
| Provider | Instance | GPUs | vCPU | RAM | Disk | $/hr | $/hr per GPU | |
|---|---|---|---|---|---|---|---|---|
| g7e.2xlarge | 1× | — | — | — | $3.36/hr | $3.36/hr | ||
| g7e.2xlarge | 1× | — | — | — | $3.36/hr | $3.36/hr | ||
| g7e.2xlarge | 1× | — | — | — | $3.36/hr | $3.36/hr | ||
| g7e.4xlarge | 1× | — | — | — | $4.00/hr | $4.00/hr | ||
| g7e.4xlarge | 1× | — | — | — | $4.00/hr | $4.00/hr | ||
| g7e.4xlarge | 1× | — | — | — | $4.00/hr | $4.00/hr | ||
| g7e.8xlarge | 1× | — | — | — | $5.27/hr | $5.27/hr | ||
| g7e.8xlarge | 1× | — | — | — | $5.27/hr | $5.27/hr | ||
| g7e.8xlarge | 1× | — | — | — | $5.27/hr | $5.27/hr | ||
| g7e.12xlarge | 2× | — | — | — | $8.29/hr | $4.14/hr | ||
| g7e.12xlarge | 2× | — | — | — | $8.29/hr | $4.14/hr | ||
| g7e.12xlarge | 2× | — | — | — | $8.29/hr | $4.14/hr | ||
| g7e.24xlarge | 4× | — | — | — | $16.57/hr | $4.14/hr | ||
| g7e.24xlarge | 4× | — | — | — | $16.57/hr | $4.14/hr | ||
| g7e.24xlarge | 4× | — | — | — | $16.57/hr | $4.14/hr | ||
| g7e.48xlarge | 8× | — | — | — | $33.14/hr | $4.14/hr | ||
| g7e.48xlarge | 8× | — | — | — | $33.14/hr | $4.14/hr | ||
| g7e.48xlarge | 8× | — | — | — | $33.14/hr | $4.14/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 PRO 6000 S?
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 96GB of VRAM.
What's the VRAM minimum to run a model on the Nvidia RTX PRO 6000 S?
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 PRO 6000 S 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
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 PRO 6000 S.
Mixtral 8x22B
by Mistral AI
Required GPUs
1× Nvidia RTX PRO 6000 S
Total VRAM
96 GB
Parameters
141B (39B active)
Context
66K tokens
Mistral Large 2
by Mistral AI
Required GPUs
1× Nvidia RTX PRO 6000 S
Total VRAM
96 GB
Parameters
123B
Context
128K tokens
Qwen3.5 122B A10b Fp8
by Alibaba (Qwen Team)
Required GPUs
1× Nvidia RTX PRO 6000 S
Total VRAM
96 GB
Parameters
122B
Context
262K tokens
Qwen: Qwen3.5-122B-A10B
by Alibaba (Qwen Team)
Required GPUs
1× Nvidia RTX PRO 6000 S
Total VRAM
96 GB
Parameters
122B
Context
262K tokens
Nvidia Nemotron 3 Super 120B A12b Fp8
by Nvidia
Required GPUs
1× Nvidia RTX PRO 6000 S
Total VRAM
96 GB
Parameters
120B
Context
262K tokens
Nvidia Nemotron 3 Super 120B A12b Bf16
by Nvidia
Required GPUs
1× Nvidia RTX PRO 6000 S
Total VRAM
96 GB
Parameters
120B
Context
262K 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 PRO 6000 S?
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 96GB of VRAM.
What's the VRAM minimum to run a model on the Nvidia RTX PRO 6000 S?
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 PRO 6000 S 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.