Renting an RTX PRO 6000 Max-Q vs RTX PRO 6000 for 96GB workloads

Short answer: Both have 96 GB of GDDR7 with ECC and 1,792 GB/s of memory bandwidth, so the same models fit and memory-bound inference should behave similarly. The Max-Q is the 300 W version: NVIDIA rates it at 3,511 AI TOPS and 110 FP32 TFLOPS, against 4,000 AI TOPS and 125 FP32 TFLOPS for the 600 W RTX PRO 6000 Blackwell Workstation Edition (NVIDIA Max-Q page, NVIDIA RTX PRO 6000 page, read 2026-11-04). For jobs that fit in 96 GB and are limited by memory, such as single-user LLM serving, compare rates; for compute-heavy jobs, time a short run on each.

Specs side by side

RTX PRO 6000 Blackwell Max-Q Workstation Edition RTX PRO 6000 Blackwell Workstation Edition
GPU memory 96 GB GDDR7 with ECC 96 GB GDDR7 with ECC
Memory bandwidth 1,792 GB/s 1,792 GB/s
AI TOPS (FP4, with sparsity) 3,511 4,000
FP32 110 TFLOPS 125 TFLOPS
Max power consumption 300 W 600 W
Compute capability 12.0 12.0
Name in CloudGPU’s price list RTX PRO 6000 Max-Q RTX PRO 6000 96G

Sources: NVIDIA’s Max-Q and RTX PRO 6000 product pages and the CUDA GPUs list, which shows both Workstation Editions at 12.0; card names from /gpu-prices.json (all read 2026-11-04). CloudGPU’s “RTX PRO 6000 96G” listing does not name the edition, so check the name and power limit on your instance:

nvidia-smi --query-gpu=name,memory.total,power.limit,power.max_limit,compute_cap --format=csv

What fits in 96 GB

Weights are Hugging Face or Ollama file sizes (read 2026-11-04); KV-cache figures use the formula in How context length adds to VRAM use. “Left over” subtracts the weights and an assumed 1.5 GB of overhead, and is an estimate.

Workload Weights or files Left over on 96 GB Notes
Llama 3.3 70B, FP8 (RedHatAI) 72.7 GB about 22 GB 320 KiB per token at FP16 KV, so 32k context fits
gpt-oss-120b, native MXFP4 (openai; Ollama gpt-oss:120b) about 65 GB about 29 GB sliding-window layers keep its KV cache smaller than the formula suggests
Qwen3 32B, BF16 (unquantised) about 65.5 GB about 29 GB 256 KiB per token; the native 32k context with room for parallel requests
Qwen2.5 72B, AWQ (Qwen) 41.6 GB about 53 GB long contexts or many parallel requests
FLUX.1-dev, full 16-bit pipeline about 31.4 GB loaded in BF16 about 63 GB room for LoRAs, ControlNets and larger batches without offloading
Wan 2.2 14B, FP8 in ComfyUI two 14.29 GB experts plus a 6.74 GB text encoder about 58 GB less offloading than on 24-48 GB cards; measure at your resolution and frame count

Sources for image rows: the Hugging Face Diffusers blog (FLUX.1-dev BF16 about 31.4 GB) and the Comfy-Org/Wan-2.2 file listing, read 2026-11-04. VRAM need depends on model, precision and workflow.

For fine-tuning, 96 GB leaves room for LoRA on mid-size models at BF16 and QLoRA on larger ones; memory use depends on sequence length, batch size and gradient checkpointing. See Unsloth and Axolotl on a cloud GPU.

Commands

Both cards are Blackwell (compute capability 12.0), so use a PyTorch build for CUDA 12.8 or newer; older builds fail with “no kernel image” (how to fix it). Install vLLM as in how to serve an OpenAI-compatible API with vLLM.

# 70B at FP8 with a 32k context (Llama is gated: accept the licence and export HF_TOKEN first)
vllm serve RedHatAI/Llama-3.3-70B-Instruct-FP8-dynamic --max-model-len 32768 --gpu-memory-utilization 0.92

# gpt-oss-120b with Ollama
ollama run gpt-oss:120b

When it loads the model, vLLM logs how many tokens of KV cache it could reserve; if your --max-model-len does not fit, lower it or add --kv-cache-dtype fp8.

Max-Q or full power?

  1. Memory-bound jobs (single-user LLM chat, low-batch serving, large models that only need to fit): the two cards have the same capacity and the same bandwidth, so expect similar token generation speed. Compare the hourly rates.
  2. Compute-bound jobs (batched serving, long prompt prefill, diffusion sampling, training): the full-power card has higher peak figures (4,000 vs 3,511 AI TOPS; 125 vs 110 FP32 TFLOPS) and twice the power budget. Time the same job on both and compare work per dollar.
  3. Benchmark command for LLMs, after building llama.cpp: llama-bench -m <model>.gguf -p 2048 -n 256 -ngl 99 reports prompt (pp) and generation (tg) tokens per second.

At 2026-11-04 19:07 (UTC+8): RTX PRO 6000 Max-Q $1.39/hour, RTX PRO 6000 96G $1.79/hour. An hour of benchmarking costs $1.39 on the Max-Q and $1.79 on the RTX PRO 6000 96G; $20 of balance covers about 14 hours on the Max-Q. Billing is per minute, rounded up, 1-minute minimum, and the instance is billed while weights download (72.7 GB for the FP8 70B). Current rates are on /pricing; /deploy shows which cards can be started now and the vCPU and memory listed under each card.

Frequently asked questions

What is the difference between the RTX PRO 6000 Max-Q and the RTX PRO 6000?

The power budget and the compute peaks. Both have 96 GB of GDDR7 with ECC and 1,792 GB/s. The Max-Q is a 300 W card rated at 3,511 AI TOPS and 110 FP32 TFLOPS; the Workstation Edition is a 600 W card rated at 4,000 AI TOPS and 125 FP32 TFLOPS.

Does the Max-Q have less VRAM?

No. Both have 96 GB, so the same models and context lengths fit.

Can a 96GB GPU run a 70B model at FP8?

Yes. Llama 3.3 70B at FP8 is 72.7 GB of weights, which leaves about 22 GB for the KV cache and runtime; at 320 KiB per token that is enough for a 32k context.

Can it run gpt-oss-120b?

Yes. Its MXFP4 weights are about 65 GB on Hugging Face, and Ollama’s gpt-oss:120b is 65 GB, which leaves room for context on a 96 GB card.

Which one should I rent for image and video generation?

Both hold the full 16-bit FLUX.1-dev pipeline in memory, and the FP8 Wan 2.2 14B files with less offloading than smaller cards need. Sampling is compute-heavy, so time one image or clip on each and compare work per dollar.

Sources (read 2026-11-04, Asia/Shanghai)

The Deploy page lists the cards you can start. Rates on this page are read from CloudGPU’s live price list when the page is rendered; /pricing has the current rate for every card.

Last updated: 2026-11-04 (Asia/Shanghai).