Best AI Models for NVIDIA RTX PRO 6000 Blackwell Server Edition (96.0GB)
Passive 96 GB server variant — needs chassis airflow; commonly rented per-GPU rather than bought for a desktop.
With 96 GB of memory, this is a high-end configuration for local AI. You can comfortably run most open-source LLMs including large 70B parameter models at good quantization levels, making it one of the best setups for serious local AI work.
At this memory tier, nearly every popular open-source model is within reach. You can run Llama 3 70B at Q4_K_M or even Q5_K_M quantization with room to spare, handle coding assistants like DeepSeek Coder 33B at high quality, and easily run any 7B–30B model at full or near-full precision. Context windows remain generous even with larger models, so multi-turn conversations and long-document processing work smoothly.
Runs Well
- 70B models (Llama 3 70B, Qwen 72B) at Q4–Q5
- 30B models at Q6–Q8 quality
- 7B–14B models at full FP16 precision
- Vision models (LLaVA, CogVLM) without compromise
Challenging
- Mixture-of-experts models like Mixtral 8x22B at higher quants
- 120B+ models still require lower quantizations
What LLMs Can NVIDIA RTX PRO 6000 Blackwell Server Edition Run?
146 models · 80 excellent · 33 good
Showing compatibility for NVIDIA RTX PRO 6000 Blackwell Server Edition
| Model | Quant | VRAM | Speed | Context | Status | Grade |
|---|---|---|---|---|---|---|
Q4_K_M·14.3 t/s tok/s·131K ctx·DECENT | Q4_K_M | 72.7 GB | 14.3 t/s | 131K | DECENT | B54 |
Q4_K_M·14.5 t/s tok/s·DECENT | Q4_K_M | 71.7 GB | 14.5 t/s | — | DECENT | B54 |
Q4_K_M·13.9 t/s tok/s·262K ctx·DECENT | Q4_K_M | 74.7 GB | 13.9 t/s | 262K | DECENT | B53 |
Q4_K_M·15.6 t/s tok/s·131K ctx·DECENT | Q4_K_M | 66.7 GB | 15.6 t/s | 131K | DECENT | B55 |
BF16·14.3 t/s tok/s·262K ctx·DECENT | BF16 | 72.6 GB | 14.3 t/s | 262K | DECENT | B54 |
Q4_K_M·14.2 t/s tok/s·DECENT | Q4_K_M | 73.3 GB | 14.2 t/s | — | DECENT | B53 |
IQ2_XS·14.1 t/s tok/s·164K ctx·DECENT | IQ2_XS | 73.5 GB | 14.1 t/s | 164K | DECENT | B53 |
Q4_K_M·16.0 t/s tok/s·131K ctx·DECENT | Q4_K_M | 65.1 GB | 16.0 t/s | 131K | DECENT | B56 |
Q3_K_M·12.8 t/s tok/s·1049K ctx·DECENT | Q3_K_M | 80.9 GB | 12.8 t/s | 1049K | DECENT | B47 |
Q4_K_M·22.3 t/s tok/s·131K ctx·DECENT | Q4_K_M | 46.6 GB | 22.3 t/s | 131K | DECENT | B62 |
Q4_K_M·13.2 t/s tok/s·DECENT | Q4_K_M | 78.5 GB | 13.2 t/s | — | DECENT | B49 |
Q4_K_M·22.3 t/s tok/s·131K ctx·DECENT | Q4_K_M | 46.6 GB | 22.3 t/s | 131K | DECENT | B62 |
Q4_K_M·21.1 t/s tok/s·262K ctx·DECENT | Q4_K_M | 49.2 GB | 21.1 t/s | 262K | DECENT | B61 |
Q4_K_M·23.3 t/s tok/s·33K ctx·DECENT | Q4_K_M | 44.6 GB | 23.3 t/s | 33K | DECENT | B64 |
Q4_K_M·21.1 t/s tok/s·262K ctx·DECENT | Q4_K_M | 49.2 GB | 21.1 t/s | 262K | DECENT | B61 |
Q4_K_M·21.2 t/s tok/s·33K ctx·DECENT | Q4_K_M | 49.0 GB | 21.2 t/s | 33K | DECENT | B61 |
NVIDIA RTX PRO 6000 Blackwell Server Edition Specifications
- Brand
- NVIDIA
- Architecture
- Blackwell
- Compute Capability
- 12.0 (CUDA SM version)
- VRAM
- 96.0 GB GDDR7
- Memory Bandwidth
- 1597.0 GB/s
- CUDA Cores
- 24,064
- Tensor Cores
- 752
- TDP
- 600W
- Release Date
- 2025-05-01
Get Started
Prompt Processing
Estimated for Midnight Miqu 70B V1.5, the compute-bound phase that reads your prompt before the first reply token appears.
Short chat
314 ms
512 tok prompt
Long chat
2.5 s
4,096 tok prompt
Document / codebase
20.1 s
32,768 tok prompt
Prefill is compute-bound and a different number from the decode tok/s shown elsewhere on this page — how prompt processing works →
Efficiency & Value
Based on Midnight Miqu 70B V1.5 at ~24.5 tok/s decode.
Tokens per watt
Higher is better.
Performance figures are estimates calibrated as of 2026-07-30 — see calibration basis →
GPUs to Consider Over NVIDIA RTX PRO 6000 Blackwell Server Edition
Similar GPUs and upgrades with more VRAM or higher bandwidth for AI
NVIDIA B200
NVIDIA · Blackwell
AMD Instinct MI300X
AMD · CDNA 3
NVIDIA GH200 Grace Hopper Superchip
NVIDIA · Hopper (Grace Hopper)
NVIDIA H200 NVL
NVIDIA · Hopper
NVIDIA H200 SXM
NVIDIA · Hopper
AMD Instinct MI250X
AMD · CDNA 2
Frequently Asked Questions
- Can NVIDIA RTX PRO 6000 Blackwell Server Edition run GPT OSS 120B?
Yes, the NVIDIA RTX PRO 6000 Blackwell Server Edition with 96 GB can run GPT OSS 120B, Llama 4 Scout 17B 16E Instruct, NVIDIA Nemotron 3 Super 120B A12B BF16, and 1702 other models. 1252 models run at excellent quality, and 283 at good quality. Check the compatibility table above for the full list with VRAM usage and estimated speed.
- Is NVIDIA RTX PRO 6000 Blackwell Server Edition good for AI?
The NVIDIA RTX PRO 6000 Blackwell Server Edition has 96 GB of GDDR7, making it excellent for running local AI models. It supports 1535 models at good quality or better. With 1597.0 GB/s memory bandwidth, it delivers fast token generation speeds. This is an enthusiast-grade GPU that handles most popular open-source LLMs.
- How many parameters can NVIDIA RTX PRO 6000 Blackwell Server Edition handle?
With 96 GB, the NVIDIA RTX PRO 6000 Blackwell Server Edition supports models from 3B to 70B+ parameters depending on quantization level. At Q4_K_M (the recommended sweet spot), you can fit roughly 160B parameters. This means 7B models at high quality (Q6/Q8) or 30B+ models at Q4.
- What quantization should I use on NVIDIA RTX PRO 6000 Blackwell Server Edition?
For the best balance of quality and speed on the NVIDIA RTX PRO 6000 Blackwell Server Edition, start with Q4_K_M — it preserves ~85% of the original model quality while keeping VRAM usage reasonable. With 24+ GB, you have the headroom to run 7B models at Q5_K_M or even Q6_K for noticeably better output quality. For larger 30B models, Q4_K_M remains the sweet spot.
- How fast is NVIDIA RTX PRO 6000 Blackwell Server Edition for AI inference?
With 1597.0 GB/s memory bandwidth, the NVIDIA RTX PRO 6000 Blackwell Server Edition achieves approximately 231 tokens/sec on a 7B model at Q4_K_M — that's very fast, well above conversational speed. A 14B model runs at ~115 tok/s. Token generation speed scales inversely with model size — smaller models are significantly faster.
tok/s = (1597 GB/s ÷ model GB) × efficiency
Smaller models = faster inference. Memory bandwidth is the main bottleneck for token generation speed.
Estimated speed on NVIDIA RTX PRO 6000 Blackwell Server Edition
~14 tok/s~15 tok/s~14 tok/s~16 tok/sReal-world results typically within ±20%. Speed depends on quantization kernel, batch size, and software stack.
- What's the best model for NVIDIA RTX PRO 6000 Blackwell Server Edition?
The top-rated models for the NVIDIA RTX PRO 6000 Blackwell Server Edition are GPT OSS 120B, Llama 4 Scout 17B 16E Instruct, NVIDIA Nemotron 3 Super 120B A12B BF16. The best choice depends on your use case: coding assistants benefit from code-tuned models, while general chat works well with instruction-tuned models like Llama or Qwen.
- What power supply and cooling does NVIDIA RTX PRO 6000 Blackwell Server Edition need?
The NVIDIA RTX PRO 6000 Blackwell Server Edition has a TDP of 600 W. A good rule of thumb is to provide at least double the GPU's TDP to cover the rest of the system — that means a 1200 W PSU or larger. At this power level, a high-airflow case matters: aim for at least two front intake fans and one rear exhaust, with tidy cabling so hot air isn't trapped around the card. LLM inference sustains full GPU load continuously — longer and more consistently than most gaming workloads — so also make sure your CPU cooler can keep up under combined load.
- Anything to watch out for with NVIDIA RTX PRO 6000 Blackwell Server Edition?
Passive 96 GB server variant — needs chassis airflow; commonly rented per-GPU rather than bought for a desktop.