Best AI Models for NVIDIA RTX PRO 5000 Blackwell (72.0GB)
With 72 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 5000 Blackwell Run?
121 models · 15 good
Showing compatibility for NVIDIA RTX PRO 5000 Blackwell
| Model | Quant | VRAM | Speed | Context | Status | Grade |
|---|---|---|---|---|---|---|
Q4_K_M·17.8 t/s tok/s·262K ctx·GOOD FIT | Q4_K_M | 49.2 GB | 17.8 t/s | 262K | GOOD FIT | A84 |
Q4_K_M·18.8 t/s tok/s·131K ctx·GOOD FIT | Q4_K_M | 46.6 GB | 18.8 t/s | 131K | GOOD FIT | A82 |
Q4_K_M·18.8 t/s tok/s·131K ctx·GOOD FIT | Q4_K_M | 46.6 GB | 18.8 t/s | 131K | GOOD FIT | A82 |
Q4_K_M·17.8 t/s tok/s·262K ctx·GOOD FIT | Q4_K_M | 49.2 GB | 17.8 t/s | 262K | GOOD FIT | A84 |
Q4_K_M·19.6 t/s tok/s·33K ctx·GOOD FIT | Q4_K_M | 44.6 GB | 19.6 t/s | 33K | GOOD FIT | A78 |
Q4_K_M·17.8 t/s tok/s·33K ctx·GOOD FIT | Q4_K_M | 49.0 GB | 17.8 t/s | 33K | GOOD FIT | A84 |
Q4_K_M·19.6 t/s tok/s·131K ctx·GOOD FIT | Q4_K_M | 44.6 GB | 19.6 t/s | 131K | GOOD FIT | A78 |
Q4_K_M·19.6 t/s tok/s·33K ctx·GOOD FIT | Q4_K_M | 44.6 GB | 19.6 t/s | 33K | GOOD FIT | A78 |
Q4_K_M·20.2 t/s tok/s·131K ctx·GOOD FIT | Q4_K_M | 43.3 GB | 20.2 t/s | 131K | GOOD FIT | A76 |
Q4_K_M·19.6 t/s tok/s·131K ctx·GOOD FIT | Q4_K_M | 44.6 GB | 19.6 t/s | 131K | GOOD FIT | A78 |
Q4_K_M·20.2 t/s tok/s·131K ctx·GOOD FIT | Q4_K_M | 43.3 GB | 20.2 t/s | 131K | GOOD FIT | A76 |
Q4_K_M·20.2 t/s tok/s·8K ctx·GOOD FIT | Q4_K_M | 43.3 GB | 20.2 t/s | 8K | GOOD FIT | A76 |
Q4_K_M·20.3 t/s tok/s·4K ctx·GOOD FIT | Q4_K_M | 43.0 GB | 20.3 t/s | 4K | GOOD FIT | A76 |
BF16·20.4 t/s tok/s·4K ctx·GOOD FIT | BF16 | 42.8 GB | 20.4 t/s | 4K | GOOD FIT | A75 |
Q3_K_M·14.7 t/s tok/s·GOOD FIT | Q3_K_M | 59.5 GB | 14.7 t/s | — | GOOD FIT | A77 |
Q4_K_M·30.6 t/s tok/s·33K ctx·FAIR FIT | Q4_K_M | 28.6 GB | 30.6 t/s | 33K | FAIR FIT | B55 |
NVIDIA RTX PRO 5000 Blackwell Specifications
- Brand
- NVIDIA
- Architecture
- Blackwell
- Compute Capability
- 12.0 (CUDA SM version)
- VRAM
- 72.0 GB GDDR7
- Memory Bandwidth
- 1344.0 GB/s
- CUDA Cores
- 14,080
- Tensor Cores
- 440
- FP16 Performance
- 73.69 TFLOPS
- TDP
- 300W
- Release Date
- 2025-04-01
- MSRP
- $4,500
Get Started
GPUs to Consider Over NVIDIA RTX PRO 5000 Blackwell
Similar GPUs and upgrades with more VRAM or higher bandwidth for AI
NVIDIA GH200 Grace Hopper Superchip
NVIDIA · Hopper (Grace Hopper)
NVIDIA H200 NVL
NVIDIA · Hopper
NVIDIA H200 SXM
NVIDIA · Hopper
NVIDIA H100 SXM
NVIDIA · Hopper
AMD Instinct MI250X
AMD · CDNA 2
NVIDIA A100 80GB SXM
NVIDIA · Ampere
Frequently Asked Questions
- Can NVIDIA RTX PRO 5000 Blackwell run Qwen3 Next 80B A3B Instruct?
Yes, the NVIDIA RTX PRO 5000 Blackwell with 72 GB can run Qwen3 Next 80B A3B Instruct, Llama 3.3 70B Instruct, Llama 3.1 70B Instruct, and 1390 other models. 8 models run at excellent quality, and 76 at good quality. Check the compatibility table above for the full list with VRAM usage and estimated speed.
- Is NVIDIA RTX PRO 5000 Blackwell good for AI?
The NVIDIA RTX PRO 5000 Blackwell has 72 GB of GDDR7, making it excellent for running local AI models. It supports 84 models at good quality or better. With 1344.0 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 5000 Blackwell handle?
With 72 GB, the NVIDIA RTX PRO 5000 Blackwell supports models from 3B to 70B+ parameters depending on quantization level. At Q4_K_M (the recommended sweet spot), you can fit roughly 120B parameters. This means 7B models at high quality (Q6/Q8) or 30B+ models at Q4.
- What quantization should I use on NVIDIA RTX PRO 5000 Blackwell?
For the best balance of quality and speed on the NVIDIA RTX PRO 5000 Blackwell, 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 5000 Blackwell for AI inference?
With 1344.0 GB/s memory bandwidth, the NVIDIA RTX PRO 5000 Blackwell achieves approximately 194 tokens/sec on a 7B model at Q4_K_M — that's very fast, well above conversational speed. A 14B model runs at ~97 tok/s. Token generation speed scales inversely with model size — smaller models are significantly faster.
tok/s = (1344 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 5000 Blackwell
~18 tok/s~19 tok/s~19 tok/s~18 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 5000 Blackwell?
The top-rated models for the NVIDIA RTX PRO 5000 Blackwell are Qwen3 Next 80B A3B Instruct, Llama 3.3 70B Instruct, Llama 3.1 70B Instruct. 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 5000 Blackwell need?
The NVIDIA RTX PRO 5000 Blackwell has a TDP of 300 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 650 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.