RTX Pro 5000 Blackwell LLM Performance
Local LLM Performance: 84.3 t/s average on 14B models at 16k context. Updated Benchmarks: March 2026.
84.3T/s
3,064T/s
Summary
NVIDIA RTX Pro 5000 Blackwell proved to be an exceptional workstation-class performer, striking a perfect balance between capacity and speed. With its 48GB of GDDR7 VRAM, we were able to run Llama 3.3 70B models at Q4 quantization entirely in memory. We also tested its NVFP4 acceleration and massive context handling, pushing mid-sized models up to 256k tokens.
Key Insights
Relative Performance (Token Generation)
Current Price in US
$4,600
Avg. Market Value
Current Pricing
Biggest LLMs You Can Run on This GPU
The models below represent the largest language models that fit fully in VRAM on this GPU using 4-bit quantization (GGUF). Benchmarks include token generation and prompt processing speeds measured at their maximum supported context length.
Note: Context values are grouped into standard tiers (4K, 16K, 32K, 64K, 128K). Models may support slightly higher context, but they remain in the lower tier unless they reach the next bracket.
RTX Pro 5000 Blackwell local LLM Inference Performance vs Similar GPUs
Compare prompt ingestion and token generation speeds against similar GPUs across widely used local models and extended context lengths up to 256K.
Local LLM Benchmarks
Prompt processing (t/s) and token generation speed (t/s) across different open weight models and context lengths.
| Model | 4k Ctx | 16k Ctx | 32k Ctx | 64k Ctx | 128k Ctx | 256k Ctx |
|---|---|---|---|---|---|---|
| Qwen3 8B (Q4_K) | 8,175.3 | 5,846.4 | 4,006.2 | 2,120.7 | 899.1 | — |
| Qwen3 14B (Q4_K) | 4,515.7 | 3,064.2 | 2,069.8 | 1,125.1 | 434.2 | — |
| gpt-oss 20B (MXFP4) | 10,339.6 | 8,747.7 | 7,257.7 | 5,273.9 | 3,163.3 | — |
| Qwen3 30B A3B (Q4_K) | 5,266.1 | 4,043.8 | 3,032.7 | 1,708.9 | 702.7 | 364.1 |
| Qwen3 32B (Q4_K) | 2,030.3 | 1,497.7 | 1,079.8 | 629.6 | — | — |
| Llama 3.3 70B (Q4_K) | 1,022.9 | 826.3 | — | — | — | — |
| Model | 4k Ctx | 16k Ctx | 32k Ctx | 64k Ctx | 128k Ctx | 256k Ctx |
|---|---|---|---|---|---|---|
| Qwen3 8B (Q4_K) | 159.8 | 126.3 | 98.3 | 64.1 | 38.1 | — |
| Qwen3 14B (Q4_K) | 98.9 | 84.3 | 70.8 | 53.8 | 36.5 | — |
| gpt-oss 20B (MXFP4) | 213.4 | 196.2 | 182.8 | 161.4 | 130.9 | — |
| Qwen3 30B A3B (Q4_K) | 188.8 | 148.5 | 123.2 | 91.9 | 61.4 | 37.0 |
| Qwen3 32B (Q4_K) | 46.6 | 42.0 | 36.4 | 28.9 | — | — |
| Llama 3.3 70B (Q4_K) | 24.0 | 22.4 | — | — | — | — |
Frequently Asked Questions
Common questions about running LLMs on the RTX Pro 5000 Blackwell.
Can the RTX Pro 5000 Blackwell run 70B models?
Yes, RTX Pro 5000 runs Llama 3.3 70B with 16K context in 4-bit quantization.
What is the recommended power supply?
A minimum of 850W Gold is recommended for a single card to ensure stability under full load.
What is the max model that the RTX Pro 5000 can run?
The maximum capacity allows for running models as large as Llama 3.3 70B with a 16k context window.
Is the RTX Pro 5000 faster than the RTX 5090?
No, the RTX 5090 is faster in both categories. For example, with Qwen3 32B (32k context), the RTX Pro 5000 achieves 36 t/s generation (vs 43 t/s on the 5090) and 1079 t/s in prompt processing (vs 1451 t/s on the 5090). The RTX Pro 5000's primary advantage is its larger 48GB VRAM buffer compared to consumer cards.