RTX 5090 32GB vs RTX PRO 6000 Blackwell 96GB for Local LLMs
The RTX PRO 6000 Blackwell 96GB holds bigger models (96.0 GB vs 32.0 GB), and the RTX 5090 32GB generates tokens about 0% faster. Below: speeds on seven popular models, the models only one card can run, and our verdict.
Specs that matter for AI
| RTX 5090 32GB | RTX PRO 6000 Blackwell 96GB | |
|---|---|---|
| Memory | 32 GB | 96 GB |
| GPU-usable memory | 32.0 GB | 96.0 GB |
| Bandwidth | 1792 GB/s | 1792 GB/s |
| Architecture | Blackwell | Blackwell |
| Released | 2025-01 | 2025-04 |
| Llama 3.1 8B Q4_K_M | ~202 tok/s | ~202 tok/s |
Speed and fit by model
Q4_K_M (or native format) with 8K context.
| Model | VRAM | RTX 5090 32GB | RTX PRO 6000 Blackwell 96GB |
|---|---|---|---|
| Llama 3.1 8B | 6.3 GB | Fits ~202 tok/s | Fits ~202 tok/s |
| Qwen 3 14B | 10.5 GB | Fits ~115 tok/s | Fits ~115 tok/s |
| gpt-oss-20b | 13.9 GB | Fits ~221 tok/s | Fits ~221 tok/s |
| Qwen 3 30B-A3B | 18.8 GB | Fits ~146 tok/s | Fits ~146 tok/s |
| Qwen 3 32B | 21.4 GB | Fits ~54 tok/s | Fits ~54 tok/s |
| Llama 3.3 70B | 43.2 GB | 70% on GPU ~2.9 tok/s | Fits ~26 tok/s |
| gpt-oss-120b | 62.0 GB | 49% on GPU ~24 tok/s | Fits ~162 tok/s |
Verdict
Same Blackwell chip and the same 1,792 GB/s, so speed is identical on models that fit both. The RTX Pro 6000 triples the memory to 96 GB, which turns one card into a 70B-at-Q8 or 120B-MoE machine. If your models fit in 32 GB, the 5090 gives the same speed for a fraction of the price.
Which should you buy?
Choose the RTX 5090 32GB if…
- Your models fit in 32 GB.
- You want the same speed for a fraction of the price.
- You also game.
Choose the RTX PRO 6000 Blackwell 96GB if…
- You need 70B at Q8 or 120B-class MoE on one card.
- You serve several users or run long contexts.
- You want workstation drivers and a blower or Max-Q option.
Best quality and longest context on each card
The highest-quality quantization that fits fully at 8K context, and the longest context that fits at Q4_K_M with an FP16 cache.
| Model | RTX 5090 32GB: best quant | RTX PRO 6000 Blackwell 96GB: best quant | RTX 5090 32GB: max context | RTX PRO 6000 Blackwell 96GB: max context |
|---|---|---|---|---|
| Qwen 3.5 9B | FP16 / BF16 | FP16 / BF16 | 256K | 256K |
| Qwen 3 14B | FP16 / BF16 | FP16 / BF16 | 40K | 40K |
| gpt-oss-20b | MXFP4 (official) | MXFP4 (official) | 128K | 128K |
| Qwen 3.6 27B | Q8_0 | FP16 / BF16 | 219K | 256K |
| Qwen 3 32B | Q6_K | FP16 / BF16 | 40K | 40K |
| Llama 3.3 70B | Q2_K | Q8_0 | – | 128K |
Image generation
Best weight precision for image models with model swapping, at each model's default resolution. Image generation is also limited by compute, so the card with more tensor-core throughput finishes each image faster even when both fit.
| Model | RTX 5090 32GB | RTX PRO 6000 Blackwell 96GB |
|---|---|---|
| SDXL 1.0 | FP16 / BF16 | FP16 / BF16 |
| FLUX.1 [dev] | FP16 / BF16 | FP16 / BF16 |
| Qwen-Image | FP8 | FP16 / BF16 |
| Wan 2.2 A14B (T2V / I2V) | FP16 / BF16 | FP16 / BF16 |
Models only one card can run
Only RTX 5090 32GB
None
Only RTX PRO 6000 Blackwell 96GB
Frequently asked questions
Is the RTX 5090 32GB or RTX PRO 6000 Blackwell 96GB better for local LLMs?
Same Blackwell chip and the same 1,792 GB/s, so speed is identical on models that fit both. The RTX Pro 6000 triples the memory to 96 GB, which turns one card into a 70B-at-Q8 or 120B-MoE machine. If your models fit in 32 GB, the 5090 gives the same speed for a fraction of the price.
Which is faster for LLM inference?
The RTX 5090 32GB, by about 0% for token generation on models that fit both (about 202 vs 202 tokens/s on Llama 3.1 8B Q4_K_M).
Which can run bigger models?
The RTX PRO 6000 Blackwell 96GB, with 96.0 GB usable versus 32.0 GB.