Updated April 2026
| GPU | Memory | Bandwidth | Price Range | Ideal Workload |
|---|---|---|---|---|
| RTX 5090 | 32 GB GDDR7 | 1,792 GB/s | ~$1,999 | 32B to 70B models (quantized), high-throughput local inference |
| Dual RTX 5090 | 64 GB GDDR7 | ~3,584 GB/s | ~$4,000 | LLaMA 3.3 70B comfortably |
| RTX PRO 6000 Blackwell | 96 GB GDDR7 | ~? | ~$8,500 | 120B+ MoE models on a single card |
| RTX 4090 | 24 GB GDDR6X | 1,010 GB/s | $1,600–$2,000 | 7B–13B models, quantized fine-tuning |
| Mac Studio M3 Ultra | 512 GB unified | 819 GB/s | $9,499 | 70B+ quantized, research and large-context workloads |
The RTX 5090 is the best GPU for most LLM workloads in 2026. Its 32 GB of GDDR7 memory running at 1,792 GB/s bandwidth handles models up to 70B parameters at Q4 quantization for around $2,000, making it the clear sweet spot for users running models up to 70B parameters.
For larger models, a dual RTX 5090 setup (64 GB combined, ~$4,000) runs LLaMA 3.3 70B comfortably, while the RTX PRO 6000 Blackwell (96 GB, ~$8,500) fits 120B+ MoE models on a single card.
Key advantage: Memory bandwidth determines token generation speed for LLMs. The RTX 5090's 1,790+ GB/s in GDDR7 crushes consumer competition and approaches datacenter levels.
For enterprise or research needs beyond consumer cards: