Can the RTX 5090 Run Llama 3?
Yes — easily for Llama 3.1 8B at FP16 with full 128K context. Llama 3.3 70B needs INT4 to fit the 5090’s 32 GB; FP8 / FP16 require a 6000 Pro or multi-GPU cluster.
YesThe RTX 5090 (32 GB) runs Llama 3 (8B family) at FP16 with 14 GB of VRAM headroom for KV cache and concurrent batching.
Detailed Breakdown
The RTX 5090 is the best-priced single GPU we rent for Llama 3 8B-class deployments. Detailed fit:
- Llama 3.1 8B FP16 — 16 GB. Trivial fit. 128K context with full KV cache headroom.
- Llama 3.1 8B FP8 — 8 GB. Lots of room for batching and longer contexts.
- Llama 3.1 8B Vision — ~22 GB. Fits comfortably.
- Llama 3.3 70B FP16 — 140 GB. Doesn’t fit.
- Llama 3.3 70B FP8 — 70 GB. Doesn’t fit.
- Llama 3.3 70B INT4 (AWQ) — 40 GB. Doesn’t fit a single 5090. 2× 5090 fits.
- Llama 3.3 70B INT3 — 30 GB. Single 5090 fits but quality starts to drop.
For 70B serving on a single GPU, use the 6000 Pro 96 GB. For 70B on a budget, use 2× RTX 5090 in tensor parallel — combined 64 GB fits Llama 3.3 70B INT4 with comfortable headroom.
Frequently Asked Questions
The questions buyers actually ask before committing to a GPU server.
Llama 3.1 8B throughput on a 5090?
About 1,200 tok/s aggregate at FP16, 1,800+ at FP8. ~85 tok/s single-stream.
Llama 3.3 70B INT4 on 1× vs 2× 5090?
Single 5090 — only INT3 fits comfortably. 2× 5090 in tensor parallel fits INT4 with KV cache room.
Llama 3.2 Vision on a 5090?
11B Vision — ~22 GB, fits. 90B Vision — needs multi-GPU.
Llama 3.1 405B?
No. Multi-node H100 cluster only.
Related Pages
Pages our visitors typically read next.
Ready to deploy?
Same-day deployment on in-stock GPUs. Talk to a specialist who actually understands your workload.
