✅Runs wellfp16 · 4K ctx · batch 1
Run Llama 3.1 8B on RTX 5090
At fp16 precision, Llama 3.1 8B (8.0 B parameters) needs roughly 19.5 GB of VRAM. RTX 5090 has 32 GB, leaving 12.5 GB of headroom — plenty of room for longer contexts and larger batch sizes. Expected throughput ≈ 105 tokens/sec on a single card.
61%
VRAM
Runs well
Estimated VRAM (fp16)
19.5 GB
of 32 GB on RTX 5090· 12.5 GB free
Tok/sec
105
TFTT
637ms
Power
220W
Rent on GPUniq marketplace
RTX 5090
Live prices from verified providers · billed hourly, no commitment.
Memory breakdown (fp16)
Model Weights
16.00 GBKV Cache
0.50 GBActivations
2.00 GBFramework Overhead
1.04 GBQuantization comparison
Lower precision = less VRAM with a small quality trade-off. Quality order: FP16 > INT8 > INT4.
| Precision | VRAM | Utilisation | Fits on RTX 5090? |
|---|---|---|---|
| FP16 (full precision) | 19.5 GB | 61% | ✅Runs well |
| INT8 (8-bit) | 10.5 GB | 33% | ✅Runs easily |
| INT4 / Q4 (4-bit) | 6.0 GB | 19% | ✅Runs easily |
Frequently asked
- Can RTX 5090 run Llama 3.1 8B?
- Yes. Llama 3.1 8B needs ≈ 19.5 GB VRAM at fp16 and RTX 5090 provides 32 GB. Expected throughput is 105 tokens/sec per GPU.
- How much VRAM does Llama 3.1 8B use?
- About 19.5 GB at fp16, 6.0 GB at INT4 (for a 4K context, batch size 1). Longer contexts add to KV cache size; larger batches increase activations.
- What's the fastest way to run Llama 3.1 8B on RTX 5090?
- Use a production inference engine like vLLM, SGLang, or TensorRT-LLM with paged attention — they cut KV cache 2–3× vs the naive estimate and batch multiple requests efficiently. For RTX 5090, enable Flash Attention 2.
- Where can I rent a RTX 5090?
- GPUniq aggregates live RTX 5090 offers from verified providers. You can deploy an instance in about a minute and pay hourly with no commitment.
Other GPUs for Llama 3.1 8B
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