⚠️Tight fitfp16 · 4K ctx · batch 1
Run Llama 3.1 8B on RTX 3090
At fp16 precision, Llama 3.1 8B (8.0 B parameters) needs roughly 19.5 GB of VRAM. RTX 3090 has 24 GB — fits, but only 4.5 GB spare. You'll want shorter sequences, batch 1, or INT8 quantization (6.0 GB at INT4).
81%
VRAM
Tight fit
Estimated VRAM (fp16)
19.5 GB
of 24 GB on RTX 3090· 4.5 GB free
Tok/sec
51
TFTT
1302ms
Power
134W
Rent on GPUniq marketplace
RTX 3090
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 3090? |
|---|---|---|---|
| FP16 (full precision) | 19.5 GB | 81% | ⚠️Tight fit |
| INT8 (8-bit) | 10.5 GB | 44% | ✅Runs easily |
| INT4 / Q4 (4-bit) | 6.0 GB | 25% | ✅Runs easily |
Frequently asked
- Can RTX 3090 run Llama 3.1 8B?
- Yes. Llama 3.1 8B needs ≈ 19.5 GB VRAM at fp16 and RTX 3090 provides 24 GB. Expected throughput is 51 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 3090?
- 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 3090, enable Flash Attention 2 and INT8/INT4 quantization to leave headroom for batching.
- Where can I rent a RTX 3090?
- GPUniq aggregates live RTX 3090 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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Want to tweak sequence length, batch size, or fine-tuning? Open the full calculator →