❌Won't fitfp16 · 4K ctx · batch 1
Does RTX 4090 run Llama 3.1 70B? No at fp16.
At fp16 precision, Llama 3.1 70B (70 B parameters) needs roughly 142.9 GB of VRAM. RTX 4090 only has 24 GB, short by 118.9 GB. INT4 quantization brings it down to 38.5 GB.
595%
VRAM
Won't fit
Estimated VRAM (fp16)
142.9 GB
of 24 GB on RTX 4090· 118.9 GB short
Tok/sec
7
TFTT
10133ms
Power
333W
Rent on GPUniq marketplace
RTX 4090
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Memory breakdown (fp16)
Quantization comparison
Lower precision = less VRAM with a small quality trade-off. Quality order: FP16 > INT8 > INT4.
| Precision | VRAM | Utilisation | Fits on RTX 4090? |
|---|---|---|---|
| FP16 (full precision) | 142.9 GB | 595% | ❌Won't fit |
| INT8 (8-bit) | 73.3 GB | 305% | ❌Won't fit |
| INT4 / Q4 (4-bit) | 38.5 GB | 161% | ❌Won't fit |
Frequently asked
- Can RTX 4090 run Llama 3.1 70B?
- Not at fp16 — Llama 3.1 70B needs about 142.9 GB while RTX 4090 has 24 GB. It fits at INT4 quantization (38.5 GB) with some accuracy trade-off.
- How much VRAM does Llama 3.1 70B use?
- About 142.9 GB at fp16, 38.5 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 70B on RTX 4090?
- 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 4090, enable Flash Attention 2.
- Where can I rent a RTX 4090?
- GPUniq aggregates live RTX 4090 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 70B
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None of the common quantization levels fit this model on RTX 4090. Consider multi-GPU deployment or a larger card.