Compatibility check
Can you run Llama 3.1 70B Instruct on the NVIDIA L4?
Not on a single NVIDIA L4. Even at 4-bit, Llama 3.1 70B Instruct needs about 41.9 GB, which exceeds the card's 24 GB. You would need roughly 2× NVIDIA L4 with tensor parallelism, a larger GPU, or a smaller model.
Memory breakdown
Weights plus a 1.3 GB KV cache at 4,096tokens, against the card's 24 GB. Verdicts leave ~10% headroom for activations and fragmentation.
| Precision | Weights | KV cache | Total | % of 24 GB | Fit |
|---|---|---|---|---|---|
| FP16 / BF16full quality | 162.4 GB | 1.3 GB | 163.7 GB | 682% | No |
| INT8 (8-bit)near-full quality | 81.2 GB | 1.3 GB | 82.5 GB | 344% | No |
| INT4 (4-bit)GPTQ / AWQ / GGUF Q4 | 40.6 GB | 1.3 GB | 41.9 GB | 175% | No |
Planning estimates, not a substitute for profiling. Real usage varies with the inference runtime, batch size, and how much context you actually use — the KV cache grows linearly with prompt length.
GPUs that run Llama 3.1 70B Instruct
Cards where this model fits (at its best precision):
Models that fit the NVIDIA L4
Other popular models that run on this card:
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Frequently asked questions
Can the NVIDIA L4 run Llama 3.1 70B Instruct?
No. Even 4-bit needs about 41.9 GB, more than the 24 GB available.
How much VRAM does Llama 3.1 70B Instruct need?
Approximately 162.4 GB in FP16, 81.2 GB in INT8, and 40.6 GB in 4-bit for the weights, plus a KV cache of about 1.3 GB at 4,096 tokens.
Does quantization let Llama 3.1 70B Instruct fit on the NVIDIA L4?
Not on a single NVIDIA L4 — even 4-bit exceeds 24 GB. You would need multiple GPUs or a larger card.
What happens to memory with longer context?
The KV cache grows linearly with prompt length. At 4,096 tokens it is about 1.3 GB here; doubling the context roughly doubles that term, so long-context use can push a tight fit over the edge.