Compatibility check
Can you run Llama 3.1 70B Instruct on the RTX 3070?
Not on a single RTX 3070. Even at 4-bit, Llama 3.1 70B Instruct needs about 41.9 GB, which exceeds the card's 8 GB. You would need roughly 6× RTX 3070 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 8 GB. Verdicts leave ~10% headroom for activations and fragmentation.
| Precision | Weights | KV cache | Total | % of 8 GB | Fit |
|---|---|---|---|---|---|
| FP16 / BF16full quality | 162.4 GB | 1.3 GB | 163.7 GB | 2046% | No |
| INT8 (8-bit)near-full quality | 81.2 GB | 1.3 GB | 82.5 GB | 1031% | No |
| INT4 (4-bit)GPTQ / AWQ / GGUF Q4 | 40.6 GB | 1.3 GB | 41.9 GB | 524% | 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 RTX 3070
Other popular models that run on this card:
Go deeper
Frequently asked questions
Can the RTX 3070 run Llama 3.1 70B Instruct?
No. Even 4-bit needs about 41.9 GB, more than the 8 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 RTX 3070?
Not on a single RTX 3070 — even 4-bit exceeds 8 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.