Compatibility check
Can you run Llama 3.2 3B Instruct on the RTX 4070?
Yes. Llama 3.2 3B Instruct fits on the RTX 4070 (12 GB) in full FP16/BF16 precision, using about 7.8 GB including a 0.4 GB KV cache at 4,096 tokens. You have comfortable headroom for longer prompts and modest batching.
Memory breakdown
Weights plus a 0.4 GB KV cache at 4,096tokens, against the card's 12 GB. Verdicts leave ~10% headroom for activations and fragmentation.
| Precision | Weights | KV cache | Total | % of 12 GB | Fit |
|---|---|---|---|---|---|
| FP16 / BF16full quality | 7.4 GB | 0.4 GB | 7.8 GB | 65% | Fits |
| INT8 (8-bit)near-full quality | 3.7 GB | 0.4 GB | 4.1 GB | 34% | Fits |
| INT4 (4-bit)GPTQ / AWQ / GGUF Q4 | 1.8 GB | 0.4 GB | 2.2 GB | 18% | Fits |
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 3.2 3B Instruct
Cards where this model fits (at its best precision):
Models that fit the RTX 4070
Other popular models that run on this card:
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Frequently asked questions
Can the RTX 4070 run Llama 3.2 3B Instruct?
Yes. In FP16 it uses about 7.8 GB, which fits the RTX 4070's 12 GB.
How much VRAM does Llama 3.2 3B Instruct need?
Approximately 7.4 GB in FP16, 3.7 GB in INT8, and 1.8 GB in 4-bit for the weights, plus a KV cache of about 0.4 GB at 4,096 tokens.
Does quantization let Llama 3.2 3B Instruct fit on the RTX 4070?
Yes. Dropping to FP16 / BF16 brings total usage to about 7.8 GB, which fits the 12 GB card with headroom for the KV cache.
What happens to memory with longer context?
The KV cache grows linearly with prompt length. At 4,096 tokens it is about 0.4 GB here; doubling the context roughly doubles that term, so long-context use can push a tight fit over the edge.