Compatibility check
Can you run OLMo 2 7B Instruct on the RTX 4090?
Yes. OLMo 2 7B Instruct fits on the RTX 4090 (24 GB) in full FP16/BF16 precision, using about 18.8 GB including a 2 GB KV cache at 4,096 tokens. You have comfortable headroom for longer prompts and modest batching.
Memory breakdown
Weights plus a 2 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 | 16.8 GB | 2 GB | 18.8 GB | 78% | Fits |
| INT8 (8-bit)near-full quality | 8.4 GB | 2 GB | 10.4 GB | 43% | Fits |
| INT4 (4-bit)GPTQ / AWQ / GGUF Q4 | 4.2 GB | 2 GB | 6.2 GB | 26% | 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 OLMo 2 7B Instruct
Cards where this model fits (at its best precision):
Models that fit the RTX 4090
Other popular models that run on this card:
Go deeper
Frequently asked questions
Can the RTX 4090 run OLMo 2 7B Instruct?
Yes. In FP16 it uses about 18.8 GB, which fits the RTX 4090's 24 GB.
How much VRAM does OLMo 2 7B Instruct need?
Approximately 16.8 GB in FP16, 8.4 GB in INT8, and 4.2 GB in 4-bit for the weights, plus a KV cache of about 2 GB at 4,096 tokens.
Does quantization let OLMo 2 7B Instruct fit on the RTX 4090?
Yes. Dropping to FP16 / BF16 brings total usage to about 18.8 GB, which fits the 24 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 2 GB here; doubling the context roughly doubles that term, so long-context use can push a tight fit over the edge.