Compatibility check
Can you run Qwen2.5 14B Instruct on the RTX 6000 Ada?
Yes. Qwen2.5 14B Instruct fits on the RTX 6000 Ada (48 GB) in full FP16/BF16 precision, using about 34.8 GB including a 0.8 GB KV cache at 4,096 tokens. You have comfortable headroom for longer prompts and modest batching.
Memory breakdown
Weights plus a 0.8 GB KV cache at 4,096tokens, against the card's 48 GB. Verdicts leave ~10% headroom for activations and fragmentation.
| Precision | Weights | KV cache | Total | % of 48 GB | Fit |
|---|---|---|---|---|---|
| FP16 / BF16full quality | 34 GB | 0.8 GB | 34.8 GB | 73% | Fits |
| INT8 (8-bit)near-full quality | 17 GB | 0.8 GB | 17.8 GB | 37% | Fits |
| INT4 (4-bit)GPTQ / AWQ / GGUF Q4 | 8.5 GB | 0.8 GB | 9.3 GB | 19% | 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 Qwen2.5 14B Instruct
Cards where this model fits (at its best precision):
Models that fit the RTX 6000 Ada
Other popular models that run on this card:
Go deeper
Frequently asked questions
Can the RTX 6000 Ada run Qwen2.5 14B Instruct?
Yes. In FP16 it uses about 34.8 GB, which fits the RTX 6000 Ada's 48 GB.
How much VRAM does Qwen2.5 14B Instruct need?
Approximately 34 GB in FP16, 17 GB in INT8, and 8.5 GB in 4-bit for the weights, plus a KV cache of about 0.8 GB at 4,096 tokens.
Does quantization let Qwen2.5 14B Instruct fit on the RTX 6000 Ada?
Yes. Dropping to FP16 / BF16 brings total usage to about 34.8 GB, which fits the 48 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 0.8 GB here; doubling the context roughly doubles that term, so long-context use can push a tight fit over the edge.