Compatibility check
Can you run Phi-3.5 Mini Instruct on the NVIDIA A10G?
Yes. Phi-3.5 Mini Instruct fits on the NVIDIA A10G (24 GB) in full FP16/BF16 precision, using about 10.3 GB including a 1.5 GB KV cache at 4,096 tokens. You have comfortable headroom for longer prompts and modest batching.
Memory breakdown
Weights plus a 1.5 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 | 8.8 GB | 1.5 GB | 10.3 GB | 43% | Fits |
| INT8 (8-bit)near-full quality | 4.4 GB | 1.5 GB | 5.9 GB | 25% | Fits |
| INT4 (4-bit)GPTQ / AWQ / GGUF Q4 | 2.2 GB | 1.5 GB | 3.7 GB | 15% | 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 Phi-3.5 Mini Instruct
Cards where this model fits (at its best precision):
Models that fit the NVIDIA A10G
Other popular models that run on this card:
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Frequently asked questions
Can the NVIDIA A10G run Phi-3.5 Mini Instruct?
Yes. In FP16 it uses about 10.3 GB, which fits the NVIDIA A10G's 24 GB.
How much VRAM does Phi-3.5 Mini Instruct need?
Approximately 8.8 GB in FP16, 4.4 GB in INT8, and 2.2 GB in 4-bit for the weights, plus a KV cache of about 1.5 GB at 4,096 tokens.
Does quantization let Phi-3.5 Mini Instruct fit on the NVIDIA A10G?
Yes. Dropping to FP16 / BF16 brings total usage to about 10.3 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 1.5 GB here; doubling the context roughly doubles that term, so long-context use can push a tight fit over the edge.