Compatibility check
Can you run Phi-3.5 Mini Instruct on the RTX 3050?
Yes, with quantization. Phi-3.5 Mini Instruct does not fit the RTX 3050 (8 GB) in FP16, but it runs at INT8 (8-bit) using about 5.9 GB (74% of VRAM). Use a GPTQ, AWQ, or GGUF build to get there, and keep prompts moderate to leave room for the KV cache.
Memory breakdown
Weights plus a 1.5 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 | 8.8 GB | 1.5 GB | 10.3 GB | 129% | No |
| INT8 (8-bit)near-full quality | 4.4 GB | 1.5 GB | 5.9 GB | 74% | Fits |
| INT4 (4-bit)GPTQ / AWQ / GGUF Q4 | 2.2 GB | 1.5 GB | 3.7 GB | 46% | 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 RTX 3050
Other popular models that run on this card:
Go deeper
Frequently asked questions
Can the RTX 3050 run Phi-3.5 Mini Instruct?
Not in FP16, but yes at INT8 (8-bit), where it uses about 5.9 GB versus the card's 8 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 RTX 3050?
Yes. Dropping to INT8 (8-bit) brings total usage to about 5.9 GB, which fits the 8 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.