Phi-3.5 Mini (3.8B): Hardware & Business Fit
- Reasoning
- Multilingual
- Long context
A compact model that punches above its size on reasoning, with a long context and an MIT license that keeps commercial use simple. Verify the exact release.
- Parameters
- ~3.8B
- Context
- ~128K tokens
- Deployment
- local
- VRAM @ 4-bit
- ~2.5GB
What Phi-3.5 Mini (3.8B) is good for
- ▸Reasoning on a budget
- ▸On-device assistant
- ▸RAG
Best quantization choices
Approximate memory per quantization (weights + KV cache at modest context). Treat as ±.
| Quant | ~Memory | When to use |
|---|---|---|
| Q4_K_M | ~2.5GB | Best size/quality trade-off — the usual default for local serving. |
| Q8_0 | ~4GB | Higher fidelity; ~1.7× the memory of 4-bit. |
| FP16 | ~8GB | Full precision; largest footprint, best quality. |
Run Phi-3.5 Mini (3.8B) locally
Pull and run with Ollama, or grab the weights from Hugging Face.
$ ollama run phi3.5microsoft/Phi-3.5-mini-instructCompatible hardware
Devices from our catalog graded for Phi-3.5 Mini (3.8B), best fit first.
- NVIDIA B200 (placeholder)NVIDIA · Datacenter GPUs
Fits at FP16 (~8GB) with ~161GB headroom — about 21 concurrent instances.
FP16 · ~8GBRuns well - Supermicro 8x H100 SuperServerSupermicro · AI Servers
Fits at FP16 (~8GB) with ~555.2GB headroom — about 70 concurrent instances.
FP16 · ~8GBRuns well - Dell PowerEdge XE9680Dell · AI Servers
Fits at FP16 (~8GB) with ~555.2GB headroom — about 70 concurrent instances.
FP16 · ~8GBRuns well - AMD Instinct MI300XAMD · Datacenter GPUs
Fits at FP16 (~8GB) with ~161GB headroom — about 21 concurrent instances.
FP16 · ~8GBRuns well - Cloud B200 (Blackwell profile, to verify)Cloud · Cloud GPU Profiles
Fits at FP16 (~8GB) with ~150.4GB headroom — about 19 concurrent instances.
FP16 · ~8GBRuns well - NVIDIA H200 (141GB)NVIDIA · Datacenter GPUs
Fits at FP16 (~8GB) with ~116.1GB headroom — about 15 concurrent instances.
FP16 · ~8GBRuns well - Cloud H200 141GB (profile)Cloud · Cloud GPU Profiles
Fits at FP16 (~8GB) with ~116.1GB headroom — about 15 concurrent instances.
FP16 · ~8GBRuns well - NVIDIA H100 (80GB)NVIDIA · Datacenter GPUs
Fits at FP16 (~8GB) with ~62.4GB headroom — about 8 concurrent instances.
FP16 · ~8GBRuns well - Cloud H100 80GB (profile)Cloud · Cloud GPU Profiles
Fits at FP16 (~8GB) with ~62.4GB headroom — about 8 concurrent instances.
FP16 · ~8GBRuns well - NVIDIA RTX PRO 6000 BlackwellNVIDIA · Professional GPUs
Fits at FP16 (~8GB) with ~76.5GB headroom — about 10 concurrent instances.
FP16 · ~8GBRuns well
Use inside the AI Business OS
Phi-3.5 Mini (3.8B) suits these AI Business OS agent archetypes:
A model is only the engine. Inside the AI Business OS it is wrapped with permissions, tools, connectors, RAG and audit so it can actually do business work safely — see how the AI Business OS works →
Frequently asked questions
What hardware do I need to run Phi-3.5 Mini (3.8B)?+
At 4-bit you need roughly ~2.5GB of usable memory. The minimum self-hostable option in our catalog is the NVIDIA GeForce RTX 3060 12GB. For a comfortable run we recommend the NVIDIA B200 (placeholder).
Which quantization should I use for Phi-3.5 Mini (3.8B)?+
Q4_K_M is the usual default — the best size/quality trade-off. Step up to Q8_0 or FP16 if you have spare memory and want higher fidelity.
Should I run Phi-3.5 Mini (3.8B) locally or in the cloud?+
Local-first is recommended for Phi-3.5 Mini (3.8B). It fits comfortably on hardware you can own, keeping data private and costs predictable.
Other sizes in the Phi family
All Phi models →Same family, different size. Pick the variant that fits your hardware.
Related models
Similar picks — family siblings and nearest-size models of the same kind.
Use Phi-3.5 Mini (3.8B) inside your AI Business OS
BrainOutput helps you run Phi-3.5 Mini (3.8B) as a private business agent — wrapped with the tools, connectors, RAG and guardrails it needs to do real work on hardware you control.
Use this model in your AI Business OS