Microsoft · Phi 3.5
Phi 3.5 Mini Instruct
Phi 3.5 Mini Instruct is a 4B chat and text-generation model available through supported Badgr execution routes.
Last validated or meaningfully updated: 2026-07-27
Availability
✓ Badgr AI API
Available through Badgr
✓ Dedicated endpoint
Available through Badgr
✓ Custom GPU deployment
Available through Badgr
Deployment profile
- Architecture
- Phi3ForCausalLM
- Licence
- MIT
- Minimum VRAM
- ~12GB
- Context
- 128K tokens
Recommended: RTX 4090 24GB. VRAM is an estimate and increases with context, cache, concurrency, and runtime overhead.
Model identity
- Languages
- multilingual
- Updated
- 9 months ago
- HuggingFace revision
- 2fe1924501
Popularity and trust
Downloads (last month)
343.4K
Likes
1.1K
Spaces using this model
100
Files and formats
Weight formats
Safetensors
Repository files
20
Tokenizer
SentencePiece
Chat template
Available
This repo ships custom modeling code (trust_remote_code required to load with Transformers).
GPU deployment scenarios
Estimated from parameter count and quantisation. Will switch to “Verified by Badgr” once a real run backs a tier.
Testing
RTX 4090 24GB
Short context, low concurrency
Small production
L40S 48GB
8K context, 1-4 concurrent requests
Higher throughput
A100 80GB
32K context, continuous batching
Large production
RTX 4090 24GB
Higher concurrency
Estimated — multimodal inputs, long context, and concurrency all increase real VRAM use beyond this estimate.
Also runs well on L40S 48GB, A100 40GB. Available in United States, Europe.
Serving compatibility
| Runtime | Status | Notes |
|---|---|---|
| vLLM | Estimated | Used for Badgr dedicated endpoints |
| Transformers | Declared by source | Basic fallback |
| llama.cpp | Requires GGUF conversion | No GGUF weights found in repo |
| SGLang | Unknown | Not evaluated |
| TensorRT-LLM | Unknown | Conversion may be required |
| TGI | Unknown | Not evaluated |
Call this model through Badgr
curl https://api.aibadgr.com/v1/chat/completions \
-H "Authorization: Bearer $BADGR_API_KEY" \
-H "Content-Type: application/json" \
-d '{"model":"microsoft/Phi-3.5-mini-instruct","messages":[{"role":"user","content":"Hello"}]}'Ready-to-run Badgr configurations
Quick test
badgr serve microsoft/Phi-3.5-mini-instruct --max-cost 2Validated dedicated endpoint command
badgr serve microsoft/Phi-3.5-mini-instruct --gpu RTX 4090 --max-cost 10Advanced configuration (defaults to automatic)
- Runtime
- Quantisation
- GPU and GPU count
- Context length
- Maximum concurrency
- Region
- Maximum hourly spend
- Persistent or capped runtime
Estimated pricing
Provider estimate: cold start
2–5 min
Provider estimate: endpoint cost
$0.18 – $0.44/hr
Idle cost
$0 when stopped
Per-token throughput cost is not shown here: Badgr has not benchmarked this model yet, and this page does not display figures it cannot back with real data.
Recommended Badgr routes
RTX 4090 24GB · United States
Availablefrom $0.18/hr
Best for
ComfyUI, Batch Inference, Qwen 2.5 7B Instruct
Startup: 2–5 min
Reliability: Standard
Last checked: just now
RTX 4090 24GB · United States
Availablefrom $0.26/hr
Best for
ComfyUI, Batch Inference, Qwen 2.5 7B Instruct
Startup: 2–5 min
Reliability: Standard
Last checked: just now
RTX 4090 24GB · United States
Availablefrom $0.26/hr
Best for
ComfyUI, Batch Inference, Qwen 2.5 7B Instruct
Startup: 2–5 min
Reliability: Standard
Last checked: just now
RTX 4090 24GB · United States
Availablefrom $0.38/hr
Best for
ComfyUI, Batch Inference, Qwen 2.5 7B Instruct
Startup: 2–5 min
Reliability: Standard
Last checked: just now
RTX 4090 24GB · United States
Availablefrom $0.44/hr
Best for
ComfyUI, Batch Inference, Qwen 2.5 7B Instruct
Startup: 2–5 min
Reliability: Standard
Last checked: just now
RTX 4090 24GB · United States
Availablefrom $0.44/hr
Best for
ComfyUI, Batch Inference, Qwen 2.5 7B Instruct
Startup: 2–5 min
Reliability: Standard
Last checked: just now
Known limitations
- Memory use increases with context length and concurrency.