Updated 3 h ago · first seen 11 Sept 2026
model_01M296QTDT4HJPWMMAAZ4M4VQD
- Parameters
- 3.8B
- T2 · 3 h ago
- Released
- 11 Aug 2026
- T2 · 3 h ago
Specification
- Release date
- 11 Aug 2026
Source:Microsoft Research — publications & blogT2observed 3 h agomediumLLM-extracted
- Status
- announced
Source:Microsoft Research — publications & blogT2observed 3 h agomediumLLM-extracted
- Openness
- proprietary
Source:Microsoft Research — publications & blogT2observed 3 h agomediumLLM-extracted
- Architecture
- SigLIP2-so400M vision encoder and Phi-4-mini-instruct (3.8B) language model connected through a lightweight adapter, with task-specific auxiliary heads for classification and visual grounding
Source:Microsoft Research — publications & blogT2observed 3 h agomediumLLM-extracted
- Parameters
- 3.8B
Source:Microsoft Research — publications & blogT2observed 3 h agomediumLLM-extracted
- Modalities
- image, text
Source:Microsoft Research — publications & blogT2observed 3 h agomediumLLM-extracted
- Input modalities
- image, text
Source:Microsoft Research — publications & blogT2observed 3 h agomediumLLM-extracted
- Output modalities
- text
Source:Microsoft Research — publications & blogT2observed 3 h agomediumLLM-extracted
Each value shows its source, tier and observation time. Conflicting claims are kept side by side and flagged — never averaged. How AI Atlas records facts →
Provenance
Attributed facts
15
Source tiers
T215
Freshest observation
3 h ago
Conflicts
None
Modalities
- Modalities
- imagetext
- Input
- imagetext
- Output
- text
Capabilities
Tool calling
Yes
Microsoft Research — publications & blog · T2
Structured output
Yes
Microsoft Research — publications & blog · T2
Reasoning
Yes
Microsoft Research — publications & blog · T2
Vision
Yes
Microsoft Research — publications & blog · T2
Audio
No
Microsoft Research — publications & blog · T2
Fine-tuning available
Unavailable
No structured attributes yet.
No benchmark results recorded
Current prices
No current prices recorded
Price history
Memory need = bytes per parameter (4-bit ≈ 0.5 × 1.15 overhead, 8-bit 1.0, fp16 2.0) + a KV-cache allowance. Not a measurement.
| Hardware | Quantization | Memory | Est. need | Fits |
|---|---|---|---|---|
| NVIDIA DGX B200 | 4bit | 1,440 GB | 2.7 GB | Yes |
| Apple M3 Ultra | 4bit | — | 2.7 GB | Yes |
| Mac Studio (Apple M5 Ultra) | 4bit | — | 2.7 GB | Yes |
| AMD Instinct MI325X | 4bit | 256 GB | 2.7 GB | Yes |
| AMD Instinct MI300X | 4bit | 192 GB | 2.7 GB | Yes |
| Apple M2 Ultra | 4bit | — | 2.7 GB | Yes |
| NVIDIA B200 | 4bit | 180 GB | 2.7 GB | Yes |
| NVIDIA H200 | 4bit | 141 GB | 2.7 GB | Yes |
| NVIDIA H200 NVL | 4bit | 141 GB | 2.7 GB | Yes |
| Apple M1 Ultra | 4bit | — | 2.7 GB | Yes |
| Apple M3 Max | 4bit | — | 2.7 GB | Yes |
| Apple M4 Max | 4bit | — | 2.7 GB | Yes |
| Mac Studio (Apple M5 Max) | 4bit | — | 2.7 GB | Yes |
| MacBook Pro (Apple M5 Max) | 4bit | — | 2.7 GB | Yes |
| NVIDIA DGX Spark | 4bit | 128 GB | 2.7 GB | Yes |
| Apple M2 Max | 4bit | — | 2.7 GB | Yes |
| NVIDIA H100 NVL | 4bit | 94 GB | 2.7 GB | Yes |
| NVIDIA A100 80GB | 4bit | 80 GB | 2.7 GB | Yes |
| NVIDIA H100 SXM | 4bit | 80 GB | 2.7 GB | Yes |
| Apple M1 Max | 4bit | — | 2.7 GB | Yes |
| Apple M4 Pro | 4bit | — | 2.7 GB | Yes |
| Mac mini (Apple M5 Pro) | 4bit | — | 2.7 GB | Yes |
| MacBook Pro (Apple M5 Pro) | 4bit | — | 2.7 GB | Yes |
| Apple M3 Pro | 4bit | — | 2.7 GB | Yes |
| Apple M1 Pro | 4bit | — | 2.7 GB | Yes |
| Apple M2 Pro | 4bit | — | 2.7 GB | Yes |
| Apple M4 | 4bit | — | 2.7 GB | Yes |
| iMac (Apple M4) | 4bit | — | 2.7 GB | Yes |
| Mac mini (Apple M6) | 4bit | — | 2.7 GB | Yes |
| MacBook Air (Apple M5) | 4bit | — | 2.7 GB | Yes |
| MacBook Pro (Apple M5) | 4bit | — | 2.7 GB | Yes |
| NVIDIA GeForce RTX 5090 | 4bit | 32 GB | 2.7 GB | Yes |
| Apple M2 | 4bit | — | 2.7 GB | Yes |
| Apple M3 | 4bit | — | 2.7 GB | Yes |
| NVIDIA GeForce RTX 3090 | 4bit | 24 GB | 2.7 GB | Yes |
| NVIDIA GeForce RTX 4090 | 4bit | 24 GB | 2.7 GB | Yes |
| Apple M1 | 4bit | — | 2.7 GB | Yes |
Ancestors 1
This model
CARE-X
3.8B params
Quantizations 0
None recorded.
Descendants 0
None recorded.
Papers 0
No papers linked yet.
Repositories 0
No repositories linked yet.
As of
Rewind the record: see this entity's attributes exactly as AI Atlas knew them on a given day.
Claim history · Training data notes
Training data notestraining_data_notes1
| Value | Valid from → to | Status | Source | Confidence | Extractor |
|---|---|---|---|---|---|
| Trained on real-world Indian clinical data from Narayana Health, including rare ICU pathologies and CT-confirmed enlargement conditions. Used de-identified, retrospective clinical data under applicable institutional ethics review. | → current | current | Microsoft Research — publications & blogT2 | medium | llm |
Claims are temporal and append-only: a new observation closes the previous claim (valid_to) instead of overwriting it. Conflicting claims from different sources are kept side by side and flagged — never averaged. Methodology →
| Source | Document | Type | Tier | Last observed | Snapshots |
|---|---|---|---|---|---|
| Microsoft Research — publications & blog | microsoft.com/en-us/research/blog/introducing-care-x-towards-clinically-useful-radiology-vlms-with-auxiliary-supervision-reward-aligned-learning-and-tool-augmented-measurement | news | T1· Official | 3 h ago | 1 |
Tier 1 = official/primary, 2 = quality secondary, 3 = community, 4 = unverified. Every snapshot is archived; see all sources and the methodology.