We have just released embedl/Cosmos-Reason2-2B-W4A16-Edge2, a new mixed-precision variant of Cosmos Reason 2 that recovers virtually the full baseline reasoning accuracy while preserving edge deployment efficiency.
After our earlier Cosmos Reason 2 releases (W4A16 and NVFP4A16), this is the variant teams have been asking for: edge-efficient deployment without giving up reasoning quality.
A recent writeup summarized the prevailing view: full-precision Cosmos models still require AGX Thor or DGX-class hardware, and quantization improves accessibility but introduces accuracy trade-offs. It remains an open question whether the accuracy trade-off is fundamental or a function of how quantization is applied.
Embedl's W4A16-Edge2 is the answer to that question. This model has the accuracy of the original higher-precision model while being as efficient as the fully quantized model.
On the official Physical AI Bench Reason Task (https://huggingface.co/spaces/shi-labs/physical-ai-bench-leaderboard):
|
Model |
Overall |
|
nvidia/Cosmos-Reason2-2B (unquantized) |
50.60 |
|
embedl/Cosmos-Reason2-2B-W4A16 |
48.68 |
|
embedl/Cosmos-Reason2-2B-W4A16-Edge2 |
50.58 |
W4A16-Edge2 brings quantization performance in line with the unquantized baseline, effectively closing the accuracy gap. In contrast, our earlier W4A16 variant showed an approximate two-point drop, which could be potentially impactful in certain deployment scenarios. With W4A16-Edge2, we achieve baseline-level accuracy while still meeting strict edge memory and latency requirements.
Embedl’s W4A16-Edge2 is a targeted mixed-precision recipe built on top of W4A16. The approach uses aggressive 4-bit quantization, which the model is robust to, while retaining FP16 precision on sensitive layers where it impacts reasoning fidelity.
Finding the right precision allocation - which layers are sensitive and which aren't - is where Embedl's proprietary optimization algorithms come in. When executed correctly, you can recover benchmark-level quality without sacrificing the memory and latency efficiencies that enable edge deployment.
We benchmarked W4A16-Edge2 on Jetson hardware across text, image, and video workloads (batch size 1, 1280×720 representative paths).
NVIDIA Jetson AGX Orin
NVIDIA Jetson Orin Nano Super
On Jetson Orin Nano Super, the base Cosmos Reason 2 model runs out of memory, while W4A16-Edge2 runs comfortably across all modalities. On AGX Orin, W4A16-Edge2 roughly halves end-to-end latency across the board, turning Cosmos Reason 2 into something you can actually deploy at the edge with responsive, real-time behavior.
You can find our detailed benchmarks in the model card: https://huggingface.co/embedl/Cosmos-Reason2-2B-W4A16-Edge2
Physical AI is moving fast from prototypes to deployed systems. The adoption signal is clear across robotics and autonomy ecosystems. At that stage, the deployment question becomes practical: can it run on the target hardware, and can it maintain the required reasoning quality?
W4A16-Edge2 answers both with hard numbers.
You can try the new W4A16-Edge2 model here:
https://huggingface.co/embedl/Cosmos-Reason2-2B-W4A16-Edge2
If you're not on Blackwell, W4A16-Edge2 is now the most accurate and edge-efficient option in the Cosmos Reason 2 lineup. For Blackwell deployments, our NVFP4A16 variant remains available, and W4A16 remains a strong option for maximal memory compression.
If you're deploying Cosmos Reason 2 at the edge and want support with model selection and optimization for your hardware profile, contact us at sales@embedl.com.