Canada's open-weight model lab.
We train, quantize, and deploy sovereign AI models on Canadian Blackwell silicon — for the regulated industries that can't run on someone else's API.
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Upstream
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Four artifacts in the same lineage. One base model in two sizes (V4-Flash, V4-Pro); two routed-expert formats (W4A16, NVFP4); Multi-Token Prediction (MTP) draft head retained on three of four. Attention is FP8 block 128×128 across all four.
| Model | Base | Routed experts | MTP | On-disk | Min hardware (TP=2) | When to pick |
|---|---|---|---|---|---|---|
| DeepSeek-V4-Flash-W4A16-FP8 | V4-Flash | W4A16 INT4 g=128 | no | ~143 GB | H200 / DGX Spark / RTX PRO 6000 | maximum compatibility, no MTP needed |
| DeepSeek-V4-Flash-W4A16-FP8-MTP | V4-Flash | W4A16 INT4 g=128 | yes (BF16) | 159 GB | H200 / RTX PRO 6000 | best $/token interactive on V4-Flash |
| DeepSeek-V4-Flash-NVFP4-FP8-MTP | V4-Flash | NVFP4 g=16 | yes (BF16) | 172 GB | RTX PRO 6000 / B300 | best Blackwell-native interactive on V4-Flash |
| DeepSeek-V4-Pro-NVFP4-FP8-MTP | V4-Pro | NVFP4 g=16 | yes (byte-identical) | 913 GiB | 8× B300 (TP=8 + EP) | only choice for V4-Pro deployment; +25–37% throughput vs upstream MXFP4 |
Upstream reference recipes: RedHatAI/DeepSeek-V4-Flash-NVFP4-FP8 (Flash NVFP4 topology) and nvidia/DeepSeek-V3.2-NVFP4 (Pro NVFP4, MTP-exclusion topology).
Every artifact has a public reproduction repo with calibration scripts, vLLM patches, bench harnesses, and findings docs:
canada-quant/dsv4-flash-w4a16-fp8canada-quant/dsv4-flash-w4a16-fp8-mtpcanada-quant/dsv4-flash-nvfp4-fp8-mtpcanada-quant/dsv4-pro-nvfp4-fp8-mtp