README.md

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title: Canada Quant Labs
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# Canada Quant Labs

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.

**What we do**
- Post-training on open base models (SFT, DPO, GRPO, RLAIF)
- Production quantization recipes (W4A16, NVFP4, MXFP4)
- Audited, air-gapped deployment with eval evidence and MRM docs

**Where we work**
- Legal · Medical · Defence · Finance
- Headquarters: Victoria, BC
- Compute: NVIDIA DGX B300 at Equinix Vancouver

**Upstream**
- Contributors to vLLM, llm-compressor, compressed-tensors

Partnerships · partnerships@cql.ca
Press · press@cql.ca
Web · [cql.ca](https://cql.ca)

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## Open releases — DeepSeek-V4 quantization family

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](https://huggingface.co/canada-quant/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](https://huggingface.co/canada-quant/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](https://huggingface.co/canada-quant/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](https://huggingface.co/canada-quant/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`](https://huggingface.co/RedHatAI/DeepSeek-V4-Flash-NVFP4-FP8) (Flash NVFP4 topology) and [`nvidia/DeepSeek-V3.2-NVFP4`](https://huggingface.co/nvidia/DeepSeek-V3.2-NVFP4) (Pro NVFP4, MTP-exclusion topology).

### Hardware shorthand

- **H200** — 8× NVIDIA H200 SXM5 (Hopper SM 9.0a, 141 GB HBM3e/GPU)
- **DGX Spark** — 2× NVIDIA DGX Spark (GB10, Blackwell SM 12.1a)
- **RTX PRO 6000** — NVIDIA RTX PRO 6000 Blackwell Server Edition (SM 12.0, sm_120, 96 GB HBM)
- **B300** — NVIDIA B300 SXM6 AC (Blackwell SM 10.3, sm_103a, 288 GB HBM3e/GPU)

### Reproduction repos

Every artifact has a public reproduction repo with calibration scripts, vLLM patches, bench harnesses, and findings docs:

- [`canada-quant/dsv4-flash-w4a16-fp8`](https://github.com/canada-quant/dsv4-flash-w4a16-fp8)
- [`canada-quant/dsv4-flash-w4a16-fp8-mtp`](https://github.com/canada-quant/dsv4-flash-w4a16-fp8-mtp)
- [`canada-quant/dsv4-flash-nvfp4-fp8-mtp`](https://github.com/canada-quant/dsv4-flash-nvfp4-fp8-mtp)
- [`canada-quant/dsv4-pro-nvfp4-fp8-mtp`](https://github.com/canada-quant/dsv4-pro-nvfp4-fp8-mtp)

### Upstream contributions filed during this work

- vLLM: PRs [#42209](https://github.com/vllm-project/vllm/pull/42209) (merged — NVFP4 MoE for DSV4), [#43248](https://github.com/vllm-project/vllm/pull/43248), [#43288](https://github.com/vllm-project/vllm/pull/43288), [#43290](https://github.com/vllm-project/vllm/pull/43290), [#43319](https://github.com/vllm-project/vllm/pull/43319), [#43467](https://github.com/vllm-project/vllm/pull/43467), [#41511](https://github.com/vllm-project/vllm/issues/41511), [#41700](https://github.com/vllm-project/vllm/issues/41700) (landed via `jasl/vllm@1d6f5c4`)
- llm-compressor: [#2745](https://github.com/vllm-project/llm-compressor/issues/2745)
- compressed-tensors: [#711](https://github.com/vllm-project/compressed-tensors/issues/711)