DeepSeek

Miles recipes for the DeepSeek family — V4 Flash, V4 Pro, and V3.2.

Miles ships recipes for the DeepSeek family across two generations. DeepSeek-V4 pairs sparse multi-head latent attention with a learned indexer, KV compressors, and hyper-connection routing. DeepSeek-V3.2 keeps the V3 MoE and MLA shapes and adds DeepSeek Sparse Attention (DSA), the same attention implementation the GLM-5 recipes use. DeepSeek-V3 itself remains available through scripts/run_deepseek.py.

Variants

Model	Active / Total	HF ID	Recipe
DeepSeek-V4-Pro	49 B / 1.6 T	TBA	deepseek-v4-pro
DeepSeek-V4-Flash	13 B / 284 B	sgl-project/DeepSeek-V4-Flash-FP8	deepseek-v4-flash
DeepSeek-V3.2	37 B / 671 B	deepseek-ai/DeepSeek-V3.2	deepseek-v3-2
DeepSeek-V3	37 B / 671 B	deepseek-ai/DeepSeek-V3	deepseek

A validated DeepSeek-V4-Pro recipe is not yet available — see radixark/miles#1046 for tracking.

Fastest path to train

DeepSeek-V4-Flash needs 8 nodes of 8× H200 and the radixark/miles:latest image:

cd /root/miles
python scripts/run_deepseek_v4.py full-train \
   --model-name DeepSeek-V4-Flash-FP8 \
   --num-nodes 8 --num-gpus-per-node 8

DeepSeek-V3.2 needs 8 training nodes of 8 GPUs plus separate rollout GPUs:

cd /root/miles
python scripts/run_deepseek_v32.py full-train \
   --actor-num-nodes 8 --rollout-num-gpus 8

See the DeepSeek-V4 Flash page for the V4 architecture summary, parallelism layouts, and known workarounds. See the DeepSeek-V3.2 page for the V3.2 flow — FP8 → BF16 conversion, the TP2 / PP4 / EP16 training layout, the NSA rollout settings, and the FP8 / MXFP8 options; see the DeepSeek V3 page for the V3 recipe.

Pairs well with

• Low Precision RL — both generations ship FP8 checkpoints and optional low-precision rollout.
• P2P Weight Transfer — amortize weight sync across ranks.
• Fault Tolerance — node failures are inevitable at 8-node scale and above.