src-e12fc30/docs/reference/env.md

Environment variables

Both engines

Variable	Default	Use
NSLM_MODEL_DIR		The model folder for the tests (Windows build)

Metal engine

Variable	Default	Use
NSLM_KV_DENSE=1	off	Commit all KV cache memory at the start. The default pages the cache (macOS 26.4 or later)
NSLM_BATCH_GEMM=N	off	Run decode steps of N or more slots through the prompt GEMMs. Faster at large batches. Gives up bit-for-bit equality

CUDA engine

Variable	Default	Use
NSLM_VRAM_RESERVE_MB	512	VRAM to keep free
NSLM_EXPERT_MIN_MB	6144	VRAM to keep for experts when the KV cache is large
NSLM_KV_VRAM_MB, NSLM_EXPERT_VRAM_MB		Fixed VRAM shares for the KV cache and the experts
NSLM_CACHE_STATS=1	off	Write the expert cache hits at exit
NSLM_NO_GRAPH=1	off	Do not use CUDA graphs for decode (K2-Horizon-MoVA and Qwen3.8-Flash-Next)
NSLM_NO_PREDICT=1	off	Do not predict the experts of the next layer during decode (K2-Horizon-MoVA)
Q4E_PROFILE=1	off	Qwen3.8-Flash-Next: write the GPU time of each forward stage at exit
Q4E_ROUTES=FILE	off	Qwen3.8-Flash-Next: write the routed experts of each forward, for tools/qwen4_cache_sim.py
NSLM_SEED_GEMM_F32=1	off	Keep seed weights in f32 in the prompt GEMM. This changed neither KLD nor NLL, and it costs about 30% of prompt speed at 4K
NSLM_PREFILL_RANGE	8192, or 32768 when the KV cache is partly in host memory	Prompt rows that the engine computes layer by layer at a time. Each range copies the earlier host rows again
NSLM_MLA_DEC_KEYS	8192 (128 MiB of VRAM)	MLA keys that the engine decompresses at a time for prompt rows. 0 attends prompts absorbed, as decode does
NSLM_MLA_SCALAR=1	off	Use the earlier one-pass latent kernel, for comparison

MLX loader

Variable	Default	Use
NSLM_MAX_CONTEXT		The longest context (tokens) that you plan to use. The loader picks the MLA cache format from it
OMLX_K2_MODEL		The oMLX K2-Horizon model file, for make test-mlx and the MLX tools