perf: decouple the Engram buffer size from max_num_batched_tokens

The number of RDMA QPs DeepEP allocates for the Engram buffer is derived
from its max_num_tokens, which in turn comes from max_num_batched_tokens
(model_loader.py). Raising the QP count therefore required raising the
global batched-token budget, which also inflates the EP/MoE/CP buffers.

Add DEEPINFER_ENGRAM_MIN_PREFILLING_TOKENS as a lower bound applied only
to the Engram buffer, so a deployment can give Engram more QPs without
resizing the other buffers. Default 0 keeps the current behavior.

deepinfer/environ.py

@@ -153,6 +153,19 @@ class envs:
     DEEPINFER_ENGRAM_PAD_TOKEN = EnvVar.int(default=2)
     DEEPINFER_ENGRAM_FORCE_GPU_STORAGE = EnvVar(lambda v: _bool(v) or envs.DEEPINFER_ACCELERATOR == "ascend")
 
+    @EnvVar
+    def DEEPINFER_ENGRAM_MIN_PREFILLING_TOKENS(v: str | None) -> int:
+        """Lower bound of the Engram buffer's prefill max_num_tokens (per CP rank, 0 disables it).
+
+        It only raises the Engram buffer (and therefore the number of RDMA QPs DeepEP allocates for it),
+        leaving the EP / MoE / CP buffers untouched. Same unit as max_num_prefilling_tokens, i.e. a
+        per-rank value already divided by the context parallel size.
+        """
+        tokens = _int(v)
+        if tokens < 0:
+            raise ValueError(f"expected a non-negative number of tokens, got {tokens}")
+        return tokens
+
     # -- Sampling --
     DEEPINFER_SAMPLER_FORCE_TOPK = EnvVar.int()
     DEEPINFER_DECODE_TOKEN_BLACKLIST = EnvVar.str()

deepinfer/model_executor/models/v4_modules/engram.py

@@ -91,7 +91,11 @@ class NgramEmbedding(nn.Module):
         assert self.weight.shape[0] == self.num_local_embeddings, (self.weight.shape, self.num_local_embeddings)
 
         self.num_entries_per_token = len(source_embedding.head_vocab_sizes)
-        self.max_num_tokens = cfg.max_num_decoding_tokens if cfg.engine_type == EngineType.COMPLETION else cfg.max_num_prefilling_tokens
+        self.max_num_tokens = (
+            cfg.max_num_decoding_tokens
+            if cfg.engine_type == EngineType.COMPLETION
+            else max(cfg.max_num_prefilling_tokens, envs.DEEPINFER_ENGRAM_MIN_PREFILLING_TOKENS)
+        )
 
     def gather_sf(self) -> torch.Tensor:
         assert self.sf is not None