diff --git a/jax/_src/pallas/mosaic/core.py b/jax/_src/pallas/mosaic/core.py
index 3407fe2e3..82fe9c2ba 100644
--- a/jax/_src/pallas/mosaic/core.py
+++ b/jax/_src/pallas/mosaic/core.py
@@ -270,6 +270,7 @@ def _tensorcore_mesh_discharge_rule(
       compiler_params=dict(
           mosaic=dict(dimension_semantics=("parallel",)),
       ),
+      backend="mosaic_tpu",
   )(*args)
   return out, ()
 
diff --git a/jax/_src/pallas/mosaic_gpu/core.py b/jax/_src/pallas/mosaic_gpu/core.py
index c4e785e50..2ed8910bf 100644
--- a/jax/_src/pallas/mosaic_gpu/core.py
+++ b/jax/_src/pallas/mosaic_gpu/core.py
@@ -512,6 +512,7 @@ def _gpu_mesh_discharge_rule(
       out_specs=[any_spec] * len(in_avals),
       input_output_aliases={i: i for i in range(len(in_avals))},
       grid=tuple(mesh.shape.items()),
+      backend="mosaic_gpu",
   )(*args)
   return out, ()
 
diff --git a/jax/_src/pallas/pallas_call.py b/jax/_src/pallas/pallas_call.py
index 4db6057d2..2bed4a083 100644
--- a/jax/_src/pallas/pallas_call.py
+++ b/jax/_src/pallas/pallas_call.py
@@ -19,7 +19,7 @@ from collections.abc import Callable, Iterable, Sequence
 import dataclasses
 from functools import partial, reduce
 import itertools
-from typing import Any
+from typing import Any, Literal
 
 import jax
 from jax import lax
@@ -362,6 +362,7 @@ def _pallas_call_jvp_rule(
     compiler_params: Any,
     cost_estimate: CostEstimate | None,
     out_avals: tuple[jax_core.AbstractValue, ...],
+    backend: _Backend | None,
 ):
   if grid_mapping.num_dynamic_grid_bounds:
     raise NotImplementedError("interpret with dynamic grid bounds unsupported")
@@ -425,7 +426,8 @@ def _pallas_call_jvp_rule(
       input_output_aliases=(),
       compiler_params=compiler_params,
       cost_estimate=jvp_cost_estimate,
-      out_avals=(*out_avals, *out_avals)
+      out_avals=(*out_avals, *out_avals),
+      backend=backend,
   )
   out_primals, out_tangents = split_list(out_flat, [len(out_flat) // 2])
   return out_primals, out_tangents
@@ -560,6 +562,7 @@ def _batch_with_explicit_loop(
     compiler_params: Any,
     cost_estimate: CostEstimate | None,
     out_avals: tuple[jax_core.AbstractValue, ...],
+    backend: _Backend | None,
 ):
   """Batch the pallas_call by calling it in loop over the batch size.
 
@@ -627,6 +630,7 @@ def _batch_with_explicit_loop(
         compiler_params=compiler_params,
         cost_estimate=cost_estimate,
         out_avals=out_avals,
+        backend=backend,
     )
     for i, batch_out_array in enumerate(batch_out):
       state[i] = jax.lax.dynamic_update_index_in_dim(
@@ -656,6 +660,7 @@ def _pallas_call_batching_rule(
     compiler_params: Any,
     cost_estimate: CostEstimate | None,
     out_avals: tuple[jax_core.AbstractValue, ...],
+    backend: _Backend | None,
 ):
   def _maybe_squeeze_out_bdim(
       x: jax.Array, bdim: int | batching.NotMapped
@@ -688,6 +693,7 @@ def _pallas_call_batching_rule(
         compiler_params=compiler_params,
         cost_estimate=cost_estimate,
         out_avals=out_avals,
+        backend=backend,
     )
     return [jnp.expand_dims(x, 0) for x in out], (0,) * len(out)
 
@@ -721,6 +727,7 @@ def _pallas_call_batching_rule(
         compiler_params=compiler_params,
         cost_estimate=cost_estimate,
         out_avals=out_avals,
+        backend=backend,
     )
   else:
     pass  # No dynamic grid dimensions
@@ -755,6 +762,7 @@ def _pallas_call_batching_rule(
           compiler_params=compiler_params,
           cost_estimate=cost_estimate,
           out_avals=out_avals,
+          backend=backend,
       )
 
   if not dims:
@@ -1128,6 +1136,7 @@ def _pallas_call_batching_rule(
       compiler_params=compiler_params,
       cost_estimate=batched_cost_estimate,
       out_avals=batched_out_avals,
+      backend=backend,
   )
   return out, (0,) * len(out)
 
@@ -1441,9 +1450,15 @@ def _unsupported_lowering_error(platform: str) -> Exception:
       " https://jax.readthedocs.io/en/latest/installation.html."
   )
 
+_Backend = Literal["mosaic_tpu", "triton", "mosaic_gpu"]
+
 
 def _pallas_call_lowering(
-    ctx: mlir.LoweringRuleContext, *in_nodes, interpret: bool, **params
+    ctx: mlir.LoweringRuleContext,
+    *in_nodes,
+    interpret: bool,
+    backend: _Backend | None,
+    **params,
 ):
   if params['jaxpr'].constvars:
     raise ValueError('Cannot lower a pallas_call with constants.')
@@ -1460,6 +1475,8 @@ def _pallas_call_lowering(
   def tpu_lowering(ctx: mlir.LoweringRuleContext,
                    *in_nodes: mlir.ir.Value | Sequence[mlir.ir.Value],
                    **params):
+    if backend and backend != "mosaic_tpu":
+      raise ValueError("Only mosaic backend supported for TPU")
     if mosaic_tpu_backend is None:
       raise _unsupported_lowering_error("tpu")
     return mosaic_tpu_backend.pallas_call_tpu_lowering_rule(
@@ -1470,12 +1487,21 @@ def _pallas_call_lowering(
                    *in_nodes: mlir.ir.Value | Sequence[mlir.ir.Value],
                    **params):
     try:
-      if _PALLAS_USE_MOSAIC_GPU.value:
-        from jax._src.pallas.mosaic_gpu import pallas_call_registration
-      else:
-        from jax._src.pallas.triton import pallas_call_registration  # type: ignore
-    except ImportError:
+      match backend:
+        case "mosaic_gpu":
+          from jax._src.pallas.mosaic_gpu import pallas_call_registration
+        case "triton":
+          from jax._src.pallas.triton import pallas_call_registration  # type: ignore
+        case None:
+          if _PALLAS_USE_MOSAIC_GPU.value:
+            from jax._src.pallas.mosaic_gpu import pallas_call_registration
+          else:
+            from jax._src.pallas.triton import pallas_call_registration  # type: ignore
+        case _:
+          raise ValueError(f"Unsupported backend: {backend}")
+    except ImportError as e:
       raise _unsupported_lowering_error("gpu")
+
     return pallas_call_registration.pallas_call_lowering(
         ctx, *in_nodes, **params
     )
@@ -1544,6 +1570,7 @@ def _pallas_call_state_discharge_rule(
     compiler_params: Any,
     cost_estimate: CostEstimate | None,
     out_avals: tuple[jax_core.AbstractValue, ...],
+    backend: _Backend | None = None
 ):
   del avals_out
   assert all(isinstance(v.aval, state.AbstractRef) for v in jaxpr.constvars)
@@ -1645,6 +1672,7 @@ def _pallas_call_state_discharge_rule(
       compiler_params=compiler_params,
       cost_estimate=cost_estimate,
       out_avals=new_out_avals,
+      backend=backend,
   )
   refs_out, rest = split_list(out_flat, [num_refs])
   updated_vals_in = refs_out + [None] * len(rest_in_avals)
@@ -1666,6 +1694,7 @@ def pallas_call(
     name: str | None = None,
     compiler_params: dict[str, Any] | pallas_core.CompilerParams | None = None,
     cost_estimate: CostEstimate | None = None,
+    backend: _Backend | None = None,
 ) -> Callable[..., Any]:
   """Invokes a Pallas kernel on some inputs.
 
@@ -1715,6 +1744,8 @@ def pallas_call(
       platform is either 'mosaic' or 'triton'. It is also possible
       to pass in `jax.experimental.pallas.tpu.TPUCompilerParams` for TPUs and
       `jax.experimental.pallas.gpu.TritonCompilerParams` for Triton/GPUs.
+    backend: Optional string literal one of  "mosaic_tpu", "triton" or "mosaic_gpu"
+      determining the backend to be used. None means let pallas decide.
 
 
   Returns:
@@ -1857,6 +1888,7 @@ def pallas_call(
           input_output_aliases=tuple(input_output_aliases.items()),
           compiler_params=compiler_params,
           cost_estimate=cost_estimate,
+          backend=backend,
       )
     out = tree_util.tree_unflatten(out_tree, out_flat)
     return out