diff --git a/mlx_lm/server.py b/mlx_lm/server.py
index 9462d6e..36f7787 100644
--- a/mlx_lm/server.py
+++ b/mlx_lm/server.py
@@ -29,6 +29,7 @@ from typing import (
 
 import mlx.core as mx
 from huggingface_hub import scan_cache_dir
+from mlx.utils import tree_flatten
 
 from ._version import __version__
 from .generate import (
@@ -428,6 +429,26 @@ def _format_top_logprobs(logprobs, top_n, tokenizer) -> Tuple[Dict[str, Any]]:
     )
 
 
+def _prompt_cache_byte_limit(cli_args, model):
+    limit = getattr(cli_args, "prompt_cache_bytes", None)
+    if limit is not None:
+        if limit < 0:
+            raise ValueError("--prompt-cache-bytes must be non-negative")
+        return limit
+
+    # Reserve workspace and half the remaining capacity for the active request.
+    try:
+        recommended = mx.device_info().get("max_recommended_working_set_size")
+    except (RuntimeError, ValueError):
+        recommended = None
+    if not recommended:
+        return 1 << 63
+    parameters = {id(p): p for _, p in tree_flatten(model.parameters())}
+    model_bytes = sum(p.nbytes for p in parameters.values())
+    reserve = max(4 * 1024**3, recommended // 8)
+    return max(0, (recommended - model_bytes - reserve) // 2)
+
+
 class ResponseGenerator:
     def __init__(self, model_provider: ModelProvider, prompt_cache: LRUPromptCache):
         self.model_provider = model_provider
@@ -443,6 +464,13 @@ class ResponseGenerator:
         self._generation_thread = Thread(target=self._run_generate)
         self._generation_thread.start()
 
+    def _configure_prompt_cache(self, model):
+        limit = _prompt_cache_byte_limit(self.cli_args, model)
+        if self.prompt_cache.max_bytes != limit:
+            self.prompt_cache.max_bytes = limit
+            self.prompt_cache.trim_to(n_bytes=limit)
+            logging.info("Retained prompt-cache limit: %.2f GB", limit / 1e9)
+
     def _run_generate(self):
         try:
             self._generate()
@@ -772,6 +800,7 @@ class ResponseGenerator:
                         model, tokenizer = self.model_provider.load(
                             args.model.model, args.model.adapter, args.model.draft
                         )
+                        self._configure_prompt_cache(model)
                     except Exception as e:
                         rqueue.put(e)
                         continue
@@ -1762,7 +1791,13 @@ def run(
     handler_class=APIHandler,
 ):
     group = mx.distributed.init()
-    prompt_cache = LRUPromptCache(model_provider.cli_args.prompt_cache_size)
+    cache_bytes = model_provider.cli_args.prompt_cache_bytes
+    if cache_bytes is not None and cache_bytes < 0:
+        raise ValueError("--prompt-cache-bytes must be non-negative")
+    prompt_cache = LRUPromptCache(
+        model_provider.cli_args.prompt_cache_size,
+        max_bytes=cache_bytes if cache_bytes is not None else 1 << 63,
+    )
     response_generator = ResponseGenerator(model_provider, prompt_cache)
     if group.rank() == 0:
         _run_http_server(host, port, response_generator)
@@ -1875,31 +1910,32 @@ def main():
     parser.add_argument(
         "--decode-concurrency",
         type=int,
-        default=32,
+        default=1,
         help="When a request is batchable then decode that many requests in parallel",
     )
     parser.add_argument(
         "--prompt-concurrency",
         type=int,
-        default=8,
+        default=1,
         help="When a request is batchable then process that many prompts in parallel",
     )
     parser.add_argument(
         "--prefill-step-size",
         type=int,
-        default=2048,
-        help="Step size for prefill processing (default: 2048)",
+        default=512,
+        help="Step size for prefill processing (default: 512)",
     )
     parser.add_argument(
         "--prompt-cache-size",
         type=int,
-        default=10,
-        help="Maximum number of distinct KV caches to hold in the prompt cache",
+        default=2,
+        help="Maximum retained prompt-cache entries (default: 2)",
     )
     parser.add_argument(
         "--prompt-cache-bytes",
         type=_parse_size,
-        help="Maximum size in bytes of the KV caches",
+        help="Maximum retained prompt-cache bytes. Default: automatic on Metal. "
+        "This does not cap active-request or total process memory.",
     )
     parser.add_argument(
         "--kv-bits",
diff --git a/tests/test_server_cache_budget.py b/tests/test_server_cache_budget.py
new file mode 100644
--- /dev/null
+++ b/tests/test_server_cache_budget.py
@@ -0,0 +1,132 @@
+# Copyright © 2026 Swift contributors.
+
+import sys
+from types import SimpleNamespace
+from unittest.mock import Mock
+
+import pytest
+
+from mlx_lm import server
+from mlx_lm.models.cache import LRUPromptCache
+
+
+class CacheState:
+    def __init__(self, nbytes):
+        self.nbytes = nbytes
+
+    def is_trimmable(self):
+        return False
+
+
+@pytest.mark.parametrize("limit", [0, 100])
+def test_server_factory_enforces_configured_bytes(monkeypatch, limit):
+    caches = []
+    provider = SimpleNamespace(
+        cli_args=SimpleNamespace(prompt_cache_size=10, prompt_cache_bytes=limit)
+    )
+    monkeypatch.setattr(
+        server, "ResponseGenerator", lambda provider, cache: caches.append(cache)
+    )
+    monkeypatch.setattr(server, "_run_http_server", lambda *args: None)
+    server.run("127.0.0.1", 0, provider)
+    cache = caches[0]
+    for i in range(5):
+        cache.insert_cache("model", [i, 1], [CacheState(80)])
+        assert cache.nbytes <= limit
+    if limit:
+        reused, remaining = cache.fetch_nearest_cache("model", [4, 1, 9])
+        assert reused is not None
+        assert remaining == [9]
+
+
+def test_negative_limit_rejected_before_worker_start(monkeypatch):
+    worker = Mock()
+    monkeypatch.setattr(server, "ResponseGenerator", worker)
+    provider = SimpleNamespace(
+        cli_args=SimpleNamespace(prompt_cache_size=2, prompt_cache_bytes=-1)
+    )
+    with pytest.raises(ValueError, match="non-negative"):
+        server.run("127.0.0.1", 0, provider)
+    worker.assert_not_called()
+
+
+def test_explicit_limit_does_not_probe_model(monkeypatch):
+    probe = Mock(side_effect=AssertionError("device probe was not needed"))
+    monkeypatch.setattr(server.mx, "device_info", probe)
+    model = Mock()
+    for limit in (0, 1024, 8 * 1024**3):
+        assert (
+            server._prompt_cache_byte_limit(
+                SimpleNamespace(prompt_cache_bytes=limit), model
+            )
+            == limit
+        )
+    model.parameters.assert_not_called()
+
+
+def test_auto_limit_reserves_room_for_active_request(monkeypatch):
+    gib = 1024**3
+    monkeypatch.setattr(
+        server.mx,
+        "device_info",
+        lambda: {"max_recommended_working_set_size": 36 * gib},
+    )
+    model = SimpleNamespace(parameters=lambda: {"weight": CacheState(15 * gib)})
+    args = SimpleNamespace(prompt_cache_bytes=None)
+    limit = server._prompt_cache_byte_limit(args, model)
+    assert 6 * gib <= limit < (36 - 15) * gib // 2
+    larger = SimpleNamespace(parameters=lambda: {"weight": CacheState(30 * gib)})
+    assert server._prompt_cache_byte_limit(args, larger) < limit
+    full = SimpleNamespace(parameters=lambda: {"weight": CacheState(36 * gib)})
+    assert server._prompt_cache_byte_limit(args, full) == 0
+
+
+def test_model_swap_trims_retained_states(monkeypatch):
+    obj = server.ResponseGenerator.__new__(server.ResponseGenerator)
+    obj.model_provider = SimpleNamespace(
+        cli_args=SimpleNamespace(prompt_cache_bytes=100)
+    )
+    obj.prompt_cache = LRUPromptCache(max_size=10)
+    obj.prompt_cache.insert_cache("old", [1], [CacheState(200)])
+    obj._configure_prompt_cache(Mock())
+    assert obj.prompt_cache.nbytes == 0
+    assert obj.prompt_cache.max_bytes == 100
+
+
+@pytest.mark.parametrize("device_info", [{}, {"max_recommended_working_set_size": 0}])
+def test_auto_limit_without_metal_metadata(monkeypatch, device_info):
+    monkeypatch.setattr(server.mx, "device_info", lambda: device_info)
+    model = Mock()
+    assert (
+        server._prompt_cache_byte_limit(SimpleNamespace(prompt_cache_bytes=None), model)
+        == 1 << 63
+    )
+    model.parameters.assert_not_called()
+
+
+def test_auto_limit_counts_tied_parameters_once(monkeypatch):
+    gib = 1024**3
+    monkeypatch.setattr(
+        server.mx,
+        "device_info",
+        lambda: {"max_recommended_working_set_size": 32 * gib},
+    )
+    weight = CacheState(8 * gib)
+    model = SimpleNamespace(parameters=lambda: {"embed": weight, "head": weight})
+    assert (
+        server._prompt_cache_byte_limit(SimpleNamespace(prompt_cache_bytes=None), model)
+        == 10 * gib
+    )
+
+
+def test_defaults_keep_reuse_enabled_and_limit_concurrency(monkeypatch):
+    captured = []
+    monkeypatch.setattr(sys, "argv", ["mlx_lm.server"])
+    monkeypatch.setattr(server, "maybe_set_recommended_wired_limit", lambda: None)
+    monkeypatch.setattr(server, "run", lambda h, p, m: captured.append(m.cli_args))
+    server.main()
+    args = captured[0]
+    assert args.prompt_cache_size == 2
+    assert args.prompt_cache_bytes is None
+    assert args.prompt_concurrency == args.decode_concurrency == 1
+    assert args.prefill_step_size == 512