diff --git a/tests/test_config.py b/tests/test_config.py
new file mode 100644
index 0000000..7a6fc61
--- /dev/null
+++ b/tests/test_config.py
@@ -0,0 +1,258 @@
+# SPDX-License-Identifier: Apache-2.0
+
+"""Unit tests for configuration classes: CacheConfig, ParallelConfig, SchedulerConfig.
+
+These tests cover:
+  - CacheConfig: gpu_memory_utilization bounds (upper + lower), block_size
+    storage, deferred fields.
+  - ParallelConfig: _verify_args assertions, data_parallel_size auto-derive
+    when 0 is passed, world_size computation.
+  - SchedulerConfig: max_num_batched_tokens vs. max_model_len check,
+    prefill interval lower bounds.
+
+No model weights, GPU, or CUDA device are required; all tests run on CPU.
+
+NOTE: test_cache_config_rejects_zero_utilization and
+test_cache_config_rejects_negative_utilization are currently marked xfail
+because the lower-bound guard has not yet been added to CacheConfig._verify_args.
+They will automatically flip to XPASS (and then be un-marked) once the companion
+fix `fix/cache-config-validation` is merged.
+"""
+
+from __future__ import annotations
+
+import pytest
+
+from deepinfer.config import CacheConfig, ParallelConfig, SchedulerConfig
+from deepinfer.engine.engine_type import EngineType
+
+
+# ---------------------------------------------------------------------------
+# CacheConfig
+# ---------------------------------------------------------------------------
+
+class TestCacheConfig:
+    def test_valid_construction(self) -> None:
+        cfg = CacheConfig(block_size=16, gpu_memory_utilization=0.9)
+        assert cfg.block_size == 16
+        assert cfg.gpu_memory_utilization == pytest.approx(0.9)
+
+    def test_boundary_utilization_one(self) -> None:
+        """Exactly 1.0 must be accepted (full GPU allocation)."""
+        cfg = CacheConfig(block_size=16, gpu_memory_utilization=1.0)
+        assert cfg.gpu_memory_utilization == pytest.approx(1.0)
+
+    def test_lookahead_tokens_default_zero(self) -> None:
+        cfg = CacheConfig(block_size=16, gpu_memory_utilization=0.5)
+        assert cfg.lookahead_tokens == 0
+
+    def test_lookahead_tokens_stored(self) -> None:
+        cfg = CacheConfig(block_size=16, gpu_memory_utilization=0.5, lookahead_tokens=4)
+        assert cfg.lookahead_tokens == 4
+
+    def test_deferred_fields_initially_none(self) -> None:
+        cfg = CacheConfig(block_size=16, gpu_memory_utilization=0.5)
+        assert cfg.num_gpu_blocks is None
+        assert cfg.num_state_seqs is None
+        assert cfg.bytes_of_state_per_seqs is None
+        assert cfg.bytes_per_block is None
+
+    def test_above_one_raises(self) -> None:
+        with pytest.raises(ValueError, match="GPU memory utilization"):
+            CacheConfig(block_size=16, gpu_memory_utilization=1.01)
+
+    def test_well_above_one_raises(self) -> None:
+        with pytest.raises(ValueError, match="GPU memory utilization"):
+            CacheConfig(block_size=16, gpu_memory_utilization=2.0)
+
+    @pytest.mark.xfail(
+        strict=True,
+        reason="Lower-bound guard missing in CacheConfig._verify_args; "
+               "fixed by PR fix/cache-config-validation.",
+    )
+    def test_zero_utilization_raises(self) -> None:
+        """gpu_memory_utilization=0.0 should be rejected — reserving zero GPU
+        memory for KV blocks makes the engine non-functional and produces a
+        cryptic division-by-zero deep in the memory profiler."""
+        with pytest.raises(ValueError, match="GPU memory utilization"):
+            CacheConfig(block_size=16, gpu_memory_utilization=0.0)
+
+    @pytest.mark.xfail(
+        strict=True,
+        reason="Lower-bound guard missing in CacheConfig._verify_args; "
+               "fixed by PR fix/cache-config-validation.",
+    )
+    def test_negative_utilization_raises(self) -> None:
+        """Negative gpu_memory_utilization is nonsensical and should be rejected
+        at construction time rather than surfacing as a cryptic failure later."""
+        with pytest.raises(ValueError, match="GPU memory utilization"):
+            CacheConfig(block_size=16, gpu_memory_utilization=-0.5)
+
+
+# ---------------------------------------------------------------------------
+# ParallelConfig
+# ---------------------------------------------------------------------------
+
+def _parallel(
+    dp: int = 2,
+    cp: int = 1,
+    ep: int = 2,
+    engram: int = 0,
+    engine_rank: int = 0,
+    engine_group_rank: int = 0,
+    engine_type: EngineType = EngineType.NORMAL,
+) -> ParallelConfig:
+    return ParallelConfig(
+        data_parallel_size=dp,
+        context_parallel_size=cp,
+        expert_parallel_size=ep,
+        engram_parallel_size=engram,
+        engine_rank=engine_rank,
+        engine_group_rank=engine_group_rank,
+        engine_type=engine_type,
+    )
+
+
+class TestParallelConfig:
+    def test_valid_minimal(self) -> None:
+        cfg = _parallel(dp=2, cp=1, ep=2, engram=0)
+        assert cfg.data_parallel_size == 2
+        assert cfg.context_parallel_size == 1
+        assert cfg.expert_parallel_size == 2
+
+    def test_data_parallel_auto_derive_from_ep_and_cp(self) -> None:
+        """When data_parallel_size=0, it should be derived as ep // cp."""
+        cfg = _parallel(dp=0, cp=2, ep=4, engram=0)
+        assert cfg.data_parallel_size == 2  # 4 // 2
+
+    def test_data_parallel_auto_derive_minimum_one(self) -> None:
+        """Auto-derived data_parallel_size is always at least 1."""
+        # ep=1, cp=4 → integer division gives 0, but clamped to 1.
+        cfg = _parallel(dp=0, cp=4, ep=1, engram=0, engine_rank=0)
+        assert cfg.data_parallel_size == 1
+
+    def test_world_size_computed_correctly(self) -> None:
+        # world_size = max(cp * dp, engram_parallel_size)
+        cfg = _parallel(dp=4, cp=2, ep=4, engram=0)
+        # engram defaults to model_parallel_size * dp = 2 * 4 = 8
+        assert cfg.world_size == 8
+
+    def test_data_parallel_rank(self) -> None:
+        cfg = _parallel(dp=4, cp=1, ep=4, engram=0, engine_rank=3)
+        assert cfg.data_parallel_rank == 3 % 4
+
+    def test_model_parallel_size_equals_context_parallel(self) -> None:
+        cfg = _parallel(dp=2, cp=3, ep=6, engram=0)
+        assert cfg.model_parallel_size == 3
+
+    def test_engram_defaults_to_model_times_dp(self) -> None:
+        cfg = _parallel(dp=2, cp=2, ep=4, engram=0)
+        # engram_parallel_size = model_parallel_size * dp = 2 * 2 = 4
+        assert cfg.engram_parallel_size == 4
+
+    def test_invalid_ep_not_divisible_raises(self) -> None:
+        """(cp * dp) must be divisible by ep."""
+        with pytest.raises(AssertionError):
+            _parallel(dp=2, cp=2, ep=3, engram=0)  # 2*2=4 not divisible by 3
+
+    def test_engine_rank_out_of_range_raises(self) -> None:
+        """engine_rank must be in [0, world_size // model_parallel_size)."""
+        with pytest.raises(AssertionError):
+            _parallel(dp=2, cp=1, ep=2, engram=0, engine_rank=999)
+
+
+# ---------------------------------------------------------------------------
+# SchedulerConfig
+# ---------------------------------------------------------------------------
+
+class TestSchedulerConfig:
+    def test_valid_basic(self) -> None:
+        cfg = SchedulerConfig(
+            max_num_batched_tokens=None,
+            max_num_seqs=64,
+            max_model_len=4096,
+        )
+        assert cfg.max_num_seqs == 64
+        assert cfg.max_model_len == 4096
+
+    def test_max_num_batched_tokens_defaults_to_max_model_len_or_4096(self) -> None:
+        cfg = SchedulerConfig(
+            max_num_batched_tokens=None,
+            max_num_seqs=4,
+            max_model_len=2048,
+        )
+        # _get_max_num_batched_tokens returns max(max_model_len, 4096, decode_tokens)
+        assert cfg.max_num_batched_tokens >= 4096
+
+    def test_explicit_max_num_batched_tokens(self) -> None:
+        cfg = SchedulerConfig(
+            max_num_batched_tokens=8192,
+            max_num_seqs=4,
+            max_model_len=4096,
+        )
+        assert cfg.max_num_batched_tokens == 8192
+
+    def test_too_small_batched_tokens_raises(self) -> None:
+        """max_num_batched_tokens < max_model_len must be rejected when
+        chunked_prefill is disabled, because it would silently cap the
+        effective context window."""
+        with pytest.raises(ValueError, match="max_num_batched_tokens"):
+            SchedulerConfig(
+                max_num_batched_tokens=512,
+                max_num_seqs=4,
+                max_model_len=4096,
+                chunked_prefill=False,
+            )
+
+    def test_too_small_batched_tokens_ok_with_chunked_prefill(self) -> None:
+        """With chunked_prefill=True the constraint is lifted."""
+        SchedulerConfig(
+            max_num_batched_tokens=512,
+            max_num_seqs=4,
+            max_model_len=4096,
+            chunked_prefill=True,
+        )
+
+    def test_prefill_attempt_interval_zero_raises(self) -> None:
+        with pytest.raises(ValueError, match="prefill_attempt_interval"):
+            SchedulerConfig(
+                max_num_batched_tokens=None,
+                max_num_seqs=4,
+                max_model_len=4096,
+                prefill_attempt_interval=0,
+            )
+
+    def test_prefill_attempt_interval_negative_raises(self) -> None:
+        with pytest.raises(ValueError, match="prefill_attempt_interval"):
+            SchedulerConfig(
+                max_num_batched_tokens=None,
+                max_num_seqs=4,
+                max_model_len=4096,
+                prefill_attempt_interval=-1,
+            )
+
+    def test_prefill_any_attempt_interval_zero_raises(self) -> None:
+        with pytest.raises(ValueError, match="prefill_any_attempt_interval"):
+            SchedulerConfig(
+                max_num_batched_tokens=None,
+                max_num_seqs=4,
+                max_model_len=4096,
+                prefill_any_attempt_interval=0,
+            )
+
+    def test_runtime_max_model_len_greater_than_max_model_len(self) -> None:
+        cfg = SchedulerConfig(
+            max_num_batched_tokens=None,
+            max_num_seqs=4,
+            max_model_len=4096,
+        )
+        assert cfg.runtime_max_model_len >= cfg.max_model_len
+
+    def test_stop_preempted_request_stored(self) -> None:
+        cfg = SchedulerConfig(
+            max_num_batched_tokens=None,
+            max_num_seqs=4,
+            max_model_len=4096,
+            stop_preempted_request=True,
+        )
+        assert cfg.stop_preempted_request is True
diff --git a/tests/test_sampling_params.py b/tests/test_sampling_params.py
new file mode 100644
index 0000000..163dedc
--- /dev/null
+++ b/tests/test_sampling_params.py
@@ -0,0 +1,323 @@
+# SPDX-License-Identifier: Apache-2.0
+
+"""Unit tests for SamplingParams validation and construction.
+
+These tests exercise every validation rule in SamplingParams._verify_args(),
+the greedy-mode clamping applied after validation, the _make_seed helper,
+and the deprecation warning for the `logprobs` parameter.
+
+All tests are CPU-only and require no model weights or CUDA device.
+"""
+
+from __future__ import annotations
+
+import random
+import warnings
+
+import pytest
+import torch
+
+from deepinfer.sampling_params import SAMPLING_EPS, SamplingParams
+
+
+# ---------------------------------------------------------------------------
+# Helpers
+# ---------------------------------------------------------------------------
+
+def make(**kwargs) -> SamplingParams:
+    """Construct SamplingParams with defaults overridden by kwargs."""
+    return SamplingParams(**kwargs)
+
+
+# ---------------------------------------------------------------------------
+# Default construction
+# ---------------------------------------------------------------------------
+
+class TestDefaults:
+    def test_default_construction_succeeds(self) -> None:
+        sp = SamplingParams()
+        assert sp.temperature == pytest.approx(1.0)
+        assert sp.top_p == pytest.approx(1.0)
+        assert sp.top_k == -1
+        assert sp.ignore_eos is False
+        assert sp.max_tokens == 16
+        assert sp.prompt_logprobs is False
+        assert sp.top_logprobs == 0
+        assert sp.so_args is None
+        assert sp.golden_token_ids is None
+
+    def test_seq_seed_auto_generated(self) -> None:
+        sp1 = SamplingParams()
+        sp2 = SamplingParams()
+        # With overwhelming probability two auto-seeds differ.
+        # If they collide the test is still valid (just flaky by chance).
+        assert 0 <= sp1.seq_seed < 2**64
+        assert 0 <= sp2.seq_seed < 2**64
+
+    def test_repr_contains_key_fields(self) -> None:
+        sp = SamplingParams(temperature=0.8, max_tokens=64)
+        r = repr(sp)
+        assert "temperature=0.8" in r
+        assert "max_tokens=64" in r
+        assert "top_p=" in r
+        assert "seq_seed=" in r
+
+
+# ---------------------------------------------------------------------------
+# temperature
+# ---------------------------------------------------------------------------
+
+class TestTemperature:
+    def test_valid_positive(self) -> None:
+        make(temperature=0.7)
+
+    def test_valid_exactly_one(self) -> None:
+        make(temperature=1.0)
+
+    def test_valid_high(self) -> None:
+        make(temperature=2.5)
+
+    def test_zero_triggers_greedy_clamp(self) -> None:
+        """temperature=0 should not raise; instead it is clamped to SAMPLING_EPS
+        and top_p/top_k are reset to greedy defaults."""
+        sp = make(temperature=0.0, top_p=0.9, top_k=50)
+        assert sp.temperature == pytest.approx(SAMPLING_EPS)
+        assert sp.top_p == pytest.approx(1.0)
+        assert sp.top_k == -1
+
+    def test_near_zero_below_eps_triggers_greedy_clamp(self) -> None:
+        sp = make(temperature=SAMPLING_EPS / 2)
+        assert sp.temperature == pytest.approx(SAMPLING_EPS)
+
+    def test_exactly_eps_is_not_clamped(self) -> None:
+        sp = make(temperature=SAMPLING_EPS)
+        # SAMPLING_EPS is NOT strictly less than SAMPLING_EPS, so no clamp.
+        assert sp.temperature == pytest.approx(SAMPLING_EPS)
+
+    def test_negative_raises(self) -> None:
+        with pytest.raises(ValueError, match="temperature must be non-negative"):
+            make(temperature=-0.1)
+
+
+# ---------------------------------------------------------------------------
+# top_p
+# ---------------------------------------------------------------------------
+
+class TestTopP:
+    def test_valid_one(self) -> None:
+        make(top_p=1.0)
+
+    def test_valid_fraction(self) -> None:
+        make(top_p=0.9)
+
+    def test_valid_near_zero(self) -> None:
+        make(top_p=1e-9)
+
+    def test_zero_raises(self) -> None:
+        with pytest.raises(ValueError, match="top_p must be in"):
+            make(top_p=0.0)
+
+    def test_above_one_raises(self) -> None:
+        with pytest.raises(ValueError, match="top_p must be in"):
+            make(top_p=1.01)
+
+    def test_negative_raises(self) -> None:
+        with pytest.raises(ValueError, match="top_p must be in"):
+            make(top_p=-0.5)
+
+
+# ---------------------------------------------------------------------------
+# top_k
+# ---------------------------------------------------------------------------
+
+class TestTopK:
+    def test_disabled(self) -> None:
+        make(top_k=-1)
+
+    def test_valid_one(self) -> None:
+        make(top_k=1)
+
+    def test_valid_large(self) -> None:
+        make(top_k=torch.iinfo(torch.int32).max)
+
+    def test_zero_raises(self) -> None:
+        with pytest.raises(ValueError, match="top_k must be -1"):
+            make(top_k=0)
+
+    def test_negative_other_than_minus_one_raises(self) -> None:
+        with pytest.raises(ValueError, match="top_k must be -1"):
+            make(top_k=-2)
+
+    def test_above_int32_max_raises(self) -> None:
+        with pytest.raises(ValueError, match="top_k must be -1"):
+            make(top_k=torch.iinfo(torch.int32).max + 1)
+
+
+# ---------------------------------------------------------------------------
+# max_tokens
+# ---------------------------------------------------------------------------
+
+class TestMaxTokens:
+    def test_valid_minimum(self) -> None:
+        sp = make(max_tokens=1)
+        assert sp.max_tokens == 1
+
+    def test_valid_default(self) -> None:
+        sp = make()
+        assert sp.max_tokens == 16
+
+    def test_valid_large(self) -> None:
+        make(max_tokens=131072)
+
+    def test_zero_raises(self) -> None:
+        """max_tokens=0 must raise ValueError rather than being silently coerced to 1."""
+        with pytest.raises(ValueError, match="max_tokens must be >= 1"):
+            make(max_tokens=0)
+
+    def test_negative_raises(self) -> None:
+        with pytest.raises(ValueError, match="max_tokens must be >= 1"):
+            make(max_tokens=-1)
+
+    def test_above_int32_max_raises(self) -> None:
+        with pytest.raises(ValueError, match="max_tokens must be >= 1"):
+            make(max_tokens=torch.iinfo(torch.int32).max + 1)
+
+
+# ---------------------------------------------------------------------------
+# top_logprobs
+# ---------------------------------------------------------------------------
+
+class TestTopLogprobs:
+    def test_valid_zero(self) -> None:
+        make(top_logprobs=0)
+
+    def test_valid_boundary(self) -> None:
+        make(top_logprobs=1000)
+
+    def test_negative_raises(self) -> None:
+        with pytest.raises(ValueError, match="top_logprobs must be in"):
+            make(top_logprobs=-1)
+
+    def test_above_1000_raises(self) -> None:
+        with pytest.raises(ValueError, match="top_logprobs must be in"):
+            make(top_logprobs=1001)
+
+
+# ---------------------------------------------------------------------------
+# seq_seed
+# ---------------------------------------------------------------------------
+
+class TestSeqSeed:
+    def test_none_generates_random_seed(self) -> None:
+        sp = make(seq_seed=None)
+        assert 0 <= sp.seq_seed < 2**64
+
+    def test_explicit_seed_stored(self) -> None:
+        sp = make(seq_seed=42)
+        assert sp.seq_seed == 42
+
+    def test_seed_clamped_to_uint64(self) -> None:
+        # A value larger than uint64 is masked to the lower 64 bits.
+        big = (2**64) + 5
+        sp = make(seq_seed=big)
+        assert sp.seq_seed == 5
+
+    def test_zero_seed(self) -> None:
+        sp = make(seq_seed=0)
+        assert sp.seq_seed == 0
+
+    def test_max_uint64_seed(self) -> None:
+        max_seed = 2**64 - 1
+        sp = make(seq_seed=max_seed)
+        assert sp.seq_seed == max_seed
+
+    def test_non_int_seed_raises(self) -> None:
+        with pytest.raises(TypeError):
+            make(seq_seed="not-an-int")  # type: ignore[arg-type]
+
+    def test_make_seed_deterministic_for_same_value(self) -> None:
+        assert SamplingParams._make_seed(123) == SamplingParams._make_seed(123)
+
+    def test_make_seed_none_is_random(self) -> None:
+        random.seed(0)
+        s1 = SamplingParams._make_seed(None)
+        random.seed(0)
+        s2 = SamplingParams._make_seed(None)
+        # Same random state → same result.
+        assert s1 == s2
+
+
+# ---------------------------------------------------------------------------
+# logprobs deprecation
+# ---------------------------------------------------------------------------
+
+class TestLogprobsDeprecation:
+    def test_logprobs_false_no_warning(self) -> None:
+        """The default logprobs=False must not emit any warning."""
+        with warnings.catch_warnings():
+            warnings.simplefilter("error")
+            make(logprobs=False)
+
+    def test_logprobs_true_emits_deprecation_warning(self) -> None:
+        with pytest.warns(DeprecationWarning, match="logprobs.*deprecated"):
+            make(logprobs=True)
+
+    def test_logprobs_true_does_not_affect_top_logprobs(self) -> None:
+        with warnings.catch_warnings():
+            warnings.simplefilter("ignore", DeprecationWarning)
+            sp = make(logprobs=True, top_logprobs=5)
+        assert sp.top_logprobs == 5
+
+
+# ---------------------------------------------------------------------------
+# Optional / passthrough fields
+# ---------------------------------------------------------------------------
+
+class TestOptionalFields:
+    def test_golden_token_ids_stored(self) -> None:
+        ids = [1, 2, 3]
+        sp = make(golden_token_ids=ids)
+        assert sp.golden_token_ids == ids
+
+    def test_golden_token_ids_none_by_default(self) -> None:
+        assert make().golden_token_ids is None
+
+    def test_so_args_stored(self) -> None:
+        so = {"schema_type": "json", "schema": "{}"}
+        sp = make(so_args=so)
+        assert sp.so_args == so
+
+    def test_ignore_eos_stored(self) -> None:
+        sp = make(ignore_eos=True)
+        assert sp.ignore_eos is True
+
+    def test_first_n_token_no_eos_stored(self) -> None:
+        sp = make(first_n_token_no_eos=3, eos_id=2)
+        assert sp.first_n_token_no_eos == 3
+        assert sp.eos_id == 2
+
+    def test_prompt_logprobs_stored(self) -> None:
+        sp = make(prompt_logprobs=True)
+        assert sp.prompt_logprobs is True
+
+
+# ---------------------------------------------------------------------------
+# Greedy mode interaction
+# ---------------------------------------------------------------------------
+
+class TestGreedyMode:
+    def test_greedy_overrides_top_k(self) -> None:
+        sp = make(temperature=0.0, top_k=100)
+        assert sp.top_k == -1
+
+    def test_non_greedy_preserves_top_k(self) -> None:
+        sp = make(temperature=0.5, top_k=50)
+        assert sp.top_k == 50
+
+    def test_greedy_overrides_top_p(self) -> None:
+        sp = make(temperature=0.0, top_p=0.95)
+        assert sp.top_p == pytest.approx(1.0)
+
+    def test_non_greedy_preserves_top_p(self) -> None:
+        sp = make(temperature=0.5, top_p=0.95)
+        assert sp.top_p == pytest.approx(0.95)