diff --git a/src/test/cuckoocache_tests.cpp b/src/test/cuckoocache_tests.cpp
index b45dc26c5..68f09ab37 100644
--- a/src/test/cuckoocache_tests.cpp
+++ b/src/test/cuckoocache_tests.cpp
@@ -1,398 +1,399 @@
 // Copyright (c) 2012-2016 The Bitcoin Core developers
 // Distributed under the MIT software license, see the accompanying
 // file COPYING or http://www.opensource.org/licenses/mit-license.php.
 
 #include <cuckoocache.h>
 
 #include <random.h>
 #include <script/sigcache.h>
 
 #include <test/test_bitcoin.h>
 
 #include <boost/test/unit_test.hpp>
 #include <boost/thread/shared_mutex.hpp>
 
 /** Test Suite for CuckooCache
  *
  *  1) All tests should have a deterministic result (using insecure rand
  *  with deterministic seeds)
  *  2) Some test methods are templated to allow for easier testing
  *  against new versions / comparing
  *  3) Results should be treated as a regression test, i.e., did the behavior
  *  change significantly from what was expected. This can be OK, depending on
  *  the nature of the change, but requires updating the tests to reflect the new
  *  expected behavior. For example improving the hit rate may cause some tests
  *  using BOOST_CHECK_CLOSE to fail.
  */
 FastRandomContext local_rand_ctx(true);
 
 BOOST_AUTO_TEST_SUITE(cuckoocache_tests);
 
 /**
  * insecure_GetRandHash fills in a uint256 from local_rand_ctx
  */
 void insecure_GetRandHash(uint256 &t) {
     uint32_t *ptr = (uint32_t *)t.begin();
     for (uint8_t j = 0; j < 8; ++j) {
         *(ptr++) = local_rand_ctx.rand32();
     }
 }
 
 /**
  * Test that no values not inserted into the cache are read out of it.
  *
  * There are no repeats in the first 200000 insecure_GetRandHash calls
  */
 BOOST_AUTO_TEST_CASE(test_cuckoocache_no_fakes) {
     local_rand_ctx = FastRandomContext(true);
     CuckooCache::cache<uint256, SignatureCacheHasher> cc{};
-    cc.setup_bytes(32 << 20);
+    size_t megabytes = 4;
+    cc.setup_bytes(megabytes << 20);
     uint256 v;
     for (int x = 0; x < 100000; ++x) {
         insecure_GetRandHash(v);
         cc.insert(v);
     }
     for (int x = 0; x < 100000; ++x) {
         insecure_GetRandHash(v);
         BOOST_CHECK(!cc.contains(v, false));
     }
 };
 
 /**
  * This helper returns the hit rate when megabytes*load worth of entries are
  * inserted into a megabytes sized cache
  */
 template <typename Cache> double test_cache(size_t megabytes, double load) {
     local_rand_ctx = FastRandomContext(true);
     std::vector<uint256> hashes;
     Cache set{};
     size_t bytes = megabytes * (1 << 20);
     set.setup_bytes(bytes);
     uint32_t n_insert = static_cast<uint32_t>(load * (bytes / sizeof(uint256)));
     hashes.resize(n_insert);
     for (uint32_t i = 0; i < n_insert; ++i) {
         uint32_t *ptr = (uint32_t *)hashes[i].begin();
         for (uint8_t j = 0; j < 8; ++j) {
             *(ptr++) = local_rand_ctx.rand32();
         }
     }
     /**
      * We make a copy of the hashes because future optimizations of the
      * cuckoocache may overwrite the inserted element, so the test is "future
      * proofed".
      */
     std::vector<uint256> hashes_insert_copy = hashes;
     /** Do the insert */
     for (uint256 &h : hashes_insert_copy) {
         set.insert(h);
     }
     /** Count the hits */
     uint32_t count = 0;
     for (uint256 &h : hashes) {
         count += set.contains(h, false);
     }
     double hit_rate = double(count) / double(n_insert);
     return hit_rate;
 }
 
 /** The normalized hit rate for a given load.
  *
  * The semantics are a little confusing, so please see the below
  * explanation.
  *
  * Examples:
  *
  * 1) at load 0.5, we expect a perfect hit rate, so we multiply by
  * 1.0
  * 2) at load 2.0, we expect to see half the entries, so a perfect hit rate
  * would be 0.5. Therefore, if we see a hit rate of 0.4, 0.4*2.0 = 0.8 is the
  * normalized hit rate.
  *
  * This is basically the right semantics, but has a bit of a glitch depending on
  * how you measure around load 1.0 as after load 1.0 your normalized hit rate
  * becomes effectively perfect, ignoring freshness.
  */
 double normalize_hit_rate(double hits, double load) {
     return hits * std::max(load, 1.0);
 }
 
 /** Check the hit rate on loads ranging from 0.1 to 2.0 */
 BOOST_AUTO_TEST_CASE(cuckoocache_hit_rate_ok) {
     /**
      * Arbitrarily selected Hit Rate threshold that happens to work for this
      * test as a lower bound on performance.
      */
     double HitRateThresh = 0.98;
-    size_t megabytes = 32;
+    size_t megabytes = 4;
     for (double load = 0.1; load < 2; load *= 2) {
         double hits =
             test_cache<CuckooCache::cache<uint256, SignatureCacheHasher>>(
                 megabytes, load);
         BOOST_CHECK(normalize_hit_rate(hits, load) > HitRateThresh);
     }
 }
 
 /** This helper checks that erased elements are preferentially inserted onto and
  * that the hit rate of "fresher" keys is reasonable*/
 template <typename Cache> void test_cache_erase(size_t megabytes) {
     double load = 1;
     local_rand_ctx = FastRandomContext(true);
     std::vector<uint256> hashes;
     Cache set{};
     size_t bytes = megabytes * (1 << 20);
     set.setup_bytes(bytes);
     uint32_t n_insert = static_cast<uint32_t>(load * (bytes / sizeof(uint256)));
     hashes.resize(n_insert);
     for (uint32_t i = 0; i < n_insert; ++i) {
         uint32_t *ptr = (uint32_t *)hashes[i].begin();
         for (uint8_t j = 0; j < 8; ++j) {
             *(ptr++) = local_rand_ctx.rand32();
         }
     }
     /** We make a copy of the hashes because future optimizations of the
      * cuckoocache may overwrite the inserted element, so the test is
      * "future proofed".
      */
     std::vector<uint256> hashes_insert_copy = hashes;
 
     /** Insert the first half */
     for (uint32_t i = 0; i < (n_insert / 2); ++i) {
         set.insert(hashes_insert_copy[i]);
     }
     /** Erase the first quarter */
     for (uint32_t i = 0; i < (n_insert / 4); ++i) {
         set.contains(hashes[i], true);
     }
     /** Insert the second half */
     for (uint32_t i = (n_insert / 2); i < n_insert; ++i) {
         set.insert(hashes_insert_copy[i]);
     }
 
     /** elements that we marked erased but that are still there */
     size_t count_erased_but_contained = 0;
     /** elements that we did not erase but are older */
     size_t count_stale = 0;
     /** elements that were most recently inserted */
     size_t count_fresh = 0;
 
     for (uint32_t i = 0; i < (n_insert / 4); ++i) {
         count_erased_but_contained += set.contains(hashes[i], false);
     }
     for (uint32_t i = (n_insert / 4); i < (n_insert / 2); ++i) {
         count_stale += set.contains(hashes[i], false);
     }
     for (uint32_t i = (n_insert / 2); i < n_insert; ++i) {
         count_fresh += set.contains(hashes[i], false);
     }
 
     double hit_rate_erased_but_contained =
         double(count_erased_but_contained) / (double(n_insert) / 4.0);
     double hit_rate_stale = double(count_stale) / (double(n_insert) / 4.0);
     double hit_rate_fresh = double(count_fresh) / (double(n_insert) / 2.0);
 
     // Check that our hit_rate_fresh is perfect
     BOOST_CHECK_EQUAL(hit_rate_fresh, 1.0);
     // Check that we have a more than 2x better hit rate on stale elements than
     // erased elements.
     BOOST_CHECK(hit_rate_stale > 2 * hit_rate_erased_but_contained);
 }
 
 BOOST_AUTO_TEST_CASE(cuckoocache_erase_ok) {
-    size_t megabytes = 32;
+    size_t megabytes = 4;
     test_cache_erase<CuckooCache::cache<uint256, SignatureCacheHasher>>(
         megabytes);
 }
 
 template <typename Cache> void test_cache_erase_parallel(size_t megabytes) {
     double load = 1;
     local_rand_ctx = FastRandomContext(true);
     std::vector<uint256> hashes;
     Cache set{};
     size_t bytes = megabytes * (1 << 20);
     set.setup_bytes(bytes);
     uint32_t n_insert = static_cast<uint32_t>(load * (bytes / sizeof(uint256)));
     hashes.resize(n_insert);
     for (uint32_t i = 0; i < n_insert; ++i) {
         uint32_t *ptr = (uint32_t *)hashes[i].begin();
         for (uint8_t j = 0; j < 8; ++j) {
             *(ptr++) = local_rand_ctx.rand32();
         }
     }
     /** We make a copy of the hashes because future optimizations of the
      * cuckoocache may overwrite the inserted element, so the test is
      * "future proofed".
      */
     std::vector<uint256> hashes_insert_copy = hashes;
     boost::shared_mutex mtx;
 
     {
         /** Grab lock to make sure we release inserts */
         boost::unique_lock<boost::shared_mutex> l(mtx);
         /** Insert the first half */
         for (uint32_t i = 0; i < (n_insert / 2); ++i) {
             set.insert(hashes_insert_copy[i]);
         }
     }
 
     /** Spin up 3 threads to run contains with erase.
      */
     std::vector<std::thread> threads;
     /** Erase the first quarter */
     for (uint32_t x = 0; x < 3; ++x)
         /** Each thread is emplaced with x copy-by-value */
         threads.emplace_back([&, x] {
             boost::shared_lock<boost::shared_mutex> l(mtx);
             size_t ntodo = (n_insert / 4) / 3;
             size_t start = ntodo * x;
             size_t end = ntodo * (x + 1);
             for (uint32_t i = start; i < end; ++i) {
                 set.contains(hashes[i], true);
             }
         });
 
     /** Wait for all threads to finish */
     for (std::thread &t : threads) {
         t.join();
     }
     /** Grab lock to make sure we observe erases */
     boost::unique_lock<boost::shared_mutex> l(mtx);
     /** Insert the second half */
     for (uint32_t i = (n_insert / 2); i < n_insert; ++i) {
         set.insert(hashes_insert_copy[i]);
     }
 
     /** elements that we marked erased but that are still there */
     size_t count_erased_but_contained = 0;
     /** elements that we did not erase but are older */
     size_t count_stale = 0;
     /** elements that were most recently inserted */
     size_t count_fresh = 0;
 
     for (uint32_t i = 0; i < (n_insert / 4); ++i) {
         count_erased_but_contained += set.contains(hashes[i], false);
     }
     for (uint32_t i = (n_insert / 4); i < (n_insert / 2); ++i) {
         count_stale += set.contains(hashes[i], false);
     }
     for (uint32_t i = (n_insert / 2); i < n_insert; ++i) {
         count_fresh += set.contains(hashes[i], false);
     }
 
     double hit_rate_erased_but_contained =
         double(count_erased_but_contained) / (double(n_insert) / 4.0);
     double hit_rate_stale = double(count_stale) / (double(n_insert) / 4.0);
     double hit_rate_fresh = double(count_fresh) / (double(n_insert) / 2.0);
 
     // Check that our hit_rate_fresh is perfect
     BOOST_CHECK_EQUAL(hit_rate_fresh, 1.0);
     // Check that we have a more than 2x better hit rate on stale elements than
     // erased elements.
     BOOST_CHECK(hit_rate_stale > 2 * hit_rate_erased_but_contained);
 }
 
 BOOST_AUTO_TEST_CASE(cuckoocache_erase_parallel_ok) {
-    size_t megabytes = 32;
+    size_t megabytes = 4;
     test_cache_erase_parallel<
         CuckooCache::cache<uint256, SignatureCacheHasher>>(megabytes);
 }
 
 template <typename Cache> void test_cache_generations() {
     // This test checks that for a simulation of network activity, the fresh hit
     // rate is never below 99%, and the number of times that it is worse than
     // 99.9% are less than 1% of the time.
     double min_hit_rate = 0.99;
     double tight_hit_rate = 0.999;
     double max_rate_less_than_tight_hit_rate = 0.01;
     // A cache that meets this specification is therefore shown to have a hit
     // rate of at least tight_hit_rate * (1 - max_rate_less_than_tight_hit_rate)
     // +
     // min_hit_rate*max_rate_less_than_tight_hit_rate = 0.999*99%+0.99*1% ==
     // 99.89%
     // hit rate with low variance.
 
     // We use deterministic values, but this test has also passed on many
     // iterations with non-deterministic values, so it isn't "overfit" to the
     // specific entropy in FastRandomContext(true) and implementation of the
     // cache.
     local_rand_ctx = FastRandomContext(true);
 
     // block_activity models a chunk of network activity. n_insert elements are
     // adde to the cache. The first and last n/4 are stored for removal later
     // and the middle n/2 are not stored. This models a network which uses half
     // the signatures of recently (since the last block) added transactions
     // immediately and never uses the other half.
     struct block_activity {
         std::vector<uint256> reads;
         block_activity(uint32_t n_insert, Cache &c) : reads() {
             std::vector<uint256> inserts;
             inserts.resize(n_insert);
             reads.reserve(n_insert / 2);
             for (uint32_t i = 0; i < n_insert; ++i) {
                 uint32_t *ptr = (uint32_t *)inserts[i].begin();
                 for (uint8_t j = 0; j < 8; ++j) {
                     *(ptr++) = local_rand_ctx.rand32();
                 }
             }
             for (uint32_t i = 0; i < n_insert / 4; ++i) {
                 reads.push_back(inserts[i]);
             }
             for (uint32_t i = n_insert - (n_insert / 4); i < n_insert; ++i) {
                 reads.push_back(inserts[i]);
             }
             for (auto h : inserts) {
                 c.insert(h);
             }
         }
     };
 
-    const uint32_t BLOCK_SIZE = 10000;
+    const uint32_t BLOCK_SIZE = 1000;
     // We expect window size 60 to perform reasonably given that each epoch
     // stores 45% of the cache size (~472k).
     const uint32_t WINDOW_SIZE = 60;
     const uint32_t POP_AMOUNT = (BLOCK_SIZE / WINDOW_SIZE) / 2;
     const double load = 10;
-    const size_t megabytes = 32;
+    const size_t megabytes = 4;
     const size_t bytes = megabytes * (1 << 20);
     const uint32_t n_insert =
         static_cast<uint32_t>(load * (bytes / sizeof(uint256)));
 
     std::vector<block_activity> hashes;
     Cache set{};
     set.setup_bytes(bytes);
     hashes.reserve(n_insert / BLOCK_SIZE);
     std::deque<block_activity> last_few;
     uint32_t out_of_tight_tolerance = 0;
     uint32_t total = n_insert / BLOCK_SIZE;
     // we use the deque last_few to model a sliding window of blocks. at each
     // step, each of the last WINDOW_SIZE block_activities checks the cache for
     // POP_AMOUNT of the hashes that they inserted, and marks these erased.
     for (uint32_t i = 0; i < total; ++i) {
         if (last_few.size() == WINDOW_SIZE) last_few.pop_front();
         last_few.emplace_back(BLOCK_SIZE, set);
         uint32_t count = 0;
         for (auto &act : last_few) {
             for (uint32_t k = 0; k < POP_AMOUNT; ++k) {
                 count += set.contains(act.reads.back(), true);
                 act.reads.pop_back();
             }
         }
         // We use last_few.size() rather than WINDOW_SIZE for the correct
         // behavior on the first WINDOW_SIZE iterations where the deque is not
         // full yet.
         double hit = double(count) / (last_few.size() * POP_AMOUNT);
         // Loose Check that hit rate is above min_hit_rate
         BOOST_CHECK(hit > min_hit_rate);
         // Tighter check, count number of times we are less than tight_hit_rate
         // (and implicityly, greater than min_hit_rate)
         out_of_tight_tolerance += hit < tight_hit_rate;
     }
     // Check that being out of tolerance happens less than
     // max_rate_less_than_tight_hit_rate of the time
     BOOST_CHECK(double(out_of_tight_tolerance) / double(total) <
                 max_rate_less_than_tight_hit_rate);
 }
 BOOST_AUTO_TEST_CASE(cuckoocache_generations) {
     test_cache_generations<CuckooCache::cache<uint256, SignatureCacheHasher>>();
 }
 
 BOOST_AUTO_TEST_SUITE_END();