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Cohere-1M — 2026-10-08

c8g.medium · peak recall 0.9999 @ 31 QPS
“cohere-1m c8g.medium query sweep phase 1: nprobe tail to 640, rerank pools 40/160/640/0”

QPS at recall

Recallmkt-rerank0mkt-rerank160mkt-rerank40mkt-rerank640
0.90936010456
0.95825610150
0.9969508943

Throughput under load

Recall · clientsmkt-rerank0mkt-rerank160mkt-rerank40mkt-rerank640
0.95 · 1 client86 (86)
p50 11.97 ms · p95 18.34 ms
58 (58)
p50 14.97 ms · p95 38.40 ms
102 (102)
p50 10.32 ms · p95 13.89 ms
53 (53)
p50 15.19 ms · p95 46.34 ms
0.95 · 2 clients (2x of 1 CPUs)83 (83) · 1.0×
p50 23.89 ms · p95 36.70 ms
56 (56) · 1.0×
p50 33.70 ms · p95 63.65 ms
99 (99) · 1.0×
p50 20.62 ms · p95 27.61 ms
51 (51) · 1.0×
p50 34.27 ms · p95 80.70 ms
0.99 · 1 client70 (70)
p50 14.32 ms · p95 22.70 ms
51 (51)
p50 17.01 ms · p95 40.96 ms
96 (96)
p50 10.70 ms · p95 14.67 ms
43 (43)
p50 17.97 ms · p95 56.29 ms
0.99 · 2 clients (2x of 1 CPUs)69 (69) · 1.0×
p50 28.54 ms · p95 42.91 ms
50 (50) · 1.0×
p50 37.66 ms · p95 67.39 ms
95 (95) · 1.0×
p50 21.45 ms · p95 27.47 ms
44 (44) · 1.0×
p50 38.66 ms · p95 95.55 ms

Each configuration is measured at its operating point for the target: its fastest sweep point whose recall reaches it. Values are measured there, not interpolated, so the recall can be above the target. Each cell is wall-clock QPS over the whole run, then in parentheses the throughput while every client is busy, which leaves out the drain at the end, and below them the p50 and p95 query latency over all clients. With more than one client, N× is the speedup: wall-clock QPS over the same configuration's single-client wall-clock QPS at that target. Ideal scaling matches the client count until the host's CPUs saturate, after which it flattens. Clients are concurrent connections from rekall, and CPU counts are the rekall host's. Wall-clock QPS includes rekall's own work between queries, so the 1-client row reads a little below QPS at recall, which comes from query latency. Recall is distance-based (ann-benchmarks, tie-aware).

Measured on config — mkt-rerank0 @0.95: mkt-rerank0 (r=0.953) · mkt-rerank160 @0.95: mkt-rerank160 (r=0.953) · mkt-rerank40 @0.95: mkt-rerank40 (r=0.952) · mkt-rerank640 @0.95: mkt-rerank640 (r=0.953) · mkt-rerank0 @0.99: mkt-rerank0 (r=0.994) · mkt-rerank160 @0.99: mkt-rerank160 (r=0.994) · mkt-rerank40 @0.99: mkt-rerank40 (r=0.992) · mkt-rerank640 @0.99: mkt-rerank640 (r=0.994)

Latency at recall

mkt-rerank0mkt-rerank160mkt-rerank40mkt-rerank640
@ recall 0.90
p5011.13 ms14.34 ms10.15 ms14.40 ms
p9516.74 ms37.12 ms13.73 ms43.08 ms
p9923.07 ms43.80 ms15.33 ms69.85 ms
@ recall 0.95
p5012.47 ms15.41 ms10.52 ms15.76 ms
p9519.32 ms38.97 ms14.08 ms48.12 ms
p9927.81 ms45.08 ms15.72 ms79.49 ms
@ recall 0.99
p5014.43 ms17.19 ms11.49 ms18.00 ms
p9522.98 ms41.12 ms15.30 ms56.64 ms
p9934.06 ms46.74 ms17.72 ms95.96 ms

All values are taken at exactly the stated recall: each configuration's own sweep is log-interpolated between its two surrounding measured points (measured values are used verbatim on an exact hit) and the best configuration at that recall is shown — values are never interpolated across different configurations. “—” means no single sweep spans that recall; nothing is extrapolated. Recall is distance-based (ann-benchmarks, tie-aware). Insert/update/delete workloads are not measured.

Plots

Pareto svg  ·  Distance-recall svg  ·  Per-config svg  ·  Build times svg  ·  Index sizes svg
paretodistance-recallbuild-timesbuild-sizes

Host

Instance typec8g.medium
Hostnameip-172-31-37-199
CPUunknown (1 vCPU)
Memory2 GB
OSUbuntu 26.04.1 LTS
Kernel7.0.0-1014-aws

Versions

rekall0.1.0 (f31e456 / feature/concurrent-search)
PostgreSQLPostgreSQL 18.6 (Ubuntu 18.6-1.pgdg26.04+2) on aarch64-unknown-linux-gnu, compiled by gcc (Ubuntu 15.2.0-16ubuntu1) 15.2.0, 64-bit
pg_prewarm1.2
pg_vectorsearch0.1.0-dev (039603a)
vector0.8.7

Search

k10
runs3
warmup500
num_queries10000
S3 archives3://rekall-bench/runs/cohere-1m/2026-10-08/c8g.medium-125128/

Index builds

IndexTypeBuild timeSizeWorkers
mkt-defaultsprism148.4s269.1 MB1
Index parameters
mkt-defaults: {"boundary_epsilon":null,"centroid_compression":null,"centroid_fastscan":null,"fan_out":null,"fastscan":null,"fastscan_bits":null,"nlist":null,"rerank_vectors":null,"soar_lambda":null}
Instance settings (observed)
instance type          c8g.medium
vCPU (observed)        1
memory (observed)      1.8 GiB
cpu model              unknown
kernel                 7.0.0-1014-aws
data directory         /var/lib/postgresql/18/main
mount point            /
filesystem             ext4
media                  ebs
device                 nvme0n1
device size            107 GB
ebs volume             vol-04865ecb7ba623cf5
ebs volume type        gp3
provisioned IOPS       3000
provisioned throughput 125 MB/s
io scheduler           mq-deadline
nr_requests            126
read_ahead_kb          128
PostgreSQL settings
checkpoint_completion_target = 0.9
cluster_name = 18/main
data_directory = /var/lib/postgresql/18/main
effective_cache_size = 1 GB
effective_io_concurrency = 200
huge_pages = try
jit = off
maintenance_io_concurrency = 200
maintenance_work_mem = 384 MB
max_connections = 52
max_parallel_maintenance_workers = 1
max_parallel_workers = 1
max_wal_size = 4 GB
random_page_cost = 1.1
server_version = 18.6 (Ubuntu 18.6-1.pgdg26.04+2)
shared_buffers = 512 MB
wal_level = replica
work_mem = 16 MB