Know which private AI setup
fits your engineering work.

Compare models and infrastructure on your actual tasks before committing more budget.

We specialize in three types of engineering workloads.

Mechanical engineering

CAD, CAE, FEM

Software engineering

Coding workflow.

AI-powered products

Image, video, 3D generation.

What do you need help with?

Deploy private inference

From OpenAI and Claude to private DeepSeek, Kimi, and GLM.

Optimize and scale private inference

Optimize your inference engine. Get more out of your GPU.

We start by sitting down with your engineers to learn how their work gets checked. Then we map how they use AI: how many people are working at once, how often they send requests, how much context goes in, how much comes back, and what happens in between.

Manifold

Example workspaceEngineering tasks

Log

Request log
Workload type	Arrival	First token	Session ID	Input	Output
ENG agent	12:00:18	12:00:19	Session_18	542	136
ENG agent	12:00:17	12:00:18	Session_17	614	154
ENG agent	12:00:16	12:00:17	Session_16	696	174
ENG agent	12:00:15	12:00:16	Session_15	302	76
ENG agent	12:00:14	12:00:15	Session_14	442	110
ENG agent	12:00:13	12:00:14	Session_13	634	158
ENG agent	12:00:12	12:00:13	Session_12	701	175
ENG agent	12:00:11	12:00:12	Session_11	739	185
ENG agent	12:00:10	12:00:11	Session_10	792	198
ENG agent	12:00:09	12:00:10	Session_09	734	184
ENG agent	12:00:08	12:00:09	Session_08	446	112
ENG agent	12:00:07	12:00:08	Session_07	494	124
ENG agent	12:00:06	12:00:07	Session_06	821	205
ENG agent	12:00:05	12:00:06	Session_05	758	190
ENG agent	12:00:04	12:00:05	Session_04	799	87
ENG agent	12:00:03	12:00:04	Session_03	612	183

Workload Profile

Last 14 requests

17.3K tokens

Workload profile
Request	Input	Cached	Output	Reasoning	Failed
#05	758	427	190	126	79
#06	821	462	205	137	86
#07	494	278	124	82	52
#08	446	251	112	74	47
#09	734	413	184	122	77
#10	792	446	198	132	82
#11	739	416	185	123	77
#12	701	394	175	117	73
#13	634	356	158	106	66
#14	442	248	110	74	46
#15	302	170	76	50	32
#16	696	392	174	116	72
#17	614	346	154	102	64
#18	542	305	136	90	57

Input
Cached
Output
Reasoning
Failed

We use your workload profile to establish a baseline for your current setup: how long requests take, how much GPU capacity gets used, and how much it costs to complete a task that passes your team’s checks.

ManifoldWorking-day baseline

Example workspaceEngineering tasks

09:00–17:00One GPU94.4%Quality pass rate640 / 678Tasks passing checks

Hourly measurements

Baseline measurements by hour
Hour	p95 latency	GPU use	Cost / task	Passed / total
09:00	2.2 s	32%	$0.12	50 / 54
10:00	3.8 s	61%	$0.06	100 / 104
11:00	6.4 s	88%	$0.04	150 / 156
12:00	1.8 s	12%	$0.30	20 / 24
13:00	2.6 s	43%	$0.10	60 / 64
14:00	5.7 s	81%	$0.05	120 / 126
15:00	4.3 s	67%	$0.06	100 / 106
16:00	2.1 s	28%	$0.15	40 / 44

Operating cost: $6 per hour, including idle time. Cost per successful task uses the same acceptance checks throughout.

We compare alternative configurations against the same workload and quality requirements, showing how each performs on cost, response time, and capacity.

ManifoldInference optimization assessment

Hooli · Example reportSelf-hosted coding agents · 400 engineers

All 400 engineers on the same two GPUs.

DeepSeek V4.1-Flash · 2× NVIDIA B300 (288 GB HBM3e each) · text-only.

MXFP4 experts · MXFP8 other weights · FP8 KV cache. Baseline: official vLLM recipe; optimized: serving configuration tuned to the same agent workload.

Targets: P95 time to first token (TTFT) ≤ 3 s · P95 inter-token latency (ITL) ≤ 15 ms.

At 400 engineers · 240 concurrent sessions · same 2× B300
Deployment	P95 TTFT	P95 ITL	Both targets met
Baseline · vLLM recipe	43.3 s	17.5 ms	No
Optimized	1.8 s	14.0 ms	Yes

Time to first token by load

Load	Baseline	Optimized
270 engineers · 160 sessions	13.9 s	1.5 s
330 engineers · 200 sessions	30.5 s	1.6 s
400 engineers · 240 sessions	43.3 s	1.8 s

Serving 400 engineers: 4× B300 without optimization, 2× B300 with optimization. Avoided extra GPU spend: $163,000/year (about $13.6K/month at the report’s on-demand rates). This excludes existing GPU costs and the optimization engagement fee.

We turn the selected configuration into a deployment plan: what infrastructure to provision, how it fits into your existing systems, and how to validate it before rollout.

ManifoldExample deployment plan

Deployment plan

Selected configuration → production rollout

Selected setup

Infrastructure: Owned GPU
GPU: 16 × NVIDIA B200
Serving: Private inference endpoint
Model: kimi-k3.1-pro
Configuration: From comparison

Launch requirement

Quality pass: ≥ 94%
p95 latency: ≤ 6 s
Capacity: ≥ 156 tasks/h

SLOs

Ongoing targets · rolling 30 days

Availability: ≥ 99.9%
p95 latency: ≤ 6 s
Quality pass: ≥ 94% of reviewed tasks

How it connects

Your team’s tools
kimi-k3.1-pro
NVIDIA B20016 GPUs · 2 nodes

Launch timeline

Set upNov 16
ConnectNov 18
TestNov 20Acceptance gate
LaunchNov 23

Roll out the chosen configuration in stages, confirm it holds your quality and speed requirements on real traffic, and hand it over with monitoring in place.

Deployment path

01
Pilot

Run the chosen configuration on a slice of real traffic.
02
Validate

Confirm quality, speed and cost match what we measured.
03
Hand over

Your team runs it, with alerts if quality, speed or cost drift.

Know which private AI setup
fits your engineering work.

We specialize in three types of engineering workloads.

Mechanical engineering

Software engineering

AI-powered products

What do you need help with?

Deploy private inference

Optimize and scale private inference

Log

Workload Profile

Hourly measurements

All 400 engineers on the same two GPUs.

Time to first token by load

Deployment plan

Selected setup

Launch requirement

SLOs

How it connects

Launch timeline

Pilot

Validate

Hand over

Know where to invest next.

Start with
your workload.

Your decision, outlined.

Request	Input	Cached	Output	Reasoning	Failed
#05	758	427	190	126	79
#06	821	462	205	137	86
#07	494	278	124	82	52
#08	446	251	112	74	47
#09	734	413	184	122	77
#10	792	446	198	132	82
#11	739	416	185	123	77
#12	701	394	175	117	73
#13	634	356	158	106	66
#14	442	248	110	74	46
#15	302	170	76	50	32
#16	696	392	174	116	72
#17	614	346	154	102	64
#18	542	305	136	90	57

Request	Input	Cached	Output	Reasoning	Failed
#05	758	427	190	126	79
#06	821	462	205	137	86
#07	494	278	124	82	52
#08	446	251	112	74	47
#09	734	413	184	122	77
#10	792	446	198	132	82
#11	739	416	185	123	77
#12	701	394	175	117	73
#13	634	356	158	106	66
#14	442	248	110	74	46
#15	302	170	76	50	32
#16	696	392	174	116	72
#17	614	346	154	102	64
#18	542	305	136	90	57

Know which private AI setup fits your engineering work.

We specialize in three types of engineering workloads.

Mechanical engineering

Software engineering

AI-powered products

What do you need help with?

Deploy private inference

Optimize and scale private inference

How it works

Log

Workload Profile

Hourly measurements

All 400 engineers on the same two GPUs.

Time to first token by load

Selected setup

Launch requirement

SLOs

How it connects

Launch timeline

Pilot

Validate

Hand over

Know where to invest next.

Know which private AI setup
fits your engineering work.

Request	Input	Cached	Output	Reasoning	Failed
#05	758	427	190	126	79
#06	821	462	205	137	86
#07	494	278	124	82	52
#08	446	251	112	74	47
#09	734	413	184	122	77
#10	792	446	198	132	82
#11	739	416	185	123	77
#12	701	394	175	117	73
#13	634	356	158	106	66
#14	442	248	110	74	46
#15	302	170	76	50	32
#16	696	392	174	116	72
#17	614	346	154	102	64
#18	542	305	136	90	57