Backend Runtimes & Frameworks
4.4/5.0
VerifiedFreeLast technical review:
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Maple

Sub-2-BPW MoE model compression to run large language models on 6GB VRAM GPUs

Pricing

$0/mo

Free
Score

4.4/5

Last technical review
2026-11-08
Available plansVisit website (opens in a new tab)

Technical Overview

Maple is a mixture-of-experts (MoE) model compression tool that enables running large language models 100% in-VRAM on consumer 6GB GPUs through manifold-activated latent expert pruning. It is targeted at researchers and developers needing to deploy large models on modest hardware without PCIe bottlenecks. Distributed as open weights and calibrated GGUF containers.

Analyst Evaluation: Pros and Limitations

Verified Advantages

  • 100% in-VRAM execution on consumer hardware with limited VRAM.
  • Eliminates PCIe bus bandwidth transfer bottlenecks.
  • Open weights format compatible with llama.cpp.

Technical Considerations

  • Requires manual calibration via imatrix for new quantizations.
  • Aggressive expert pruning that may alter performance on specific tasks.

Technical Capabilities & Features

2/7
Not available:REST & GraphQL API
Not available:SSO / SAML Authentication
Included:Self-Hosted Option
Included:Full Data Export
Not available:Role-Based Access Control (RBAC)
Not available:Custom Domains & SSL
Not available:Immutable Audit Logs
Scorecard
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Technical Scorecard: Maple

85/100
DX & Tech Documentation
Excellent
80/100
Pricing Fairness
Excellent
95/100
Security & Governance
Favorable
70/100
Data Portability & Sovereignty
Excellent
90/100
Performance & Reliability
Excellent
90/100

Time to Value & Fit by Segment: Maple

Estimated Time-to-Value< 1 hour (Immediate)

Near-instantaneous deployment with minimal technical friction.

Learning CurveModerate (Standard)

Requires consulting technical documentation and basic credentials setup.

Fit Index by Organization Size

Startup & Agile Teams1 to 50 members
80%
Mid-Market & Scaleup50 to 500 members
70%
Corporate Enterprise500+ members
60%
Pricing & TCO
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Pricing

$0.00/mo

Model: FREE.

Free Tier Verification:

  • No credit card required
  • Commercial use allowed
  • Unlimited usage with no expiration
  • No mandatory watermarks
Get Maple (opens in a new tab)

Billing Risks & Hidden Clauses: Maple

Free Tier Limits:

Disponible como proyecto de código abierto y pesos libres sin coste de uso.

Total Cost of Ownership (TCO) Projection: Maple$0.00/mo
Deterministic cost estimation at 12 and 36 months based on team size, including SSO tax premiums and annual prepay discounts.
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Licensing Cost (1 to 100+ seats)
$0/forever
Unlimited team members with zero penalties
SSO Tax (SAML Enterprise)
$0no surcharge
No artificial security barriers
3-Year Investment (Software TCO)
$0total
operates under a zero-cost or open-source model. Licensing TCO is identical:
Usage Surcharges & Bill Shock Simulator: MapleWhat usage costs as you scale
Detect overage penalties before they impact engineering budget.
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Prototype / MVP (10 GB)

Included base quota in plan: 10 GB.
Low Risk / Predictable Bill
Base Monthly Rate
$0/mo
Fixed subscription or contracted tier
Overage Penalty
$0/month extra
Usage covered within plan quota
Estimated Monthly Invoice
$0/mo
Total monthly cost before taxes
Technical Mitigation Recommendation:

Suitable for development; verify concurrent connection limits.

Base Production (50 GB)

Included base quota in plan: 50 GB.
Low Risk / Predictable Bill
Base Monthly Rate
$0/mo
Fixed subscription or contracted tier
Overage Penalty
$0/month extra
Usage covered within plan quota
Estimated Monthly Invoice
$0/mo
Total monthly cost before taxes
Technical Mitigation Recommendation:

Implement table compression policies and temporal partitioning.

Active Growth (250 GB)

Included base quota in plan: 250 GB.
Low Risk / Predictable Bill
Base Monthly Rate
$0/mo
Fixed subscription or contracted tier
Overage Penalty
$0/month extra
Usage covered within plan quota
Estimated Monthly Invoice
$0/mo
Total monthly cost before taxes
Technical Mitigation Recommendation:

Offload cold backups to standard object storage (S3).

Large Scale (1.000 GB (1 TB))

Included base quota in plan: 1,000 GB.
Low Risk / Predictable Bill
Base Monthly Rate
$0/mo
Fixed subscription or contracted tier
Overage Penalty
$0/month extra
Usage covered within plan quota
Estimated Monthly Invoice
$0/mo
Total monthly cost before taxes
Technical Mitigation Recommendation:

Migrate to dedicated instance or self-hosted cluster to curtail I/O fees.

Security & Data Exit
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Compliance & Security Standards: Maple

SOC 2 Type IINot reported
ISO 27001Not reported
GDPR / RGPDNot reported
HIPAANot reported
PCI-DSSNot reported
Jurisdiction & Legal Sovereignty
🇺🇸United States [US]Immune to US CLOUD Act (EU Sovereign)DPA On Request
SSO Availability (SAML Enterprise)
No SSO Documented
Data Residency
GLOBAL
AI Privacy Policy
Not evaluated
Data Liberation & Migration Runbook: MapleGGUF
Actionable escape runbook and credential export guarantees to mitigate vendor lock-in.
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Supported Structured Export Formats
GGUF
Password Hash Portability
Locked: Proprietary Algorithm / No Export

Credential hashes are guarded under proprietary algorithms and cannot be exported.

CLI Command / Rescue Endpoint
git clone https://amsach.github.io/maple
Exit Protocol & Migration Notes

Los pesos comprimidos y archivos GGUF son completamente exportables y compatibles con cualquier infraestructura que soporte llama.cpp.

Deployment & Infrastructure Requirements: MapleOther
Hardware specs and Docker directives to run on local servers, dedicated VPS, or Kubernetes without telemetry leak to the cloud.
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Minimum Memory1 GB RAM (Recommended)
Compute CPU1 vCPU
Runtime / LanguagePython
Code LicenseOther
Docker CLI Directive
docker run -d --name maple -p 8080:8080 maple:latest
Companion Services & Dependencies:
PostgreSQL 15+Redis 7+
Community & Repository
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Technical Reputation of Maple

83/100 Technical Trust

Frequent Praises in Production

  • 100% in-VRAM execution on consumer hardware with limited VRAM.
  • Eliminates PCIe bus bandwidth transfer bottlenecks.
  • Open weights format compatible with llama.cpp.

Reported Production Frictions

  • Requires manual calibration via imatrix for new quantizations.
  • Aggressive expert pruning that may alter performance on specific tasks.
Commercial Treatment & Vendor Lock-in:1-Click Cancellation

Immediate cancellation from account settings without phone calls or forced retention.

Open source repository activity: Maple29
Stars, forks and release cadence
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Stars29
Forks1
Open Issues0
SPDX LicenseOther
Latest Commit DetectedNov 8, 2026

Frequently Asked Technical Questions (Maple)

How does Maple run large models on 6GB GPUs?

Maple compresses mixture-of-experts models using manifold-activated pruning that removes inactive experts, reducing the total container size below 5 GB.

What hardware is required to use Maple containers?

Containers are optimized for consumer GPUs with at least 6GB of VRAM, such as laptop graphics cards like the RTX 4050, avoiding system RAM paging.

Is this open source software?

Yes, it is distributed under an open-weights approach and models are compatible with local execution frameworks like llama.cpp.

Direct Alternatives

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