Jamba (AI21 Labs)
Israeli production model. Interleaves Mamba SSM layers with Transformer attention (1:7 ratio) + MoE blocks every 2 layers. SSM layers maintain fixed-size recurrent hidden state — compressed long-context memory bypassing growing KV cache.
At a glance
- Type
- SSM-Transformer hybrid + 256K KV-bypass context
- Tier
- T1
- Section
- Dedicated memory layers
- Created
- 2020-11
- Latest release
- not applicable — not OSS
- License
- not applicable — not OSS
- GitHub
- not applicable — no GitHub repo
- Pricing
- Free trial ($10 credit, no CC required); pay-as-you-go per token; enterprise custom pricing with VPC/on-prem
- Funding
- $608M total Series D · 2025-05
Taxonomy
- storage
- kv-cache
- retrieval
- attention
- persistence
- session
- update
- read-only
- unit
- kv-token
- governance
- opaque
- conflict
- none
When to use
Optimised for: long context (256K) at lower cost via SSM-Transformer hybrid
Anti-fit: not for ultra-low-cost commodity inference (premium positioning)
Pros & cons
Pros
Hybrid Transformer/Mamba architecture means memory cost grows sub-quadratically — long-context inference is genuinely cheaper.
Cons
Smaller ecosystem than Llama / Mistral; less third-party tooling support.
Claims & capabilities
256K context on single 8-GPU node. 3× throughput vs Mixtral 8x7B on long contexts. Jamba Reasoning 3B runs 250K-context inference on a laptop.
Technical surface
- API surface
- searched not found
- Backend storage
- searched not found
- Deployment
- Both (AI21 Studio cloud; Azure/AWS Bedrock/GCP Vertex AI; private VPC; on-premises)
- Embedding model
- searched not found
- Multi-tenancy
- searched not found
- MCP
- not documented publicly
- A2A
- not documented publicly
- OpenTelemetry
- not documented publicly
Similar systems
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