Jamba 1.5 Mini
By AI21 Labs · Israel
Updated 2026-07-13
Overview
AI21 Labs' hybrid SSM-Transformer with MoE routing, activating 12B of 52B parameters. Delivers a verified 256k context window but ships under AI21's non-OSI Jamba license.
When to pick this model
- Long-document workflows that genuinely use 200k+ tokens
- Multilingual chat across the 9 supported languages
- Benchmarking SSM-Transformer hybrids against pure attention models
- Use cases where the Jamba license terms are acceptable
VRAM requirements by quantization
| Quantization | VRAM required |
|---|---|
| Q4_K_M (recommended) | 30 GB |
| Q5_K_M | 37 GB |
| Q8_0 | 55 GB |
| FP16 (no quantization) | 104 GB |
VRAM figures include model weights plus a typical 8k KV cache and ~600 MB runtime overhead (Ollama / llama.cpp baseline). Add headroom for higher context lengths.
In practice, Jamba 1.5 Mini spills past single consumer GPUs even at Q4_K_M (30 GB) — think dual-GPU or workstation cards. Stepping up to Q8_0 nearly doubles the footprint to 55 GB, and unquantized FP16 weights take 104 GB — plan your GPU around the Q4 or Q5 figure unless you specifically need the higher fidelity.
Without a GPU, Jamba 1.5 Mini needs roughly 48 GB of system RAM to run on CPU via llama.cpp or Ollama — workable for background jobs, but far slower than GPU inference. Throughput estimates from our compatibility engine: around 6 tokens/sec on entry-level GPUs, on the order of 20 tokens/sec on a mid-range card, and up to 50 tokens/sec on high-end hardware — assuming the chosen quantization fully fits in VRAM.
What hardware do you need
The table below matches Jamba 1.5 Mini to common GPU memory tiers, using the highest-fidelity quantization that fully fits each card class. Spilling layers to system RAM works but costs most of the speed, so size your card to the quantization you actually want to run.
| GPU memory | Example cards | Best fit for Jamba 1.5 Mini |
|---|---|---|
| 8 GB | RTX 5070 Laptop, RTX 5060, RTX 5060 Ti 8GB | Does not fit — needs 30 GB at Q4_K_M |
| 12 GB | RTX 5070, RTX 5070 Ti Laptop, RTX 4080 Laptop | Does not fit — needs 30 GB at Q4_K_M |
| 16 GB | RTX 5080, RTX 4080 Super, Radeon RX 9070 XT | Does not fit — needs 30 GB at Q4_K_M |
| 24 GB | RTX 4090, Radeon RX 7900 XTX, RTX 5090 Laptop | Does not fit — needs 30 GB at Q4_K_M |
| 32 GB | RTX 5090 | Q4_K_M (30 GB used) |
Which GPU should you buy to run Jamba 1.5 Mini?
To run Jamba 1.5 Mini locally at Q4, you need ~30 GB of VRAM. The best value for this is a RTX 5090 (32 GB VRAM).
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Strengths
- Effective 256k context (86% on RULER)
- Unique SSM-Transformer hybrid architecture
- Strong throughput vs. dense models of similar capability
- Solid 9-language coverage
Limitations
- Custom Jamba license is not OSI-approved
- Partial llama.cpp support complicates local deployment
- Superseded by Jamba 1.6 and 1.7
- Smaller fine-tune ecosystem than Llama or Qwen
Typical workloads
In our catalog grid, Jamba 1.5 Mini is filed under Efficient Long Context, Mamba Hybrid — the use cases where its size/quality trade-off makes the most sense. Its tags translate to concrete workloads: multilingual workloads.
The 250k-token context window is large enough to hold entire codebases' worth of files or long reports in a single prompt, which is what makes local RAG and document analysis practical. It ships under the Jamba Open Model License license — commercial use is generally possible but read the specific terms before embedding it in a product.
Architecture & training
Architecture: Hybrid SSM-Transformer (Mamba+Attention) + MoE · 52B/12B active
Training: 256k effective ctx (86% at 256k RULER).
A novel hybrid with real long-context performance, now eclipsed by newer Jamba releases and gated by a non-standard license.
Quick start
# HuggingFace : ai21labs/AI21-Jamba-Mini-1.5Or use the open-source MCP server to query this model from Claude Desktop, Cursor, or any MCP-compatible client.
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Frequently asked questions
How much VRAM does Jamba 1.5 Mini need?
At the recommended Q4_K_M quantization, Jamba 1.5 Mini needs about 30 GB of VRAM. Q8_0 takes 55 GB, and unquantized FP16 weights take 104 GB.
Can Jamba 1.5 Mini run without a GPU?
Yes — with roughly 48 GB of system RAM it runs CPU-only through llama.cpp or Ollama. Expect a fraction of GPU speed, which is fine for background or batch jobs but slow for interactive chat.
What context window does Jamba 1.5 Mini support?
Jamba 1.5 Mini supports a 250k-token context window (256,000 tokens).
Can I use Jamba 1.5 Mini commercially?
Jamba 1.5 Mini ships under the Jamba Open Model License license. Commercial use is generally permitted subject to its terms — review the license text before shipping a product.
How fast is Jamba 1.5 Mini on consumer hardware?
Our compatibility engine estimates on the order of 20 tokens/sec on a mid-range GPU and up to 50 tokens/sec on high-end cards, assuming the quantization fully fits in VRAM.
Which quantization of Jamba 1.5 Mini should I download first?
Start with Q4_K_M (30 GB) — the standard size/quality sweet spot. Step up to Q5_K_M or Q8_0 only if you have VRAM headroom. It does not fit a single 24 GB consumer card — plan for multi-GPU or server hardware.