AI21 Jamba Mini 1.5 — Hardware Requirements & GPU Compatibility
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AI21 Jamba Mini 1.5 is a 51.6B-parameter open language model from AI21 Labs in the Jamba family. At BF16 it needs about 113.45 GB of VRAM — see which GPUs and Macs can run it below.
AI21 Jamba Mini 1.5 runs on a 128 GB+ Mac or a datacenter GPU
Best local pick → Mac Pro M2 Ultra (128 GB) · BF16 · 117.8 GB · ~5 tok/s
Macs from 128 GB (BF16 · ~5 tok/s)
Other
Not set up for it?
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run AI21 Jamba Mini 1.5
One recommended way. Every other method, OS and quant is in the step-by-step guide.Each way opens a step-by-step guide for your OS and quant.
Recommended
vllm serve ai21labs/AI21-Jamba-Mini-1.5 --max-model-len 16384 --tensor-parallel-size 8vLLM repo with its own quantisation
Download: Weights on Hugging Face
Ways to runOther ways
each opens step-by-stepRun in cloud
Runs on B200 (192 GB) (74 GB headroom) · BF16
- Generation speed
- ~46 tok/s
- generation speed
- Cost per 1M output tokens
- $36.27
- per 1M output tokens
How Much VRAM Does AI21 Jamba Mini 1.5 Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | Runs on | |
|---|---|---|---|---|
| 113.5 GB | 122.7 GB | 16-bit · bfloat — Brain floating point 16 — preferred for training | Runs on Instinct MI250X 128 GB |
Selected BF16
Brain floating point 16117.8GBat 16K context
- Weights
- 112.5 GB
- KV cache
- 4.9 GB
- Overhead
- 0.3 GB
Server-class: ~117.8 GB at 16K context · one Instinct MI250X 128 GB
Or .
Which Hardware Can Run AI21 Jamba Mini 1.5?
BF16 · 113.5 GBAI21 Jamba Mini 1.5 (BF16) requires 113.5 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 148+ GB is recommended. No consumer GPU has enough memory. 17 devices with unified memory can run AI21 Jamba Mini 1.5, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB), Mac Pro M2 Ultra (128 GB).
128–144 GB
fits · 10.2 GB free · up to 32K ctx
4 GPUs · 2 Mac classes · 1 device family
GH200 Grace Hopper Superchip~28Simulate output on GH200 Grace Hopper SuperchipH200 NVL~28Simulate output on H200 NVLH200 SXM~28Simulate output on H200 SXMM2 Ultra*128 GB~5Simulate output on Mac Pro M2 Ultra (128 GB)M5 Max128 GB~4Simulate output on Mac Studio M5 Max (128 GB)GB10~2Simulate output on ASUS Ascent GX10192+ GB
fits · 74.2 GB free · up to 32K ctx
6 GPUs · 1 Mac class · 2 device families
B200~46Simulate output on B200B300~46Simulate output on B300Instinct MI350X~42Simulate output on Instinct MI350XM3 Ultra*256–512 GB~5Simulate output on Mac Studio (M3 Ultra, 256GB)8x H100 SXM5~154Simulate output on NVIDIA DGX H1008x A100 SXM4~94Simulate output on NVIDIA DGX A100 640GB
By VRAM tier · typical GPUs and Macs at BF16
Consumer GPUs (up to 32 GB) and Macs up to 96 GB: too small for this model (needs ≥ 117.8 GB)8 GB tier →12 GB tier →16 GB tier →24 GB tier →Macs →
How good is AI21 Jamba Mini 1.5?
llmrun Score blends public benchmark boards into one number; 100 = the average open model.
Not enough data yet for an llmrun Score.
A Score needs results on enough independent boards. How the Composite Score is built
Benchmarks
This is the single board showing results for this model; badges show who ran the test.
- LMArena Text
LMArena · Oct 2026Arena
Arena1239.8
#132/188 open
Similar models
2| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| Kimi Linear 48B A3B BaseMoonshot AI | — | 23.4 GB | ~275 | same size |
| Llama 3 3 Nemotron Super 49B V1 5NVIDIA | — | 22.8 GB | ~35 | same size |
Frequently Asked Questions
How much VRAM does AI21 Jamba Mini 1.5 need?
AI21 Jamba Mini 1.5 requires 113.5 GB of VRAM at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 51.6B × 16 bits ÷ 8 = 103.1 GB
KV Cache + Overhead ≈ 10.4 GB (at 2K context + ~0.3 GB framework)
Fit ratings and hardware model lists check this model with room for a 16K-token context, which needs a little more memory.
VRAM usage by quantization
Can NVIDIA GeForce RTX 5090 run AI21 Jamba Mini 1.5?
No — AI21 Jamba Mini 1.5 requires at least 113.5 GB at BF16, which exceeds the NVIDIA GeForce RTX 5090's 32 GB of VRAM.
Can I run AI21 Jamba Mini 1.5 on a Mac?
Yes — Mac Pro M2 Ultra (128 GB) and 14 other Macs can run AI21 Jamba Mini 1.5. Apple Silicon uses unified memory, so the model shares RAM with the system. At BF16 you need at least 113.5 GB of usable unified memory (RAM minus macOS overhead).
Can I run AI21 Jamba Mini 1.5 locally?
Yes — AI21 Jamba Mini 1.5 can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is AI21 Jamba Mini 1.5?
At BF16, AI21 Jamba Mini 1.5 can reach ~42 tok/s on AMD Instinct MI350X. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: NVIDIA B200 → 8000 ÷ 113.5 × 0.65 = ~46 tok/s
Estimated speed at BF16 (113.5 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of AI21 Jamba Mini 1.5?
At BF16, the download is about 103.14 GB.
Which GPUs can run AI21 Jamba Mini 1.5?
No single consumer GPU has enough VRAM to run AI21 Jamba Mini 1.5 at BF16 (113.5 GB). Multi-GPU or professional hardware is required.
Which devices can run AI21 Jamba Mini 1.5?
17 devices with unified memory can run AI21 Jamba Mini 1.5 at BF16 (113.5 GB), including ASUS Ascent GX10, Mac Pro M2 Ultra (128 GB), Mac Pro M2 Ultra (192 GB), Mac Studio (M3 Ultra, 256GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.