AI21-Jamba2-3B: how much VRAM does it need?
This page was written and translated automatically by AI and checked by software against its sources, with no human editing. Specifications come from the sources listed; memory requirements and speeds are estimates calculated with published formulas. How we use AI
AI21-Jamba2-3B has 3.2B parameters. At Q4_K_M with a context of 8,192 tokens, a single GPU needs 3.1 GiB of available memory for the result to be “Fits”.
Key facts
- Original repository
- ai21labs/AI21-Jamba2-3B@525c6c8
- Publisher
- ai21labs
- License
- apache-2.0
- Parameters (GGUF tensor table: the tensors llama.cpp loads; multi-token-prediction layers not counted)
- 3.2B
- Layers
- 28
- Attention layout
- 2 full-attention layers; 26 recurrent / linear-attention layers (fixed-size state)
- KV cache per token (F16, all attention layers)
- 1.0 KiB
- Longest supported context
- 262,144
GGUF files and memory needed
All quantizations come from one GGUF repository at one pinned revision: bartowski/ai21labs_AI21-Jamba2-3B-GGUF@02d70ac
| Quantization | File size | X at 4,096 context | X at 8,192 context | X at 32,768 context | X at 131,072 context |
|---|---|---|---|---|---|
| BF16 | 5.96 GiB | 7.3 GiB | 7.3 GiB | 7.3 GiB | 7.6 GiB |
| Q8_0 | 3.17 GiB | 4.5 GiB | 4.5 GiB | 4.6 GiB | 4.9 GiB |
| Q6_K | 2.46 GiB | 3.8 GiB | 3.8 GiB | 3.9 GiB | 4.1 GiB |
| Q5_K_M | 2.12 GiB | 3.4 GiB | 3.4 GiB | 3.5 GiB | 3.8 GiB |
| Q4_K_M | 1.80 GiB | 3.1 GiB | 3.1 GiB | 3.2 GiB | 3.5 GiB |
| Q4_0 | 1.73 GiB | 3.0 GiB | 3.1 GiB | 3.1 GiB | 3.4 GiB |
| IQ4_XS | 1.64 GiB | 3.0 GiB | 3.0 GiB | 3.0 GiB | 3.3 GiB |
| Q3_K_M | 1.43 GiB | 2.8 GiB | 2.8 GiB | 2.8 GiB | 3.1 GiB |
| IQ3_XXS | 1.21 GiB | 2.5 GiB | 2.5 GiB | 2.6 GiB | 2.9 GiB |
| Q2_K | 1.13 GiB | 2.4 GiB | 2.5 GiB | 2.5 GiB | 2.8 GiB |
| IQ2_M | 1.05 GiB | 2.4 GiB | 2.4 GiB | 2.4 GiB | 2.7 GiB |
Smallest GPU for each quantization
| Quantization | Smallest GPU that fits (one card) | Memory |
|---|---|---|
| BF16 | NVIDIA GeForce RTX 2080 Ti | 11 GB |
| Q8_0 | NVIDIA GeForce RTX 3050 8GB | 8 GB |
| Q6_K | NVIDIA GeForce RTX 3050 8GB | 8 GB |
| Q5_K_M | NVIDIA GeForce RTX 3050 8GB | 8 GB |
| Q4_K_M | NVIDIA GeForce RTX 3050 8GB | 8 GB |
| Q4_0 | NVIDIA GeForce RTX 3050 8GB | 8 GB |
| IQ4_XS | NVIDIA GeForce RTX 3050 8GB | 8 GB |
| Q3_K_M | NVIDIA GeForce RTX 2060 6GB | 6 GB |
| IQ3_XXS | NVIDIA GeForce RTX 2060 6GB | 6 GB |
| Q2_K | NVIDIA GeForce RTX 2060 6GB | 6 GB |
| IQ2_M | NVIDIA GeForce RTX 2060 6GB | 6 GB |
GPUs that can run AI21-Jamba2-3B
| GPU | Memory | Run profile | Highest quantization that fits | Available memory needed for “Fits” | Longest context | Speed ceiling | Result |
|---|---|---|---|---|---|---|---|
| NVIDIA GeForce GTX 1060 | 6 GB | CUDA (NVIDIA) | Q3_K_M | 2.8 GiB | 45,312 | ≤ 130 tok/s | Fits |
| NVIDIA GeForce GTX 1660 | 6 GB | CUDA (NVIDIA) | Q3_K_M | 2.8 GiB | 45,312 | ≤ 130 tok/s | Fits |
| NVIDIA GeForce GTX 1660 SUPER | 6 GB | CUDA (NVIDIA) | Q3_K_M | 2.8 GiB | 45,312 | ≤ 228 tok/s | Fits |
| NVIDIA GeForce RTX 2060 6GB | 6 GB | CUDA (NVIDIA) | Q3_K_M | 2.8 GiB | 45,312 | ≤ 228 tok/s | Fits |
| NVIDIA GeForce GTX 1070 | 8 GB | CUDA (NVIDIA) | Q8_0 | 4.5 GiB | 135,168 | ≤ 79.1 tok/s | Fits |
| NVIDIA GeForce GTX 1070 Ti | 8 GB | CUDA (NVIDIA) | Q8_0 | 4.5 GiB | 135,168 | ≤ 79.1 tok/s | Fits |
| NVIDIA GeForce GTX 1080 | 8 GB | CUDA (NVIDIA) | Q8_0 | 4.5 GiB | 135,168 | ≤ 98.9 tok/s | Fits |
| NVIDIA GeForce RTX 2060 SUPER | 8 GB | CUDA (NVIDIA) | Q8_0 | 4.5 GiB | 135,168 | ≤ 138 tok/s | Fits |
| NVIDIA GeForce RTX 2070 | 8 GB | CUDA (NVIDIA) | Q8_0 | 4.5 GiB | 135,168 | ≤ 138 tok/s | Fits |
| NVIDIA GeForce RTX 2080 | 8 GB | CUDA (NVIDIA) | Q8_0 | 4.5 GiB | 135,168 | ≤ 138 tok/s | Fits |
| NVIDIA GeForce RTX 3050 8GB | 8 GB | CUDA (NVIDIA) | Q8_0 | 4.5 GiB | 135,168 | ≤ 69.2 tok/s | Fits |
| NVIDIA GeForce RTX 3060 Ti | 8 GB | CUDA (NVIDIA) | Q8_0 | 4.5 GiB | 135,168 | ≤ 138 tok/s | Fits |
| NVIDIA GeForce RTX 3070 | 8 GB | CUDA (NVIDIA) | Q8_0 | 4.5 GiB | 135,168 | ≤ 138 tok/s | Fits |
| NVIDIA GeForce RTX 3070 Ti | 8 GB | CUDA (NVIDIA) | Q8_0 | 4.5 GiB | 135,168 | ≤ 188 tok/s | Fits |
| NVIDIA GeForce RTX 4060 | 8 GB | CUDA (NVIDIA) | Q8_0 | 4.5 GiB | 135,168 | ≤ 84.0 tok/s | Fits |
| NVIDIA GeForce RTX 5060 | 8 GB | CUDA (NVIDIA) | Q8_0 | 4.5 GiB | 262,144 | ≤ 138 tok/s | Fits |
| NVIDIA GeForce RTX 3080 10GB | 10 GB | CUDA (NVIDIA) | Q8_0 | 4.5 GiB | 262,144 | ≤ 235 tok/s | Fits |
| NVIDIA GeForce GTX 1080 Ti | 11 GB | CUDA (NVIDIA) | BF16 | 7.3 GiB | 207,872 | ≤ 79.7 tok/s | Fits |
| NVIDIA GeForce RTX 2080 Ti | 11 GB | CUDA (NVIDIA) | BF16 | 7.3 GiB | 207,872 | ≤ 101 tok/s | Fits |
| AMD Radeon RX 7700 XT | 12 GB | ROCm on Linux (AMD) | BF16 | 7.3 GiB | 262,144 | — | Fits |
| NVIDIA GeForce RTX 3080 Ti | 12 GB | CUDA (NVIDIA) | BF16 | 7.3 GiB | 262,144 | ≤ 150 tok/s | Fits |
| NVIDIA GeForce RTX 4070 | 12 GB | CUDA (NVIDIA) | BF16 | 7.3 GiB | 262,144 | ≤ 83.0 tok/s | Fits |
| NVIDIA GeForce RTX 4070 Ti | 12 GB | CUDA (NVIDIA) | BF16 | 7.3 GiB | 262,144 | ≤ 83.0 tok/s | Fits |
| NVIDIA GeForce RTX 5070 | 12 GB | CUDA (NVIDIA) | BF16 | 7.3 GiB | 262,144 | ≤ 111 tok/s | Fits |
| AMD Radeon RX 7800 XT | 16 GB | ROCm on Linux (AMD) | BF16 | 7.3 GiB | 262,144 | — | Fits |
| AMD Radeon RX 7900 GRE | 16 GB | ROCm on Linux (AMD) | BF16 | 7.3 GiB | 262,144 | — | Fits |
| NVIDIA GeForce RTX 4060 Ti 16GB | 16 GB | CUDA (NVIDIA) | BF16 | 7.3 GiB | 262,144 | ≤ 47.4 tok/s | Fits |
| NVIDIA GeForce RTX 4070 Ti SUPER | 16 GB | CUDA (NVIDIA) | BF16 | 7.3 GiB | 262,144 | ≤ 111 tok/s | Fits |
| NVIDIA GeForce RTX 4080 | 16 GB | CUDA (NVIDIA) | BF16 | 7.3 GiB | 262,144 | ≤ 118 tok/s | Fits |
| NVIDIA GeForce RTX 5060 Ti 16GB | 16 GB | CUDA (NVIDIA) | BF16 | 7.3 GiB | 262,144 | — | Fits |
| NVIDIA GeForce RTX 5070 Ti | 16 GB | CUDA (NVIDIA) | BF16 | 7.3 GiB | 262,144 | ≤ 148 tok/s | Fits |
| NVIDIA GeForce RTX 5080 | 16 GB | CUDA (NVIDIA) | BF16 | 7.3 GiB | 262,144 | ≤ 158 tok/s | Fits |
| AMD Radeon RX 7900 XT | 20 GB | ROCm on Linux (AMD) | BF16 | 7.3 GiB | 262,144 | — | Fits |
| AMD Radeon RX 7900 XTX | 24 GB | ROCm on Linux (AMD) | BF16 | 7.3 GiB | 262,144 | — | Fits |
| NVIDIA GeForce RTX 3090 | 24 GB | CUDA (NVIDIA) | BF16 | 7.3 GiB | 262,144 | ≤ 154 tok/s | Fits |
| NVIDIA GeForce RTX 3090 Ti | 24 GB | CUDA (NVIDIA) | BF16 | 7.3 GiB | 262,144 | ≤ 166 tok/s | Fits |
| NVIDIA GeForce RTX 5090 | 32 GB | CUDA (NVIDIA) | BF16 | 7.3 GiB | 262,144 | ≤ 295 tok/s | Fits |
GPUs without a verdict
| GPU | Memory | Run profile | Smallest quantization | Available memory needed for “Fits” | Why no verdict | Result |
|---|---|---|---|---|---|---|
| NVIDIA GeForce GTX 1050 Ti | 4 GB | CUDA (NVIDIA) | IQ2_M | 2.4 GiB | Unknown: too little memory left after the reserve to judge reliably | Unknown |
| NVIDIA GeForce GTX 1650 | 4 GB | CUDA (NVIDIA) | IQ2_M | 2.4 GiB | Unknown: too little memory left after the reserve to judge reliably | Unknown |
| AMD Radeon Pro V520 | 8 GB | ROCm on Linux (AMD) | IQ2_M | 2.4 GiB | Unknown: memory configuration not established for this GPU; only “Doesn’t fit” would be shown. | Unknown |
| NVIDIA P4 | 8 GB | CUDA (NVIDIA) | IQ2_M | 2.4 GiB | Unknown: memory configuration not established for this GPU; only “Doesn’t fit” would be shown. | Unknown |
| NVIDIA P100 | 16 GB | CUDA (NVIDIA) | IQ2_M | 2.4 GiB | Unknown: memory configuration not established for this GPU; only “Doesn’t fit” would be shown. | Unknown |
| NVIDIA L4 | 24 GB | CUDA (NVIDIA) | IQ2_M | 2.4 GiB | Unknown: memory configuration not established for this GPU; only “Doesn’t fit” would be shown. | Unknown |
| NVIDIA Tesla P40 | 24 GB | CUDA (NVIDIA) | IQ2_M | 2.4 GiB | Unknown: memory configuration not established for this GPU; only “Doesn’t fit” would be shown. | Unknown |
| AMD Radeon AI PRO R9700 | 32 GB | ROCm on Linux (AMD) | IQ2_M | 2.4 GiB | Unknown: memory configuration not established for this GPU; only “Doesn’t fit” would be shown. | Unknown |
| AMD Radeon PRO W7800 32GB | 32 GB | ROCm on Linux (AMD) | IQ2_M | 2.4 GiB | Unknown: memory configuration not established for this GPU; only “Doesn’t fit” would be shown. | Unknown |
| NVIDIA Tesla V100 32GB | 32 GB | CUDA (NVIDIA) | IQ2_M | 2.4 GiB | Unknown: memory configuration not established for this GPU; only “Doesn’t fit” would be shown. | Unknown |
| AMD Radeon PRO W7800 48GB | 48 GB | ROCm on Linux (AMD) | IQ2_M | 2.4 GiB | Unknown: memory configuration not established for this GPU; only “Doesn’t fit” would be shown. | Unknown |
| AMD Radeon PRO W7900 | 48 GB | ROCm on Linux (AMD) | IQ2_M | 2.4 GiB | Unknown: memory configuration not established for this GPU; only “Doesn’t fit” would be shown. | Unknown |
| NVIDIA L40 | 48 GB | CUDA (NVIDIA) | IQ2_M | 2.4 GiB | Unknown: memory configuration not established for this GPU; only “Doesn’t fit” would be shown. | Unknown |
| AMD Instinct MI210 | 64 GB | ROCm on Linux (AMD) | IQ2_M | 2.4 GiB | Unknown: memory configuration not established for this GPU; only “Doesn’t fit” would be shown. | Unknown |
| NVIDIA H100 PCIe | 80 GB | CUDA (NVIDIA) | IQ2_M | 2.4 GiB | Unknown: memory configuration not established for this GPU; only “Doesn’t fit” would be shown. | Unknown |
| NVIDIA H100 NVL | 94 GB | CUDA (NVIDIA) | IQ2_M | 2.4 GiB | Unknown: memory configuration not established for this GPU; only “Doesn’t fit” would be shown. | Unknown |
| NVIDIA H200 SXM | 141 GB | CUDA (NVIDIA) | IQ2_M | 2.4 GiB | Unknown: memory configuration not established for this GPU; only “Doesn’t fit” would be shown. | Unknown |
| NVIDIA B200 | 180 GB | CUDA (NVIDIA) | IQ2_M | 2.4 GiB | Unknown: memory configuration not established for this GPU; only “Doesn’t fit” would be shown. | Unknown |
| AMD Instinct MI300X | 192 GB | ROCm on Linux (AMD) | IQ2_M | 2.4 GiB | Unknown: memory configuration not established for this GPU; only “Doesn’t fit” would be shown. | Unknown |
| NVIDIA B100 | 192 GB | CUDA (NVIDIA) | IQ2_M | 2.4 GiB | Unknown: memory configuration not established for this GPU; only “Doesn’t fit” would be shown. | Unknown |
| AMD Instinct MI325X | 256 GB | ROCm on Linux (AMD) | IQ2_M | 2.4 GiB | Unknown: memory configuration not established for this GPU; only “Doesn’t fit” would be shown. | Unknown |
| AMD Instinct MI350X | 288 GB | ROCm on Linux (AMD) | IQ2_M | 2.4 GiB | Unknown: memory configuration not established for this GPU; only “Doesn’t fit” would be shown. | Unknown |
| AMD Instinct MI355X | 288 GB | ROCm on Linux (AMD) | IQ2_M | 2.4 GiB | Unknown: memory configuration not established for this GPU; only “Doesn’t fit” would be shown. | Unknown |
- CUDA (NVIDIA): llama.cpp, CUDA back end (NVIDIA discrete GPU), all layers on the GPU, Flash Attention on, micro-batch (-ub) 512, F16 KV cache, context (-c) as shown; “Fits” is judged with llama-server’s default 4 parallel sequences (-np auto, unified KV cache) and “Doesn’t fit” with 1 sequence; idle GPU with no other programs using its memory.
- ROCm on Linux (AMD): llama.cpp, ROCm back end on Linux (AMD discrete GPU), all layers on the GPU, Flash Attention on, micro-batch (-ub) 512, F16 KV cache, context (-c) as shown; “Fits” is judged with llama-server’s default 4 parallel sequences (-np auto, unified KV cache) and “Doesn’t fit” with 1 sequence; idle GPU with no other programs using its memory.
Original weights
The model’s original weight files (safetensors, counted as described in the methodology) total 6.0 GiB. They are listed for reference only; every estimate on this page is for the GGUF files above, run with llama.cpp. ai21labs/AI21-Jamba2-3B · Methodology