AI21 Labs·Jamba·JambaForCausalLM

AI21 Jamba Reasoning 3B — Hardware Requirements & GPU Compatibility

ChatReasoning

AI21 Jamba Reasoning 3B is a 3.2B-parameter open language model from AI21 Labs in the Jamba family. It supports a context window of up to 262,144 tokens. At Q4_K_M it needs about 2.25 GB of VRAM — see which GPUs and Macs can run it below.

2.9K downloads 133 likes262K context

Specifications

Publisher
AI21 Labs
Family
Jamba
Parameters
3.2B
Architecture
JambaForCausalLM
Context Length
262,144 tokens
Vocabulary Size
65,536
Release Date
2025-10-05
License
Apache 2.0

Get Started

How Much VRAM Does AI21 Jamba Reasoning 3B Need?

Select a quantization to see compatible GPUs below.

QuantizationBitsVRAM
Q2_Kest.3.401.7 GB
Q3_K_Mest.3.901.9 GB
Q4_K_Mest.4.802.3 GB
Q5_K_Mest.5.702.6 GB
Q6_Kest.6.603.0 GB
Q8_0est.8.003.5 GB
BF16est.16.006.7 GB

est.= calculated VRAM estimate; no published GGUF file found for that quantization yet. Other rows are verified against real community uploads.

Which GPUs Can Run AI21 Jamba Reasoning 3B?

Q4_K_M · 2.3 GB

AI21 Jamba Reasoning 3B (Q4_K_M) requires 2.3 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 3+ GB is recommended. Using the full 262K context window can add up to 3.7 GB, bringing total usage to 6.0 GB. 50 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.

Runs great

Plenty of headroom
NVIDIA GeForce RTX 5090~518 tok/sNVIDIA GeForce RTX 3090 Ti~291 tok/sNVIDIA GeForce RTX 4090~291 tok/sNVIDIA GeForce RTX 5080~277 tok/sNVIDIA GeForce RTX 3090~271 tok/sNVIDIA GeForce RTX 3080 Ti~264 tok/sNVIDIA GeForce RTX 5070 Ti~259 tok/sNVIDIA GeForce RTX 5090 Laptop GPU~259 tok/sAMD Radeon RX 7900 XTX~235 tok/sNVIDIA GeForce RTX 3080~220 tok/sNVIDIA GeForce RTX 4080 SUPER~213 tok/sNVIDIA GeForce RTX 4080~207 tok/sAMD Radeon RX 7900 XT~196 tok/sNVIDIA GeForce RTX 4070 Ti SUPER~194 tok/sNVIDIA GeForce RTX 5070~194 tok/sNVIDIA TITAN RTX~194 tok/sNVIDIA GeForce RTX 2080 Ti~178 tok/sNVIDIA GeForce RTX 3070 Ti~176 tok/sNVIDIA GeForce RTX 4090 Laptop GPU~166 tok/sAMD Radeon RX 9070~156 tok/sAMD Radeon RX 9070 XT~156 tok/sAMD Radeon RX 7800 XT~153 tok/sNVIDIA GeForce RTX 4070~146 tok/sNVIDIA GeForce RTX 4070 SUPER~146 tok/sNVIDIA GeForce RTX 4070 Ti~146 tok/sAMD Radeon RX 7900 GRE~141 tok/sNVIDIA GeForce GTX 1080 Ti~140 tok/sNVIDIA GeForce RTX 3060 Ti~129 tok/sNVIDIA GeForce RTX 3070~129 tok/sNVIDIA GeForce RTX 5060~129 tok/sNVIDIA GeForce RTX 5060 Ti 16GB~129 tok/sNVIDIA GeForce RTX 5060 Ti 8GB~129 tok/sAMD Radeon RX 6800~125 tok/sAMD Radeon RX 6800 XT~125 tok/sAMD Radeon RX 6900 XT~125 tok/sIntel Arc A770 16GB~124 tok/sIntel Arc A750~114 tok/sAMD Radeon RX 7700 XT~106 tok/sNVIDIA GeForce RTX 3060 12GB~104 tok/sIntel Arc B580~101 tok/sAMD Radeon RX 6700 XT~94 tok/sIntel Arc B570~84 tok/sNVIDIA GeForce RTX 4060 Ti 16GB~83 tok/sNVIDIA GeForce RTX 4060 Ti 8GB~83 tok/sNVIDIA GeForce RTX 4060~79 tok/sAMD Radeon RX 9060 XT 16GB~78 tok/sAMD Radeon RX 7600~70 tok/sAMD Radeon RX 7600 XT~70 tok/sNVIDIA GeForce RTX 3060 8GB~69 tok/sNVIDIA GeForce RTX 3050 8GB~65 tok/s

Which Devices Can Run AI21 Jamba Reasoning 3B?

Q4_K_M · 2.3 GB

59 devices with unified memory can run AI21 Jamba Reasoning 3B, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.

Runs great

Plenty of headroom
NVIDIA DGX H100~7742 tok/sNVIDIA DGX A100 640GB~4712 tok/sMac Studio (M3 Ultra, 256GB)~255 tok/sMac Studio (M3 Ultra, 512GB)~255 tok/sMac Studio (M3 Ultra, 96GB)~255 tok/sMac Pro M2 Ultra (192 GB)~249 tok/sMac Studio M2 Ultra (192 GB)~249 tok/sMacBook Pro 16" M5 Max (128 GB)~191 tok/sMac Studio M4 Max (128 GB)~170 tok/sMac Studio M4 Max (64 GB)~170 tok/sMacBook Pro 16" M4 Max (48 GB)~170 tok/sMacBook Pro 16" M4 Max (64 GB)~170 tok/sMac Studio M4 Max (36 GB)~127 tok/sMacBook Pro 14" M4 Max (36 GB)~127 tok/sMacBook Pro 16" M3 Max (48 GB)~127 tok/sMacBook Pro 14-inch (M5 Pro)~96 tok/sMac Mini M4 Pro (24 GB)~85 tok/sMac Mini M4 Pro (48 GB)~85 tok/sMacBook Pro 14" M4 Pro (24 GB)~85 tok/sMacBook Pro 16" M4 Pro (24 GB)~85 tok/sASUS Ascent GX10~79 tok/sNVIDIA DGX Spark~79 tok/sNVIDIA Jetson AGX Thor Developer Kit~79 tok/sAsus ROG Flow Z13 (2025, Ryzen AI Max+ 395, 128 GB)~74 tok/sBeelink GTR9 Pro (Ryzen AI Max+ 395, 128 GB)~74 tok/sFramework Desktop (Ryzen AI Max+ 395, 128 GB)~74 tok/sGMKtec EVO-X2 (Ryzen AI Max+ 395, 128 GB)~74 tok/sHP Z2 Mini G1a (Ryzen AI Max+ PRO 395, 128 GB)~74 tok/sHP ZBook Ultra G1a 14 (Ryzen AI Max+ PRO 395, 128 GB)~74 tok/sMinisforum MS-S1 MAX (Ryzen AI Max+ 395, 128 GB)~74 tok/sSnapdragon X2 Elite Extreme Copilot+ PC~66 tok/sNVIDIA Jetson AGX Orin 32GB~59 tok/sNVIDIA Jetson AGX Orin 64GB~59 tok/sMacBook Pro 14-inch (M5)~48 tok/siPad Pro M5 13" (16 GB)~48 tok/sSnapdragon X Elite Copilot+ PC~39 tok/sMac Mini M4 (16 GB)~37 tok/sMac Mini M4 (32 GB)~37 tok/sMacBook Air 13" M4 (16 GB)~37 tok/sMacBook Air 13" M4 (24 GB)~37 tok/sMacBook Air 15" M4 (16 GB)~37 tok/sMacBook Air 15" M4 (24 GB)~37 tok/sMacBook Pro 14" M4 (16 GB)~37 tok/siPad Pro M4 13" (16 GB)~37 tok/sMacBook Air 13" M3 (16 GB)~32 tok/sMacBook Air 13" M3 (24 GB)~32 tok/sMacBook Air 13" M3 (8 GB)~32 tok/sIntel Core Ultra 9 288V (Lunar Lake) Laptop~30 tok/sNVIDIA Jetson Orin NX 16GB~30 tok/sNVIDIA Jetson Orin Nano 8GB (Super)~30 tok/sAMD Ryzen AI 9 HX 370 (Strix Point) Laptop~29 tok/sApple iPhone 17 Pro~24 tok/siPhone 17 Pro Max~24 tok/siPhone 17~21 tok/siPhone Air~21 tok/siPhone 15 ProiPhone 15 Pro MaxiPhone 16 ProiPhone 16 Pro Max

Related Models

Frequently Asked Questions

How much VRAM does AI21 Jamba Reasoning 3B need?

AI21 Jamba Reasoning 3B requires 2.3 GB of VRAM at Q4_K_M, or 6.7 GB at BF16. Full 262K context adds up to 3.7 GB (6.0 GB total).

VRAM = Weights + KV Cache + Overhead

Weights = 3.2B × 4.8 bits ÷ 8 = 1.9 GB

KV Cache + Overhead 0.4 GB (at 2K context + ~0.3 GB framework)

KV Cache + Overhead 4.1 GB (at full 262K context)

VRAM usage by quantization

2.3 GB
6.0 GB

Learn more about VRAM estimation →

What's the best quantization for AI21 Jamba Reasoning 3B?

For AI21 Jamba Reasoning 3B, Q4_K_M (2.3 GB) offers the best balance of quality and VRAM usage. Q5_K_M (2.6 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 1.7 GB.

VRAM requirement by quantization

Q2_K
1.7 GB
Q4_K_M
2.3 GB
Q5_K_M
2.6 GB
Q6_K
3.0 GB
Q8_0
3.5 GB
BF16
6.7 GB

★ Recommended — best balance of quality and VRAM usage.

Learn more about quantization →

Can I run AI21 Jamba Reasoning 3B on a Mac?

AI21 Jamba Reasoning 3B requires at least 1.7 GB at Q2_K, which exceeds the unified memory of most consumer Macs. You would need a Mac Studio or Mac Pro with a high-memory configuration.

Can I run AI21 Jamba Reasoning 3B locally?

Yes — AI21 Jamba Reasoning 3B can run locally on consumer hardware. At Q4_K_M quantization it needs 2.3 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.

How fast is AI21 Jamba Reasoning 3B?

At Q4_K_M, AI21 Jamba Reasoning 3B can reach ~1956 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~291 tok/s. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.

tok/s = (bandwidth GB/s ÷ model GB) × efficiency

Example: NVIDIA B2008000 ÷ 2.3 × 0.65 = ~2311 tok/s

Estimated speed at Q4_K_M (2.3 GB)

~2311 tok/s
~291 tok/s
~2311 tok/s
~1956 tok/s

Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.

Learn more about tok/s estimation →

What's the download size of AI21 Jamba Reasoning 3B?

At Q4_K_M, the download is about 1.92 GB. The full-precision BF16 version is 6.39 GB. The smallest option (Q2_K) is 1.36 GB.

Which GPUs can run AI21 Jamba Reasoning 3B?

50 consumer GPUs can run AI21 Jamba Reasoning 3B at Q4_K_M (2.3 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT. 50 GPUs have plenty of headroom for comfortable inference.

Which devices can run AI21 Jamba Reasoning 3B?

59 devices with unified memory can run AI21 Jamba Reasoning 3B at Q4_K_M (2.3 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Apple iPhone 17 Pro, Asus ROG Flow Z13 (2025, Ryzen AI Max+ 395, 128 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.