Will It Run AI

Can ai21labs AI21 Jamba2 3B run on NVIDIA L20 48GB?

YES — Runs Great

C43Usable
Estimated from fit model

ai21labs AI21 Jamba2 3B needs ~7.9 GB VRAM. NVIDIA L20 48GB has 48.0 GB. With Q4_K_M quantization, expect ~48 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: StandardBottleneck: Balanced
Share:

Operating mode

Choose the run profile you care about

Interactive favors responsiveness, while light API and scale-out lean harder on serving readiness. The fit stays the same, but the recommendation lens changes.

Current mode

Balanced

Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 7.9 GB, 48.0 tok/s, Runs well
7.9 GB required48.0 GB available
16% VRAM used

Fit status

Runs well

Decode

48.0 tok/s

TTFT

4033 ms

Safe context

1.8M

Memory

7.9 GB / 48.0 GB

Memory breakdown

Weights1.8 GB
KV Cache0.4 GB
Runtime0.9 GB
Headroom4.8 GB

See how fast it feels

See how fast it feelsai21labs AI21 Jamba2 3B on NVIDIA L20 48GB
1st promptCold start — includes initialization
>What is local AI inference?
Local AI inference means running an AI language model directly on your own hardware — your laptop, desktop, or server — instead of sending requests to a remote cloud API. When you run inference locally the model weights are loaded into your GPU or unified memory. Each token you generate requires reading those weights from memory, so memory bandwidth is the main bottleneck for decode speed. Key benefits of running locally: - Full privacy: your prompts never leave your machine - No per-token cost or rate limits - Works offline once the model is downloaded - Latency depends only on your hardware
2nd promptWarm — model ready, no init overhead
>How much VRAM do I need?
It depends on the model size and quantization level. A rough rule of thumb: Model size Q4 (4-bit) Q8 (8-bit) FP16 7B params ~4.3 GB ~7.5 GB ~14 GB 13B params ~7.9 GB ~13.9 GB ~26 GB 70B params ~42.7 GB ~74.9 GB ~140 GB Most people use 4-bit quantization (Q4_K_M) which gives 90-95% of full quality at a fraction of the memory. A 24 GB GPU can comfortably run most 7B-13B models.
Estimated: 48.0 tok/s decode · 4.0s TTFT (warm) · 120 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

No major red flags

This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.

Best improvement path

Performance by workload

WorkloadGradeFitDecodeTTFTContext
ChatCRuns well48.0 tok/s2200 ms1.8M
CodingCRuns well48.0 tok/s4033 ms1.8M
Agentic CodingCRuns well48.0 tok/s5867 ms1.8M
ReasoningCRuns well48.0 tok/s4767 ms1.8M
RAGCRuns well48.0 tok/s7333 ms1.8M

Quantization options

How ai21labs AI21 Jamba2 3B (3B params) fits at each quantization level on NVIDIA L20 48GB (48.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
1.2 GB
LowC41
Q3_K_S
3
1.5 GB
LowC41
NVFP4
4
1.7 GB
MediumC41
Q4_K_M
4
1.8 GB
MediumC41
Q5_K_M
5
2.2 GB
HighC41
Q6_K
6
2.5 GB
HighC41
Q8_0
8
3.2 GB
Very HighC41
F16Best for your GPU
16
6.1 GB
MaximumC41

Get started

Copy-paste commands to run ai21labs AI21 Jamba2 3B on your machine.

Run

lms load hf-bartowski--ai21labs-ai21-jamba2-3b-gguf && lms server start

Opciones de mejora

Hardware que ejecuta bien ai21labs AI21 Jamba2 3B

MacBook Pro M4 Max 96GBOpción económica
96 GB Unified (+48)
C
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.42 tok/s decodificación

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$2,499 MSRP

MacBook Pro M2 Max 96GBMejor relación calidad-precio
96 GB Unified (+48)
C
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.42 tok/s decodificación

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$3,199 MSRP

Frequently asked questions

Can NVIDIA L20 48GB run ai21labs AI21 Jamba2 3B?

Yes, NVIDIA L20 48GB can run ai21labs AI21 Jamba2 3B with a C grade (Runs well). Expected decode speed: 48.0 tok/s.

How much VRAM does ai21labs AI21 Jamba2 3B need?

ai21labs AI21 Jamba2 3B (3B parameters) requires approximately 7.9 GB of memory with Q4_K_M quantization.

What is the best quantization for ai21labs AI21 Jamba2 3B?

The recommended quantization for ai21labs AI21 Jamba2 3B is Q4_K_M, which balances quality and memory efficiency.

What speed will ai21labs AI21 Jamba2 3B run at on NVIDIA L20 48GB?

On NVIDIA L20 48GB, ai21labs AI21 Jamba2 3B achieves approximately 48.0 tokens per second decode speed with a time-to-first-token of 4033ms using Q4_K_M quantization.

Can NVIDIA L20 48GB run ai21labs AI21 Jamba2 3B for coding?

For coding workloads, ai21labs AI21 Jamba2 3B on NVIDIA L20 48GB receives a C grade with 48.0 tok/s and 1.8M context.

What context window can ai21labs AI21 Jamba2 3B use on NVIDIA L20 48GB?

On NVIDIA L20 48GB, ai21labs AI21 Jamba2 3B can safely use up to 1.8M tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for NVIDIA L20 48GBSee all hardware for ai21labs AI21 Jamba2 3B
Embed this result▼

Paste this snippet into any page to show a live fit card.

<iframe src="https://willitrunai.com/embed/hf-bartowski--ai21labs-ai21-jamba2-3b-gguf-on-l20-48gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: