Can AI21 Jamba Reasoning 3B run on NVIDIA V100 32GB?

YES — Runs Great

C43Usable

Estimated from fit model

AI21 Jamba Reasoning 3B needs ~6.6 GB VRAM. NVIDIA V100 32GB has 32.0 GB. With Q4_K_M quantization, expect ~42 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: HighStack: BasicBottleneck: Balanced

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) — 6.6 GB, 42.0 tok/s, Runs well

6.6 GB required32.0 GB available

21% VRAM used

Fit status

Runs well

Decode

42.0 tok/s

TTFT

4610 ms

Safe context

1.2M

Memory

6.6 GB / 32.0 GB

Memory breakdown

Weights1.8 GB

KV Cache0.4 GB

Runtime1.2 GB

Headroom3.2 GB

See how fast it feels

See how fast it feelsAI21 Jamba Reasoning 3B on NVIDIA V100 32GB
1st promptCold start — includes initialization
>Solve: If a train travels at 60 mph for 2.5 hours, how far does it go?
Let me work through this step by step.

Given information:
  - Speed = 60 mph (miles per hour)
  - Time  = 2.5 hours

Formula: Distance = Speed x Time

Calculation:
  Distance = 60 mph x 2.5 h
           = 150 miles

The train travels 150 miles.

Double-check: at 60 mph the train covers 60 miles each hour. In half an hour it covers 30 miles. So in 2.5 hours: 60 + 60 + 30 = 150 miles. Confirmed.
2nd promptWarm — model ready, no init overhead
>What if the train stops for 20 minutes halfway?
The stop doesn't change the total distance covered while moving, but it affects total trip time.

Breakdown:
  - First half: 1.25 hours at 60 mph = 75 miles
  - Stop: 20 minutes (no distance)
  - Second half: 1.25 hours at 60 mph = 75 miles

Total distance: 150 miles (same)
Total time: 2.5 h + 0.33 h = 2.83 hours
Average speed: 150 / 2.83 = 53 mph

The distance stays the same but average speed drops to 53 mph because of the stop.
Estimated: 42.0 tok/s decode · 4.6s TTFT (warm) · 105 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

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Runs well	42.0 tok/s	2514 ms	1.2M
Coding	C	Runs well	42.0 tok/s	4610 ms	1.2M
Agentic Coding	C	Runs well	42.0 tok/s	6705 ms	1.2M
Reasoning	C	Runs well	42.0 tok/s	5448 ms	1.2M
RAG	C	Runs well	42.0 tok/s	8381 ms	1.2M

Quantization options

How AI21 Jamba Reasoning 3B (3B params) fits at each quantization level on NVIDIA V100 32GB (32.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	1.2 GB	Low	C42
Q3_K_S	3	1.5 GB	Low	C42
NVFP4	4

Get started

Copy-paste commands to run AI21 Jamba Reasoning 3B on your machine.

Run

lms load hf-ai21labs--ai21-jamba-reasoning-3b-gguf && lms server start

Upgrade options

Hardware that runs AI21 Jamba Reasoning 3B well

Mac mini M4 64GBBudget pick

64 GB Unified (+32)

Adds memory headroom for longer context windows and future model growth.42 tok/s decode

Adds memory headroom for longer context windows and future model growth.

~$1,099 MSRP

MacBook Pro M4 Pro 64GBBest value

64 GB Unified (+32)

Adds memory headroom for longer context windows and future model growth.42 tok/s decode

Adds memory headroom for longer context windows and future model growth.

~$1,599 MSRP

Frequently asked questions

Can NVIDIA V100 32GB run AI21 Jamba Reasoning 3B?

Yes, NVIDIA V100 32GB can run AI21 Jamba Reasoning 3B with a C grade (Runs well). Expected decode speed: 42.0 tok/s.

How much VRAM does AI21 Jamba Reasoning 3B need?

AI21 Jamba Reasoning 3B (3B parameters) requires approximately 6.6 GB of memory with Q4_K_M quantization.

What is the best quantization for AI21 Jamba Reasoning 3B?

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

What speed will AI21 Jamba Reasoning 3B run at on NVIDIA V100 32GB?

On NVIDIA V100 32GB, AI21 Jamba Reasoning 3B achieves approximately 42.0 tokens per second decode speed with a time-to-first-token of 4610ms using Q4_K_M quantization.

Can NVIDIA V100 32GB run AI21 Jamba Reasoning 3B for coding?

For coding workloads, AI21 Jamba Reasoning 3B on NVIDIA V100 32GB receives a C grade with 42.0 tok/s and 1.2M context.

What context window can AI21 Jamba Reasoning 3B use on NVIDIA V100 32GB?

On NVIDIA V100 32GB, AI21 Jamba Reasoning 3B can safely use up to 1.2M tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for NVIDIA V100 32GB See all hardware for AI21 Jamba Reasoning 3B

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