Mac Studio M3 Ultra 256GBBudget pick
256 GB Unified (+128)819 GB/s (+546)
C
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.
~$6,999 MSRP
Can ai21labs AI21 Jamba2 3B run on NVIDIA DGX Spark 128GB?
YES — With F16
C43Usable
○Estimated from fit model
ai21labs AI21 Jamba2 3B needs ~20.8 GB VRAM. NVIDIA DGX Spark 128GB has 0 MB. With F16 quantization, expect ~37 tok/s.
Runtime: OllamaCapacity: RoomyBandwidth: LowStack: BasicBottleneck: 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.
ai21labs AI21 Jamba2 3B at Q4_K_M needs 3.4 GB — too much for NVIDIA DGX Spark 128GB (0.0 GB). Runs at F16 (20.8 GB) with maximum quality. 8 quantization levels fit.
Select quantization to explore
Q4_K_M (Medium quality) — 16.4 GB, 42.0 tok/s, Runs well
16.4 GB required108.8 GB available
15% VRAM used
Fit status
Runs well
Decode
42.0 tok/s
TTFT
4610 ms
Safe context
4.2M
Memory
16.4 GB / 108.8 GB
Memory breakdown
Weights1.8 GB
KV Cache0.4 GB
Runtime1.2 GB
Headroom13.1 GB
See how fast it feels
See how fast it feelsai21labs AI21 Jamba2 3B on NVIDIA DGX Spark 128GB
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: 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.
Shared-memory contention still exists
The OS, browser, and inference runtime all compete for the same physical memory pool, so real-world headroom is less forgiving than raw capacity suggests.
Best improvement path
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | F | Too heavy | 16.1 tok/s | 6554 ms | 4K |
| Coding | F | Too heavy | 16.1 tok/s | 12016 ms | 4K |
| Agentic Coding | F | Too heavy | 16.1 tok/s | 17478 ms | 4K |
| Reasoning | F | Too heavy | 16.1 tok/s | 14201 ms | 4K |
| RAG | F | Too heavy | 16.1 tok/s | 21848 ms | 4K |
Quantization options
How ai21labs AI21 Jamba2 3B (3B params) fits at each quantization level on NVIDIA DGX Spark 128GB (92.2 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 1.2 GB | Low | D39 |
Q3_K_S | 3 | 1.5 GB | Low | D39 |
NVFP4 | 4 |
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 startUpgrade options