GTX 1660 Super 6GBBudget pick6 GB VRAM (+2)336 GB/s (+144)
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.
~$229 MSRP
ai21labs AI21 Jamba2 3B needs ~3.8 GB VRAM. RTX 3050 Ti Laptop 4GB has 4.0 GB. With Q4_K_M quantization, expect ~42 tok/s.
Operating mode
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.
Select quantization to explore
Fit status
Tight fit
Decode
42.0 tok/s
TTFT
4610 ms
Safe context
26K
Memory
3.8 GB / 4.0 GB
This setup is broadly balanced for this model.
Very little memory headroom
You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Tight fit | 42.0 tok/s | 2514 ms | 26K |
| Coding | C | Tight fit | 42.0 tok/s | 4610 ms | 26K |
| Agentic Coding | C | Runs with offload (needs ~0.1 GB host RAM) | 42.0 tok/s | 6705 ms | 26K |
| Reasoning | C | Tight fit | 42.0 tok/s | 5448 ms | 26K |
| RAG | C | Runs with offload (needs ~0.1 GB host RAM) | 42.0 tok/s | 8381 ms | 26K |
Inference speed
Estimated decode speed (tokens/sec) for ai21labs AI21 Jamba2 3B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~57 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.
| GPU / Mac | Memory | Quant | Speed (tok/s) | Fits? |
|---|---|---|---|---|
| RTX 5090 32GB | 32 GB | Q4_K_M | 57.0 | Fits |
| RTX 4090 24GB | 24 GB | Q4_K_M | 48.0 | Fits |
| RTX 4080 Super 16GB | 16 GB | Q4_K_M | 48.0 | Fits |
| RTX 3090 24GB | 24 GB | Q4_K_M | 42.0 | Fits |
| RTX 4070 12GB | 12 GB | Q4_K_M | 42.0 | Fits |
| RTX 3060 12GB | 12 GB | Q4_K_M | 42.0 | Fits |
| RTX 4060 8GB | 8 GB | Q4_K_M | 42.0 | Fits |
RX 7900 XTX 24GB | 24 GB | Q4_K_M | 42.0 | Fits |
MacBook Pro M4 Max 128GB | 128 GB | Q4_K_M | 42.0 | Fits |
Mac Studio M3 Ultra 256GB | 256 GB | Q4_K_M | 42.0 | Fits |
Mac Studio M2 Ultra 128GB | 128 GB | Q4_K_M | 42.0 | Fits |
Mac Studio M1 Ultra 128GB | 128 GB | Q4_K_M | 42.0 | Fits |
MacBook Pro M4 Max 64GB | 64 GB | Q4_K_M | 42.0 | Fits |
MacBook Pro M3 Max 64GB | 64 GB | Q4_K_M | 42.0 | Fits |
MacBook Pro M1 Max 64GB | 64 GB | Q4_K_M | 42.0 | Fits |
MacBook Pro M4 Pro 48GB | 48 GB | Q4_K_M | 42.0 | Fits |
Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.
How ai21labs AI21 Jamba2 3B (3B params) fits at each quantization level on RTX 3050 Ti Laptop 4GB (4.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 1.2 GB | Low | B55 |
Q3_K_S | 3 | 1.5 GB | Low | C55 |
NVFP4 | 4 | 1.7 GB | Medium | C55 |
Q4_K_MBest for your GPU | 4 | 1.8 GB | Medium | C54 |
Q5_K_M | 5 | 2.2 GB | High | F0 |
Q6_K | 6 | 2.5 GB | High | F0 |
Q8_0 | 8 | 3.2 GB | Very High | F0 |
F16 | 16 | 6.1 GB | Maximum | F0 |
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
GTX 1660 Super 6GBBudget pickAdds memory headroom for longer context windows and future model growth.
~$229 MSRP
GTX 1060 6GBBest valueAdds memory headroom for longer context windows and future model growth.
~$249 MSRP
GTX 1660 Ti 6GBNVIDIA upgradeAdds memory headroom for longer context windows and future model growth.
~$279 MSRP
Yes, RTX 3050 Ti Laptop 4GB can run ai21labs AI21 Jamba2 3B with a C grade (Tight fit). Expected decode speed: 42.0 tok/s.
ai21labs AI21 Jamba2 3B (3B parameters) requires approximately 3.8 GB of memory with Q4_K_M quantization.
The recommended quantization for ai21labs AI21 Jamba2 3B is Q4_K_M, which balances quality and memory efficiency.
On RTX 3050 Ti Laptop 4GB, ai21labs AI21 Jamba2 3B achieves approximately 42.0 tokens per second decode speed with a time-to-first-token of 4610ms using Q4_K_M quantization.
For coding workloads, ai21labs AI21 Jamba2 3B on RTX 3050 Ti Laptop 4GB receives a C grade with 42.0 tok/s and 26K context.
On RTX 3050 Ti Laptop 4GB, ai21labs AI21 Jamba2 3B can safely use up to 26K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
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-rtx-3050-ti-laptop-4gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview: