Intel Arc A380 6GBBudget pick6 GB VRAM (+2)186 GB/s (+74)
C
Raises estimated decode speed by about 40%.42 tok/s decode
Raises estimated decode speed by about 40%.
Adds memory headroom for longer context windows and future model growth.
~$139 MSRP
ai21labs AI21 Jamba Reasoning 3B needs ~3.5 GB VRAM. Intel Arc A370M 4GB has 4.0 GB. With Q4_K_M quantization, expect ~30 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
30.0 tok/s
TTFT
6456 ms
Safe context
40K
Memory
3.5 GB / 4.0 GB
The raw memory story may look fine, but the software ecosystem is still a constraint here.
Runtime ecosystem is narrower than CUDA
Intel GPUs can look attractive on memory per dollar, but local AI tooling, kernels, and model coverage are still broader and easier on CUDA today.
Prefer CUDA if you want the path of least resistance
If your goal is maximum runtime coverage, easier troubleshooting, and better support for new local AI releases, CUDA is usually still the safer upgrade path.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Tight fit | 30.0 tok/s | 3521 ms | 40K |
| Coding | C | Tight fit | 30.0 tok/s | 6456 ms | 40K |
| Agentic Coding | C | Runs with offload | 30.0 tok/s | 9390 ms | 40K |
| Reasoning | C | Tight fit | 30.0 tok/s | 7629 ms | 40K |
| RAG | C | Runs with offload | 30.0 tok/s | 11738 ms | 40K |
How ai21labs AI21 Jamba Reasoning 3B (3B params) fits at each quantization level on Intel Arc A370M 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 |
Copy-paste commands to run ai21labs AI21 Jamba Reasoning 3B on your machine.
Run
lms load hf-bartowski--ai21labs-ai21-jamba-reasoning-3b-gguf && lms server startUpgrade options
Intel Arc A380 6GBBudget pickRaises estimated decode speed by about 40%.
Adds memory headroom for longer context windows and future model growth.
~$139 MSRP
Intel Arc Pro A40 6GBBest valueRaises estimated decode speed by about 40%.
Adds memory headroom for longer context windows and future model growth.
~$249 MSRP
Yes, Intel Arc A370M 4GB can run ai21labs AI21 Jamba Reasoning 3B with a C grade (Tight fit). Expected decode speed: 30.0 tok/s.
ai21labs AI21 Jamba Reasoning 3B (3B parameters) requires approximately 3.5 GB of memory with Q4_K_M quantization.
The recommended quantization for ai21labs AI21 Jamba Reasoning 3B is Q4_K_M, which balances quality and memory efficiency.
On Intel Arc A370M 4GB, ai21labs AI21 Jamba Reasoning 3B achieves approximately 30.0 tokens per second decode speed with a time-to-first-token of 6456ms using Q4_K_M quantization.
For coding workloads, ai21labs AI21 Jamba Reasoning 3B on Intel Arc A370M 4GB receives a C grade with 30.0 tok/s and 40K context.
On Intel Arc A370M 4GB, ai21labs AI21 Jamba Reasoning 3B can safely use up to 40K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/hf-bartowski--ai21labs-ai21-jamba-reasoning-3b-gguf-on-arc-a370m-4gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
1.7 GB |
| Medium |
| C55 |
Q4_K_MBest for your GPU | 4 | 1.8 GB | Medium | C55 |
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 |
Prefer CUDA if you want the path of least resistance. If your goal is maximum runtime coverage, easier troubleshooting, and better support for new local AI releases, CUDA is usually still the safer upgrade path.
Often yes, if your goal is the easiest setup and the widest runtime support. Intel can offer attractive memory capacity, but CUDA still tends to win on tooling maturity, guides, kernels, and model coverage for local AI.