Qwen3-VL 30B A3B Instruct needs ~34.4 GB VRAM. Gaudi 3 128GB has 128.0 GB. With Q4_K_M quantization, expect ~405 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
Runs well
Decode
405.0 tok/s
TTFT
478 ms
Safe context
256K
Memory
34.4 GB / 128.0 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 | S | Runs well | 405.0 tok/s | 350 ms | 256K |
| Coding | S | Runs well | 405.0 tok/s | 478 ms | 256K |
| Agentic Coding | S | Runs well | 405.0 tok/s | 695 ms | 256K |
| Reasoning | S | Runs well | 405.0 tok/s | 565 ms | 256K |
| RAG | S | Runs well | 405.0 tok/s | 869 ms | 256K |
How Qwen3-VL 30B A3B Instruct (30B params) fits at each quantization level on Gaudi 3 128GB (128.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 11.7 GB | Low | A81 |
Q3_K_S | 3 | 14.7 GB | Low | A81 |
NVFP4 | 4 |
Copy-paste commands to run Qwen3-VL 30B A3B Instruct on your machine.
Run
lms load Qwen3-VL-30B-A3B-Instruct && lms server startYour hardware
| Model | Params | Grade | Decode | Capabilities |
|---|---|---|---|---|
 Devstral 2 123B Instruct | 123B | S | 37.5 tok/s | |
 Qwen3-Coder 30B A3B Instruct | 30.5B | S |
Yes, Gaudi 3 128GB can run Qwen3-VL 30B A3B Instruct with a S grade (Runs well). Expected decode speed: 405.0 tok/s.
Qwen3-VL 30B A3B Instruct (30B parameters) requires approximately 34.4 GB of memory with Q4_K_M quantization.
The recommended quantization for Qwen3-VL 30B A3B Instruct is Q4_K_M, which balances quality and memory efficiency.
On Gaudi 3 128GB, Qwen3-VL 30B A3B Instruct achieves approximately 405.0 tokens per second decode speed with a time-to-first-token of 478ms using Q4_K_M quantization.
For coding workloads, Qwen3-VL 30B A3B Instruct on Gaudi 3 128GB receives a S grade with 405.0 tok/s and 256K context.
On Gaudi 3 128GB, Qwen3-VL 30B A3B Instruct can safely use up to 256K tokens of context. The model's official context limit is 256K, 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/qwen-3-vl-30b-a3b-on-gaudi-3-128gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
16.8 GB |
| Medium |
| A81 |
Q4_K_M | 4 | 18.3 GB | Medium | A81 |
Q5_K_M | 5 | 21.6 GB | High | A81 |
Q6_K | 6 | 24.6 GB | High | A82 |
Q8_0 | 8 | 32.1 GB | Very High | A83 |
F16Best for your GPU | 16 | 61.5 GB | Maximum | S88 |
| 391.6 tok/s |
| Qwen 3.5 122B A10B | 122B | S | 104.1 tok/s |
| Qwen 3.6 35B A3B | 35B | S | 329.1 tok/s |
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.