Qwen 3.5 122B A10B needs ~90.6 GB VRAM. Intel Data Center GPU Max 1550 128GB has 128.0 GB. With Q4_K_M quantization, expect ~74 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
81.0 tok/s
TTFT
2389 ms
Safe context
131K
Memory
90.6 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 | 81.0 tok/s | 1303 ms | 131K |
| Coding | S | Runs well | 74.1 tok/s | 2613 ms | 131K |
| Agentic Coding | S | Runs well | 81.0 tok/s | 3474 ms | 131K |
| Reasoning | S | Runs well | 81.0 tok/s | 2823 ms | 131K |
| RAG | S | Runs well | 81.0 tok/s | 4343 ms | 131K |
How Qwen 3.5 122B A10B (122B params) fits at each quantization level on Intel Data Center GPU Max 1550 128GB (128.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 47.6 GB | Low | S87 |
Q3_K_S | 3 | 59.8 GB | Low | S89 |
NVFP4 | 4 |
Copy-paste commands to run Qwen 3.5 122B A10B on your machine.
Run
lms load Qwen3.5-122B-A10B-Instruct && lms server startYour hardware
| Model | Params | Grade | Decode | Capabilities |
|---|---|---|---|---|
 Devstral 2 123B Instruct | 123B | S | 29.2 tok/s |
Yes, Intel Data Center GPU Max 1550 128GB can run Qwen 3.5 122B A10B with a S grade (Runs well). Expected decode speed: 74.1 tok/s.
Qwen 3.5 122B A10B (122B parameters) requires approximately 90.6 GB of memory with Q4_K_M quantization.
The recommended quantization for Qwen 3.5 122B A10B is Q4_K_M, which balances quality and memory efficiency.
On Intel Data Center GPU Max 1550 128GB, Qwen 3.5 122B A10B achieves approximately 74.1 tokens per second decode speed with a time-to-first-token of 2613ms using Q4_K_M quantization.
For coding workloads, Qwen 3.5 122B A10B on Intel Data Center GPU Max 1550 128GB receives a S grade with 74.1 tok/s and 131K context.
On Intel Data Center GPU Max 1550 128GB, Qwen 3.5 122B A10B can safely use up to 131K tokens of context. The model's official context limit is 131K, 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-3.5-122b-a10b-on-max-1550-128gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
68.3 GB |
| Medium |
| S90 |
Q4_K_M | 4 | 74.4 GB | Medium | S90 |
Q5_K_M | 5 | 87.8 GB | High | S90 |
Q6_KBest for your GPU | 6 | 100.0 GB | High | S90 |
Q8_0 | 8 | 130.5 GB | Very High | F0 |
F16 | 16 | 250.1 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.