Qwen3-Coder-Next needs ~64.0 GB VRAM. Intel Data Center GPU Max 1550 128GB has 128.0 GB. With Q4_K_M quantization, expect ~136 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
136.1 tok/s
TTFT
1422 ms
Safe context
256K
Memory
64.0 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 | 136.1 tok/s | 776 ms | 256K |
| Coding | S | Runs well | 136.1 tok/s | 1422 ms | 256K |
| Agentic Coding | S | Runs well | 136.1 tok/s | 2068 ms | 256K |
| Reasoning | S | Runs well | 136.1 tok/s | 1681 ms | 256K |
| RAG | S | Runs well | 136.1 tok/s | 2586 ms | 256K |
How Qwen3-Coder-Next (80B 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 | 31.2 GB | Low | A81 |
Q3_K_S | 3 | 39.2 GB | Low | A82 |
NVFP4 | 4 |
Copy-paste commands to run Qwen3-Coder-Next on your machine.
Run
ollama run qwen3-coder-nextYour hardware
| Model | Params | Grade | Decode | Capabilities |
|---|---|---|---|---|
 Devstral 2 123B Instruct | 123B | S | 29.2 tok/s | |
 Qwen 3.5 122B A10B | 122B | S |
Yes, Intel Data Center GPU Max 1550 128GB can run Qwen3-Coder-Next with a S grade (Runs well). Expected decode speed: 136.1 tok/s.
Qwen3-Coder-Next (80B parameters) requires approximately 64.0 GB of memory with Q4_K_M quantization.
The recommended quantization for Qwen3-Coder-Next is Q4_K_M, which balances quality and memory efficiency.
On Intel Data Center GPU Max 1550 128GB, Qwen3-Coder-Next achieves approximately 136.1 tokens per second decode speed with a time-to-first-token of 1422ms using Q4_K_M quantization.
For coding workloads, Qwen3-Coder-Next on Intel Data Center GPU Max 1550 128GB receives a S grade with 136.1 tok/s and 256K context.
On Intel Data Center GPU Max 1550 128GB, Qwen3-Coder-Next 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-coder-next-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:
44.8 GB |
| Medium |
| A83 |
Q4_K_M | 4 | 48.8 GB | Medium | A84 |
Q5_K_M | 5 | 57.6 GB | High | S85 |
Q6_K | 6 | 65.6 GB | High | S87 |
Q8_0Best for your GPU | 8 | 85.6 GB | Very High | S88 |
F16 | 16 | 164.0 GB | Maximum | F0 |
| 81 tok/s |
| Mistral Small 4 119B | 119B | S | 87.9 tok/s |
 GPT-OSS 120B | 117B | S | 30.7 tok/s |
 Command A 111B | 111B | S | 32.5 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.