Qwen3.5 27B needs ~33.3 GB VRAM. Intel Data Center GPU Max 1550 128GB has 128.0 GB. With Q4_K_M quantization, expect ~122 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
122.4 tok/s
TTFT
1582 ms
Safe context
495K
Memory
33.3 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 | C | Runs well | 122.4 tok/s | 863 ms | 495K |
| Coding | C | Runs well | 122.4 tok/s | 1582 ms | 495K |
| Agentic Coding | C | Runs well | 122.4 tok/s | 2301 ms | 495K |
| Reasoning | C | Runs well | 122.4 tok/s | 1869 ms | 495K |
| RAG | C | Runs well | 122.4 tok/s | 2876 ms | 495K |
How Qwen3.5 27B (27B 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 | 10.5 GB | Low | D39 |
Q3_K_S | 3 | 13.2 GB | Low | D39 |
NVFP4 | 4 |
Copy-paste commands to run Qwen3.5 27B on your machine.
Run
docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
--hf-repo "unsloth/Qwen3.5-27B-GGUF" \
--hf-file "Qwen3.5-27B-GGUF-Q4_K_M.gguf" \
-c 4096 -ngl 99Yes, Intel Data Center GPU Max 1550 128GB can run Qwen3.5 27B with a C grade (Runs well). Expected decode speed: 122.4 tok/s.
Qwen3.5 27B (27B parameters) requires approximately 33.3 GB of memory with Q4_K_M quantization.
The recommended quantization for Qwen3.5 27B is Q4_K_M, which balances quality and memory efficiency.
On Intel Data Center GPU Max 1550 128GB, Qwen3.5 27B achieves approximately 122.4 tokens per second decode speed with a time-to-first-token of 1582ms using Q4_K_M quantization.
For coding workloads, Qwen3.5 27B on Intel Data Center GPU Max 1550 128GB receives a C grade with 122.4 tok/s and 495K context.
On Intel Data Center GPU Max 1550 128GB, Qwen3.5 27B can safely use up to 495K 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-unsloth--qwen3-5-27b-gguf-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:
15.1 GB |
| Medium |
| D39 |
Q4_K_M | 4 | 16.5 GB | Medium | D39 |
Q5_K_M | 5 | 19.4 GB | High | D39 |
Q6_K | 6 | 22.1 GB | High | D40 |
Q8_0 | 8 | 28.9 GB | Very High | C41 |
F16Best for your GPU | 16 | 55.4 GB | Maximum | C45 |
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.