DeepSeek Coder V2 16B needs ~26.8 GB VRAM. Intel Data Center GPU Max 1550 128GB has 128.0 GB. With Q4_K_M quantization, expect ~492 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
491.8 tok/s
TTFT
394 ms
Safe context
131K
Memory
26.8 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 | A | Runs well | 491.8 tok/s | 350 ms | 131K |
| Coding | A | Runs well | 491.8 tok/s | 394 ms | 131K |
| Agentic Coding | A | Runs well | 491.8 tok/s | 573 ms | 131K |
| Reasoning | A | Runs well | 491.8 tok/s | 465 ms | 131K |
| RAG | A | Runs well | 491.8 tok/s | 716 ms | 131K |
How DeepSeek Coder V2 16B (16B 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 | 6.2 GB | Low | B67 |
Q3_K_S | 3 | 7.8 GB | Low | B67 |
NVFP4 | 4 |
Copy-paste commands to run DeepSeek Coder V2 16B on your machine.
Run
lms load DeepSeek-Coder-V2-Lite-Instruct && lms server startYour hardware
| Model | Params | Grade | Decode | Capabilities |
|---|---|---|---|---|
 Devstral 2 123B Instruct | 123B | S | 29.2 tok/s | |
 Qwen3-Coder 30B A3B Instruct | 30.5B | S |
Yes, Intel Data Center GPU Max 1550 128GB can run DeepSeek Coder V2 16B with a A grade (Runs well). Expected decode speed: 491.8 tok/s.
DeepSeek Coder V2 16B (16B parameters) requires approximately 26.8 GB of memory with Q4_K_M quantization.
The recommended quantization for DeepSeek Coder V2 16B is Q4_K_M, which balances quality and memory efficiency.
On Intel Data Center GPU Max 1550 128GB, DeepSeek Coder V2 16B achieves approximately 491.8 tokens per second decode speed with a time-to-first-token of 394ms using Q4_K_M quantization.
For coding workloads, DeepSeek Coder V2 16B on Intel Data Center GPU Max 1550 128GB receives a A grade with 491.8 tok/s and 131K context.
On Intel Data Center GPU Max 1550 128GB, DeepSeek Coder V2 16B 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/deepseek-coder-v2-16b-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:
9.0 GB |
| Medium |
| B67 |
Q4_K_M | 4 | 9.8 GB | Medium | B67 |
Q5_K_M | 5 | 11.5 GB | High | B67 |
Q6_K | 6 | 13.1 GB | High | B67 |
Q8_0 | 8 | 17.1 GB | Very High | B67 |
F16Best for your GPU | 16 | 32.8 GB | Maximum | B69 |
| 304.8 tok/s |
| Qwen 3.5 27B | 27B | S | 132.2 tok/s |
| Qwen 3.6 27B | 27B | S | 82.4 tok/s |
| Qwen 3.5 122B A10B | 122B | S | 81 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.