Mac Studio M3 Ultra 256GBBudget pick
256 GB Unified (+128)
C
Adds memory headroom for longer context windows and future model growth.98 tok/s decode
Adds memory headroom for longer context windows and future model growth.
~$6,999 MSRP
DeepSeek LLM 7B needs ~25.3 GB VRAM. Intel Data Center GPU Max 1550 128GB has 128.0 GB. With Q4_K_M quantization, expect ~98 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
98.0 tok/s
TTFT
1976 ms
Safe context
4K
Memory
25.3 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 | 98.0 tok/s | 1078 ms | 4K |
| Coding | C | Runs well | 98.0 tok/s | 1976 ms | 4K |
| Agentic Coding | C | Runs well | 98.0 tok/s | 2873 ms | 4K |
| Reasoning | C | Runs well | 98.0 tok/s | 2335 ms | 4K |
| RAG | C | Runs well | 98.0 tok/s | 3592 ms | 4K |
How DeepSeek LLM 7B (7B 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 | 2.7 GB | Low | D37 |
Q3_K_S | 3 | 3.4 GB | Low | D37 |
NVFP4 | 4 |
Copy-paste commands to run DeepSeek LLM 7B on your machine.
Run
ollama run deepseek-llmUpgrade options
Yes, Intel Data Center GPU Max 1550 128GB can run DeepSeek LLM 7B with a C grade (Runs well). Expected decode speed: 98.0 tok/s.
DeepSeek LLM 7B (7B parameters) requires approximately 25.3 GB of memory with Q4_K_M quantization.
The recommended quantization for DeepSeek LLM 7B is Q4_K_M, which balances quality and memory efficiency.
On Intel Data Center GPU Max 1550 128GB, DeepSeek LLM 7B achieves approximately 98.0 tokens per second decode speed with a time-to-first-token of 1976ms using Q4_K_M quantization.
For coding workloads, DeepSeek LLM 7B on Intel Data Center GPU Max 1550 128GB receives a C grade with 98.0 tok/s and 4K context.
On Intel Data Center GPU Max 1550 128GB, DeepSeek LLM 7B can safely use up to 4K tokens of context. The model's official context limit is 4K, 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-llm-7b-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:
3.9 GB |
| Medium |
| D37 |
Q4_K_M | 4 | 4.3 GB | Medium | D37 |
Q5_K_M | 5 | 5.0 GB | High | D37 |
Q6_K | 6 | 5.7 GB | High | D37 |
Q8_0 | 8 | 7.5 GB | Very High | D37 |
F16Best for your GPU | 16 | 14.3 GB | Maximum | D37 |
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.