Mixtral 8x7B needs ~44.3 GB VRAM. Intel Data Center GPU Max 1550 128GB has 128.0 GB. With Q4_K_M quantization, expect ~135 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
145.0 tok/s
TTFT
1335 ms
Safe context
33K
Memory
44.3 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 | B | Runs well | 145.0 tok/s | 728 ms | 33K |
| Coding | B | Runs well | 134.9 tok/s | 1435 ms | 33K |
| Agentic Coding | B | Runs well | 145.0 tok/s | 1942 ms | 33K |
| Reasoning | B | Runs well | 145.0 tok/s | 1578 ms | 33K |
| RAG | B | Runs well | 145.0 tok/s | 2427 ms | 33K |
How Mixtral 8x7B (47B 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 | 18.3 GB | Low | C54 |
Q3_K_S | 3 | 23.0 GB | Low | C55 |
NVFP4 | 4 |
Copy-paste commands to run Mixtral 8x7B on your machine.
Run
ollama run mixtralYes, Intel Data Center GPU Max 1550 128GB can run Mixtral 8x7B with a B grade (Runs well). Expected decode speed: 134.9 tok/s.
Mixtral 8x7B (47B parameters) requires approximately 44.3 GB of memory with Q4_K_M quantization.
The recommended quantization for Mixtral 8x7B is Q4_K_M, which balances quality and memory efficiency.
On Intel Data Center GPU Max 1550 128GB, Mixtral 8x7B achieves approximately 134.9 tokens per second decode speed with a time-to-first-token of 1435ms using Q4_K_M quantization.
For coding workloads, Mixtral 8x7B on Intel Data Center GPU Max 1550 128GB receives a B grade with 134.9 tok/s and 33K context.
On Intel Data Center GPU Max 1550 128GB, Mixtral 8x7B can safely use up to 33K tokens of context. The model's official context limit is 33K, 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/mixtral-8x7b-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:
26.3 GB |
| Medium |
| B55 |
Q4_K_M | 4 | 28.7 GB | Medium | B56 |
Q5_K_M | 5 | 33.8 GB | High | B57 |
Q6_K | 6 | 38.5 GB | High | B57 |
Q8_0 | 8 | 50.3 GB | Very High | B59 |
F16Best for your GPU | 16 | 96.4 GB | Maximum | B63 |
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.