Will It Run AI

Can Mixtral 8x7B run on Intel Data Center GPU Max 1550 128GB?

YES — Runs Great

B65Good
Estimated from fit model

Mixtral 8x7B needs ~44.3 GB VRAM. Intel Data Center GPU Max 1550 128GB has 128.0 GB. With Q4_K_M quantization, expect ~145 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: StandardBottleneck: Balanced
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Operating mode

Choose the run profile you care about

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.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 44.3 GB, 145.0 tok/s, Runs well
44.3 GB required128.0 GB available
35% VRAM used

Fit status

Runs well

Decode

145.0 tok/s

TTFT

1335 ms

Safe context

33K

Memory

44.3 GB / 128.0 GB

Memory breakdown

Weights28.7 GB
KV Cache2.0 GB
Runtime0.9 GB
Headroom12.8 GB

See how fast it feels

See how fast it feelsMixtral 8x7B on Intel Data Center GPU Max 1550 128GB
1st promptCold start — includes initialization
>Solve: If a train travels at 60 mph for 2.5 hours, how far does it go?
Let me work through this step by step. Given information: - Speed = 60 mph (miles per hour) - Time = 2.5 hours Formula: Distance = Speed x Time Calculation: Distance = 60 mph x 2.5 h = 150 miles The train travels 150 miles. Double-check: at 60 mph the train covers 60 miles each hour. In half an hour it covers 30 miles. So in 2.5 hours: 60 + 60 + 30 = 150 miles. Confirmed.
2nd promptWarm — model ready, no init overhead
>What if the train stops for 20 minutes halfway?
The stop doesn't change the total distance covered while moving, but it affects total trip time. Breakdown: - First half: 1.25 hours at 60 mph = 75 miles - Stop: 20 minutes (no distance) - Second half: 1.25 hours at 60 mph = 75 miles Total distance: 150 miles (same) Total time: 2.5 h + 0.33 h = 2.83 hours Average speed: 150 / 2.83 = 53 mph The distance stays the same but average speed drops to 53 mph because of the stop.
Estimated: 145.0 tok/s decode · 1.3s TTFT (warm) · 363 tok/s prefill

What limits this setup

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.

Best improvement path

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.

Performance by workload

WorkloadGradeFitDecodeTTFTContext
ChatBRuns well145.0 tok/s728 ms33K
CodingBRuns well145.0 tok/s1335 ms33K
Agentic CodingBRuns well145.0 tok/s1942 ms33K
ReasoningBRuns well145.0 tok/s1578 ms33K
RAGBRuns well134.9 tok/s2609 ms33K

Quantization options

How Mixtral 8x7B (47B params) fits at each quantization level on Intel Data Center GPU Max 1550 128GB (128.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
18.3 GB
LowC54
Q3_K_S
3
23.0 GB
LowC55
NVFP4
4
26.3 GB
MediumB55
Q4_K_M
4
28.7 GB
MediumB56
Q5_K_M
5
33.8 GB
HighB57
Q6_K
6
38.5 GB
HighB57
Q8_0
8
50.3 GB
Very HighB59
F16Best for your GPU
16
96.4 GB
MaximumB63

Get started

Copy-paste commands to run Mixtral 8x7B on your machine.

Run

ollama run mixtral

Frequently asked questions

Can Intel Data Center GPU Max 1550 128GB run Mixtral 8x7B?

Yes, Intel Data Center GPU Max 1550 128GB can run Mixtral 8x7B with a B grade (Runs well). Expected decode speed: 145.0 tok/s.

How much VRAM does Mixtral 8x7B need?

Mixtral 8x7B (47B parameters) requires approximately 44.3 GB of memory with Q4_K_M quantization.

What is the best quantization for Mixtral 8x7B?

The recommended quantization for Mixtral 8x7B is Q4_K_M, which balances quality and memory efficiency.

What speed will Mixtral 8x7B run at on Intel Data Center GPU Max 1550 128GB?

On Intel Data Center GPU Max 1550 128GB, Mixtral 8x7B achieves approximately 145.0 tokens per second decode speed with a time-to-first-token of 1335ms using Q4_K_M quantization.

Can Intel Data Center GPU Max 1550 128GB run Mixtral 8x7B for coding?

For coding workloads, Mixtral 8x7B on Intel Data Center GPU Max 1550 128GB receives a B grade with 145.0 tok/s and 33K context.

What context window can Mixtral 8x7B use on Intel Data Center GPU Max 1550 128GB?

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.

What should I upgrade first if Mixtral 8x7B feels slow on Intel Data Center GPU Max 1550 128GB?

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.

Would CUDA be a better path than Intel Data Center GPU Max 1550 128GB for Mixtral 8x7B?

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.

See all results for Intel Data Center GPU Max 1550 128GBSee all hardware for Mixtral 8x7B
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