Will It Run AI

Can cognitivecomputations Dolphin3.0 R1 Mistral 24B run on Intel Data Center GPU Max 1550 128GB?

YES — Runs Great

C47Usable
Estimated from fit model

cognitivecomputations Dolphin3.0 R1 Mistral 24B needs ~31.2 GB VRAM. Intel Data Center GPU Max 1550 128GB has 128.0 GB. With Q4_K_M quantization, expect ~138 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) — 31.2 GB, 137.7 tok/s, Runs well
31.2 GB required128.0 GB available
24% VRAM used

Fit status

Runs well

Decode

137.7 tok/s

TTFT

1406 ms

Safe context

567K

Memory

31.2 GB / 128.0 GB

Memory breakdown

Weights14.6 GB
KV Cache2.8 GB
Runtime0.9 GB
Headroom12.8 GB

See how fast it feels

See how fast it feelscognitivecomputations Dolphin3.0 R1 Mistral 24B 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: 137.7 tok/s decode · 1.4s TTFT (warm) · 344 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
ChatCRuns well137.7 tok/s767 ms567K
CodingCRuns well137.7 tok/s1406 ms567K
Agentic CodingCRuns well137.7 tok/s2045 ms567K
ReasoningCRuns well137.7 tok/s1662 ms567K
RAGCRuns well137.7 tok/s2556 ms567K

Quantization options

How cognitivecomputations Dolphin3.0 R1 Mistral 24B (24B params) fits at each quantization level on Intel Data Center GPU Max 1550 128GB (128.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
9.4 GB
LowD38
Q3_K_S
3
11.8 GB
LowD38
NVFP4
4
13.4 GB
MediumD39
Q4_K_M
4
14.6 GB
MediumD39
Q5_K_M
5
17.3 GB
HighD39
Q6_K
6
19.7 GB
HighD39
Q8_0
8
25.7 GB
Very HighD40
F16Best for your GPU
16
49.2 GB
MaximumC44

Get started

Copy-paste commands to run cognitivecomputations Dolphin3.0 R1 Mistral 24B on your machine.

Run

lms load hf-bartowski--cognitivecomputations-dolphin3-0-r1-mistral-24b-gguf && lms server start

Frequently asked questions

Can Intel Data Center GPU Max 1550 128GB run cognitivecomputations Dolphin3.0 R1 Mistral 24B?

Yes, Intel Data Center GPU Max 1550 128GB can run cognitivecomputations Dolphin3.0 R1 Mistral 24B with a C grade (Runs well). Expected decode speed: 137.7 tok/s.

How much VRAM does cognitivecomputations Dolphin3.0 R1 Mistral 24B need?

cognitivecomputations Dolphin3.0 R1 Mistral 24B (24B parameters) requires approximately 31.2 GB of memory with Q4_K_M quantization.

What is the best quantization for cognitivecomputations Dolphin3.0 R1 Mistral 24B?

The recommended quantization for cognitivecomputations Dolphin3.0 R1 Mistral 24B is Q4_K_M, which balances quality and memory efficiency.

What speed will cognitivecomputations Dolphin3.0 R1 Mistral 24B run at on Intel Data Center GPU Max 1550 128GB?

On Intel Data Center GPU Max 1550 128GB, cognitivecomputations Dolphin3.0 R1 Mistral 24B achieves approximately 137.7 tokens per second decode speed with a time-to-first-token of 1406ms using Q4_K_M quantization.

Can Intel Data Center GPU Max 1550 128GB run cognitivecomputations Dolphin3.0 R1 Mistral 24B for coding?

For coding workloads, cognitivecomputations Dolphin3.0 R1 Mistral 24B on Intel Data Center GPU Max 1550 128GB receives a C grade with 137.7 tok/s and 567K context.

What context window can cognitivecomputations Dolphin3.0 R1 Mistral 24B use on Intel Data Center GPU Max 1550 128GB?

On Intel Data Center GPU Max 1550 128GB, cognitivecomputations Dolphin3.0 R1 Mistral 24B can safely use up to 567K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

What should I upgrade first if cognitivecomputations Dolphin3.0 R1 Mistral 24B 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 cognitivecomputations Dolphin3.0 R1 Mistral 24B?

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 cognitivecomputations Dolphin3.0 R1 Mistral 24B
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