Will It Run AI

Can Mistral Small 24B Instruct 2501 run on Intel Data Center GPU Max 1550 128GB?

YES — Runs Great

C47Usable
Estimated from fit model

Mistral Small 24B Instruct 2501 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 feelsMistral Small 24B Instruct 2501 on Intel Data Center GPU Max 1550 128GB
1st promptCold start — includes initialization
>What is local AI inference?
Local AI inference means running an AI language model directly on your own hardware — your laptop, desktop, or server — instead of sending requests to a remote cloud API. When you run inference locally the model weights are loaded into your GPU or unified memory. Each token you generate requires reading those weights from memory, so memory bandwidth is the main bottleneck for decode speed. Key benefits of running locally: - Full privacy: your prompts never leave your machine - No per-token cost or rate limits - Works offline once the model is downloaded - Latency depends only on your hardware
2nd promptWarm — model ready, no init overhead
>How much VRAM do I need?
It depends on the model size and quantization level. A rough rule of thumb: Model size Q4 (4-bit) Q8 (8-bit) FP16 7B params ~4.3 GB ~7.5 GB ~14 GB 13B params ~7.9 GB ~13.9 GB ~26 GB 70B params ~42.7 GB ~74.9 GB ~140 GB Most people use 4-bit quantization (Q4_K_M) which gives 90-95% of full quality at a fraction of the memory. A 24 GB GPU can comfortably run most 7B-13B models.
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 Mistral Small 24B Instruct 2501 (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
LowD39
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 Mistral Small 24B Instruct 2501 on your machine.

Run

lms load hf-maziyarpanahi--mistral-small-24b-instruct-2501-gguf && lms server start

Frequently asked questions

Can Intel Data Center GPU Max 1550 128GB run Mistral Small 24B Instruct 2501?

Yes, Intel Data Center GPU Max 1550 128GB can run Mistral Small 24B Instruct 2501 with a C grade (Runs well). Expected decode speed: 137.7 tok/s.

How much VRAM does Mistral Small 24B Instruct 2501 need?

Mistral Small 24B Instruct 2501 (24B parameters) requires approximately 31.2 GB of memory with Q4_K_M quantization.

What is the best quantization for Mistral Small 24B Instruct 2501?

The recommended quantization for Mistral Small 24B Instruct 2501 is Q4_K_M, which balances quality and memory efficiency.

What speed will Mistral Small 24B Instruct 2501 run at on Intel Data Center GPU Max 1550 128GB?

On Intel Data Center GPU Max 1550 128GB, Mistral Small 24B Instruct 2501 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 Mistral Small 24B Instruct 2501 for coding?

For coding workloads, Mistral Small 24B Instruct 2501 on Intel Data Center GPU Max 1550 128GB receives a C grade with 137.7 tok/s and 567K context.

What context window can Mistral Small 24B Instruct 2501 use on Intel Data Center GPU Max 1550 128GB?

On Intel Data Center GPU Max 1550 128GB, Mistral Small 24B Instruct 2501 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 Mistral Small 24B Instruct 2501 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 Mistral Small 24B Instruct 2501?

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 Mistral Small 24B Instruct 2501
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