Will It Run AI

Can Mistral Small 24B Instruct 2501 run on Intel Arc Pro B60 24GB?

YES — Tight Fit

C48Usable
Estimated from fit model

Mistral Small 24B Instruct 2501 needs ~20.8 GB VRAM. Intel Arc Pro B60 24GB has 24.0 GB. With Q4_K_M quantization, expect ~17 tok/s.

Runtime: llama.cppCapacity: TightBandwidth: MediumStack: 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) — 20.8 GB, 16.8 tok/s, Tight fit
20.8 GB required24.0 GB available
87% VRAM used

Fit status

Tight fit

Decode

16.8 tok/s

TTFT

11510 ms

Safe context

34K

Memory

20.8 GB / 24.0 GB

Memory breakdown

Weights14.6 GB
KV Cache2.8 GB
Runtime0.9 GB
Headroom2.4 GB

See how fast it feels

See how fast it feelsMistral Small 24B Instruct 2501 on Intel Arc Pro B60 24GB
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: 16.8 tok/s decode · 11.5s TTFT (warm) · 42 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 well16.8 tok/s6278 ms34K
CodingCTight fit16.8 tok/s11510 ms34K
Agentic CodingCRuns with offload16.8 tok/s16742 ms34K
ReasoningCTight fit16.8 tok/s13603 ms34K
RAGCRuns with offload16.8 tok/s20928 ms34K

Quantization options

How Mistral Small 24B Instruct 2501 (24B params) fits at each quantization level on Intel Arc Pro B60 24GB (24.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
9.4 GB
LowC49
Q3_K_S
3
11.8 GB
LowC50
NVFP4
4
13.4 GB
MediumC50
Q4_K_M
4
14.6 GB
MediumC50
Q5_K_MBest for your GPU
5
17.3 GB
HighC50
Q6_K
6
19.7 GB
HighF0
Q8_0
8
25.7 GB
Very HighF0
F16
16
49.2 GB
MaximumF0

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

Opciones de mejora

Hardware que ejecuta bien Mistral Small 24B Instruct 2501

MacBook Pro M4 Pro 64GBOpción económica
64 GB Unified (+40)
C
Sube la velocidad estimada de decodificación alrededor de un 28%.21.5 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 28%.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$1,599 MSRP

NVIDIARTX 5090 32GBMejor relación calidad-precio
32 GB VRAM (+8)1792 GB/s (+1336)
B
Sube la velocidad estimada de decodificación alrededor de un 290%.65.5 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 290%.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

No es solo un salto de hardware. También te deja en un ecosistema de runtimes más limpio para LLMs locales.

~$1,999 MSRP

Frequently asked questions

Can Intel Arc Pro B60 24GB run Mistral Small 24B Instruct 2501?

Yes, Intel Arc Pro B60 24GB can run Mistral Small 24B Instruct 2501 with a C grade (Tight fit). Expected decode speed: 16.8 tok/s.

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

Mistral Small 24B Instruct 2501 (24B parameters) requires approximately 20.8 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 Arc Pro B60 24GB?

On Intel Arc Pro B60 24GB, Mistral Small 24B Instruct 2501 achieves approximately 16.8 tokens per second decode speed with a time-to-first-token of 11510ms using Q4_K_M quantization.

Can Intel Arc Pro B60 24GB run Mistral Small 24B Instruct 2501 for coding?

For coding workloads, Mistral Small 24B Instruct 2501 on Intel Arc Pro B60 24GB receives a C grade with 16.8 tok/s and 34K context.

What context window can Mistral Small 24B Instruct 2501 use on Intel Arc Pro B60 24GB?

On Intel Arc Pro B60 24GB, Mistral Small 24B Instruct 2501 can safely use up to 34K 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 Arc Pro B60 24GB?

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 Arc Pro B60 24GB 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 Arc Pro B60 24GBSee all hardware for Mistral Small 24B Instruct 2501
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