Will It Run AI

Can internlm2 math plus 20b i1 run on Intel Arc Pro B60 24GB?

YES — Runs Great

C52Usable
Estimated from fit model

internlm2 math plus 20b i1 needs ~17.8 GB VRAM. Intel Arc Pro B60 24GB has 24.0 GB. With Q4_K_M quantization, expect ~20 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: 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) — 17.8 GB, 20.2 tok/s, Runs well
17.8 GB required24.0 GB available
74% VRAM used

Fit status

Runs well

Decode

20.2 tok/s

TTFT

9592 ms

Safe context

58K

Memory

17.8 GB / 24.0 GB

Memory breakdown

Weights12.2 GB
KV Cache2.3 GB
Runtime0.9 GB
Headroom2.4 GB

See how fast it feels

See how fast it feelsinternlm2 math plus 20b i1 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: 20.2 tok/s decode · 9.6s TTFT (warm) · 51 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 well20.2 tok/s5232 ms58K
CodingCRuns well20.2 tok/s9592 ms58K
Agentic CodingCTight fit20.2 tok/s13952 ms58K
ReasoningCRuns well20.2 tok/s11336 ms58K
RAGCTight fit20.2 tok/s17440 ms58K

Quantization options

How internlm2 math plus 20b i1 (20B params) fits at each quantization level on Intel Arc Pro B60 24GB (24.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
7.8 GB
LowC47
Q3_K_S
3
9.8 GB
LowC48
NVFP4
4
11.2 GB
MediumC49
Q4_K_M
4
12.2 GB
MediumC50
Q5_K_M
5
14.4 GB
HighC49
Q6_KBest for your GPU
6
16.4 GB
HighC49
Q8_0
8
21.4 GB
Very HighF0
F16
16
41.0 GB
MaximumF0

Get started

Copy-paste commands to run internlm2 math plus 20b i1 on your machine.

Run

lms load hf-mradermacher--internlm2-math-plus-20b-i1-gguf && lms server start

Opciones de mejora

Hardware que ejecuta bien internlm2 math plus 20b i1

NVIDIARTX 5090 32GBOpción económica
32 GB VRAM (+8)1792 GB/s (+1336)
C
Sube la velocidad estimada de decodificación alrededor de un 344%.89.7 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 344%.

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

MacBook Pro M4 Max 36GBMejor relación calidad-precio
36 GB Unified (+12)
C
Sube la velocidad estimada de decodificación alrededor de un 40%.28.3 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 40%.

~$2,499 MSRP

Frequently asked questions

Can Intel Arc Pro B60 24GB run internlm2 math plus 20b i1?

Yes, Intel Arc Pro B60 24GB can run internlm2 math plus 20b i1 with a C grade (Runs well). Expected decode speed: 20.2 tok/s.

How much VRAM does internlm2 math plus 20b i1 need?

internlm2 math plus 20b i1 (20B parameters) requires approximately 17.8 GB of memory with Q4_K_M quantization.

What is the best quantization for internlm2 math plus 20b i1?

The recommended quantization for internlm2 math plus 20b i1 is Q4_K_M, which balances quality and memory efficiency.

What speed will internlm2 math plus 20b i1 run at on Intel Arc Pro B60 24GB?

On Intel Arc Pro B60 24GB, internlm2 math plus 20b i1 achieves approximately 20.2 tokens per second decode speed with a time-to-first-token of 9592ms using Q4_K_M quantization.

Can Intel Arc Pro B60 24GB run internlm2 math plus 20b i1 for coding?

For coding workloads, internlm2 math plus 20b i1 on Intel Arc Pro B60 24GB receives a C grade with 20.2 tok/s and 58K context.

What context window can internlm2 math plus 20b i1 use on Intel Arc Pro B60 24GB?

On Intel Arc Pro B60 24GB, internlm2 math plus 20b i1 can safely use up to 58K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

What should I upgrade first if internlm2 math plus 20b i1 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 internlm2 math plus 20b i1?

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 internlm2 math plus 20b i1
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