Can InternLM 7B run on Intel Arc Pro B60 24GB?

YES — Runs Great

A75Great

Estimated from fit model

InternLM 7B needs ~15.4 GB VRAM. Intel Arc Pro B60 24GB has 24.0 GB. With Q4_K_M quantization, expect ~58 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: MediumStack: StandardBottleneck: Balanced

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) — 15.4 GB, 57.7 tok/s, Runs well

15.4 GB required24.0 GB available

64% VRAM used

Fit status

Runs well

Decode

57.7 tok/s

TTFT

3357 ms

Safe context

Memory

15.4 GB / 24.0 GB

Memory breakdown

Weights4.3 GB

KV Cache7.8 GB

Runtime0.9 GB

Headroom2.4 GB

See how fast it feels

See how fast it feelsInternLM 7B on Intel Arc Pro B60 24GB
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: 57.7 tok/s decode · 3.4s TTFT (warm) · 144 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

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Runs well	57.7 tok/s	1831 ms	8K
Coding	A	Runs well	57.7 tok/s	3357 ms	8K
Agentic Coding	A	Runs with offload	57.7 tok/s	4883 ms	8K
Reasoning	A	Runs well	57.7 tok/s	3968 ms	8K
RAG	A	Runs with offload	57.7 tok/s	6104 ms	8K

Quantization options

How InternLM 7B (7B params) fits at each quantization level on Intel Arc Pro B60 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	B65
Q3_K_S	3	3.4 GB	Low	B65
NVFP4	4	3.9 GB	Medium	B66
Q4_K_M	4	4.3 GB	Medium	B66
Q5_K_M	5	5.0 GB	High	B66
Q6_K	6	5.7 GB	High	B66
Q8_0	8	7.5 GB	Very High	B68
F16Best for your GPU	16	14.3 GB	Maximum	A71

Get started

Copy-paste commands to run InternLM 7B on your machine.

Run

docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
  --hf-repo "InternLM/InternLM-7B" \
  --hf-file "InternLM-7B-Q4_K_M.gguf" \
  -c 4096 -ngl 99

Your hardware

More models your Intel Arc Pro B60 24GB can run

Model	Params	Grade	Decode
Qwen3-Coder 30B A3B Instruct	30.5B	S	37.2 tok/s
Qwen 3.5 27B	27B	S	16.1 tok/s
Qwen 3.6 27B	27B	S	12.3 tok/s
Qwen 3.6 35B A3B	35B	A	16.6 tok/s
Qwen3-VL 30B A3B Instruct	30B	S	38.5 tok/s

Frequently asked questions

Can Intel Arc Pro B60 24GB run InternLM 7B?

Yes, Intel Arc Pro B60 24GB can run InternLM 7B with a A grade (Runs well). Expected decode speed: 57.7 tok/s.

How much VRAM does InternLM 7B need?

InternLM 7B (7B parameters) requires approximately 15.4 GB of memory with Q4_K_M quantization.

What is the best quantization for InternLM 7B?

The recommended quantization for InternLM 7B is Q4_K_M, which balances quality and memory efficiency.

What speed will InternLM 7B run at on Intel Arc Pro B60 24GB?

On Intel Arc Pro B60 24GB, InternLM 7B achieves approximately 57.7 tokens per second decode speed with a time-to-first-token of 3357ms using Q4_K_M quantization.

Can Intel Arc Pro B60 24GB run InternLM 7B for coding?

For coding workloads, InternLM 7B on Intel Arc Pro B60 24GB receives a A grade with 57.7 tok/s and 8K context.

What context window can InternLM 7B use on Intel Arc Pro B60 24GB?

On Intel Arc Pro B60 24GB, InternLM 7B can safely use up to 8K tokens of context. The model's official context limit is 8K, but available memory constrains the safe maximum.

What should I upgrade first if InternLM 7B 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 InternLM 7B?

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 24GB See all hardware for InternLM 7B

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