Can MPT-7B-Instruct run on Intel Arc Pro B60 24GB?
YES — Runs Great
MPT-7B-Instruct needs ~15.4 GB VRAM. Intel Arc Pro B60 24GB has 24.0 GB. With Q4_K_M quantization, expect ~58 tok/s.
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.
Select quantization to explore
Fit status
Runs well
Decode
57.7 tok/s
TTFT
3357 ms
Safe context
8K
Memory
15.4 GB / 24.0 GB
Memory breakdown
See how fast it feels
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 | B | Runs well | 57.7 tok/s | 1831 ms | 8K |
| Coding | A | Runs well | 57.7 tok/s | 3357 ms | 8K |
| Agentic Coding | B | Runs with offload | 57.7 tok/s | 4883 ms | 8K |
| Reasoning | A | Runs well | 57.7 tok/s | 3968 ms | 8K |
| RAG | B | Runs with offload | 57.7 tok/s | 6104 ms | 8K |
Quantization options
How MPT-7B-Instruct (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 | B60 |
Q3_K_S | 3 | 3.4 GB | Low | B61 |
NVFP4 | 4 | 3.9 GB | Medium | B61 |
Q4_K_M | 4 | 4.3 GB | Medium | B61 |
Q5_K_M | 5 | 5.0 GB | High | B62 |
Q6_K | 6 | 5.7 GB | High | B62 |
Q8_0 | 8 | 7.5 GB | Very High | B63 |
F16Best for your GPU | 16 | 14.3 GB | Maximum | B66 |
Get started
Copy-paste commands to run MPT-7B-Instruct on your machine.
Run
lms load mpt-7b-instruct && lms server startYour hardware
More models your Intel Arc Pro B60 24GB can run
| Model | Params | Grade | Decode | Capabilities |
|---|---|---|---|---|
| 30.5B | S | 37.2 tok/s | ||
| 27B | S | 16.1 tok/s | ||
| 27B | S | 12.3 tok/s | ||
| 35B | A | 16.6 tok/s | ||
| 30B | S | 38.5 tok/s |
Frequently asked questions
Can Intel Arc Pro B60 24GB run MPT-7B-Instruct?
Yes, Intel Arc Pro B60 24GB can run MPT-7B-Instruct with a A grade (Runs well). Expected decode speed: 57.7 tok/s.
How much VRAM does MPT-7B-Instruct need?
MPT-7B-Instruct (7B parameters) requires approximately 15.4 GB of memory with Q4_K_M quantization.
What is the best quantization for MPT-7B-Instruct?
The recommended quantization for MPT-7B-Instruct is Q4_K_M, which balances quality and memory efficiency.
What speed will MPT-7B-Instruct run at on Intel Arc Pro B60 24GB?
On Intel Arc Pro B60 24GB, MPT-7B-Instruct 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 MPT-7B-Instruct for coding?
For coding workloads, MPT-7B-Instruct on Intel Arc Pro B60 24GB receives a A grade with 57.7 tok/s and 8K context.
What context window can MPT-7B-Instruct use on Intel Arc Pro B60 24GB?
On Intel Arc Pro B60 24GB, MPT-7B-Instruct 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 MPT-7B-Instruct 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 MPT-7B-Instruct?
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.
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