MacBook Pro M4 Max 36GBOpção econômica
36 GB Unified (+12)
C
This setup is broadly balanced for this model.65.9 tok/s decodificação
~$2,499 MSRP
Can DeepSeek LLM 7B run on Intel Arc Pro B60 24GB?
YES — Runs Great
C52Usable
Estimated from fit model
DeepSeek LLM 7B needs ~14.9 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.
Select quantization to explore
Q4_K_M (Medium quality) — 14.9 GB, 57.7 tok/s, Runs well
14.9 GB required24.0 GB available
62% VRAM used
Fit status
Runs well
Decode
57.7 tok/s
TTFT
3357 ms
Safe context
4K
Memory
14.9 GB / 24.0 GB
Memory breakdown
Weights4.3 GB
KV Cache7.3 GB
Runtime0.9 GB
Headroom2.4 GB
See how fast it feels
See how fast it feelsDeepSeek LLM 7B 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: 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 | C | Runs well | 57.7 tok/s | 1831 ms | 4K |
| Coding | C | Runs well | 57.7 tok/s | 3357 ms | 4K |
| Agentic Coding | C | Tight fit | 57.7 tok/s | 4883 ms | 4K |
| Reasoning | C | Runs well | 57.7 tok/s | 3968 ms | 4K |
| RAG | C | Tight fit | 57.7 tok/s | 6104 ms | 4K |
Quantization options
How DeepSeek LLM 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 | C43 |
Q3_K_S | 3 | 3.4 GB | Low | C43 |
NVFP4 | 4 | 3.9 GB | Medium | C43 |
Q4_K_M | 4 | 4.3 GB | Medium | C43 |
Q5_K_M | 5 | 5.0 GB | High | C44 |
Q6_K | 6 | 5.7 GB | High | C44 |
Q8_0 | 8 | 7.5 GB | Very High | C45 |
F16Best for your GPU | 16 | 14.3 GB | Maximum | C48 |
Get started
Copy-paste commands to run DeepSeek LLM 7B on your machine.
Run
ollama run deepseek-llmOpções de upgrade
Hardware que roda bem DeepSeek LLM 7B
Frequently asked questions
Can Intel Arc Pro B60 24GB run DeepSeek LLM 7B?
Yes, Intel Arc Pro B60 24GB can run DeepSeek LLM 7B with a C grade (Runs well). Expected decode speed: 57.7 tok/s.
How much VRAM does DeepSeek LLM 7B need?
DeepSeek LLM 7B (7B parameters) requires approximately 14.9 GB of memory with Q4_K_M quantization.
What is the best quantization for DeepSeek LLM 7B?
The recommended quantization for DeepSeek LLM 7B is Q4_K_M, which balances quality and memory efficiency.
What speed will DeepSeek LLM 7B run at on Intel Arc Pro B60 24GB?
On Intel Arc Pro B60 24GB, DeepSeek LLM 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 DeepSeek LLM 7B for coding?
For coding workloads, DeepSeek LLM 7B on Intel Arc Pro B60 24GB receives a C grade with 57.7 tok/s and 4K context.
What context window can DeepSeek LLM 7B use on Intel Arc Pro B60 24GB?
On Intel Arc Pro B60 24GB, DeepSeek LLM 7B can safely use up to 4K tokens of context. The model's official context limit is 4K, but available memory constrains the safe maximum.
What should I upgrade first if DeepSeek LLM 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 DeepSeek LLM 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.
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