Can DeepSeek LLM 7B run on Intel Arc A770 16GB?

YES — Tight Fit

C51Usable

○Estimated from fit model

DeepSeek LLM 7B needs ~14.1 GB VRAM. Intel Arc A770 16GB has 16.0 GB. With Q4_K_M quantization, expect ~59 tok/s.

Runtime: llama.cppCapacity: TightBandwidth: 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) — 14.1 GB, 59.0 tok/s, Tight fit

14.1 GB required16.0 GB available

88% VRAM used

Fit status

Tight fit

Decode

59.0 tok/s

TTFT

3280 ms

Safe context

Memory

14.1 GB / 16.0 GB

Memory breakdown

Weights4.3 GB

KV Cache7.3 GB

Runtime0.9 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsDeepSeek LLM 7B on Intel Arc A770 16GB
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: 59.0 tok/s decode · 3.3s TTFT (warm) · 148 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	59.0 tok/s	1789 ms	4K
Coding	C	Tight fit	59.0 tok/s	3280 ms	4K
Agentic Coding	F	Too heavy	24.0 tok/s	11755 ms	4K
Reasoning	C	Tight fit	59.0 tok/s	3877 ms	4K
RAG	F	Too heavy	24.0 tok/s	14694 ms	4K

Quantization options

How DeepSeek LLM 7B (7B params) fits at each quantization level on Intel Arc A770 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	C45
Q3_K_S	3	3.4 GB	Low	C46
NVFP4	4

Get started

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

Run

ollama run deepseek-llm

Upgrade options

Hardware that runs DeepSeek LLM 7B well

👁 Intel
Intel Arc Pro B60 24GBBudget pick

24 GB VRAM (+8)

Adds memory headroom for longer context windows and future model growth.57.7 tok/s decode

Adds memory headroom for longer context windows and future model growth.

~$599 MSRP

RX 7900 XT 20GBBest value

20 GB VRAM (+4)800 GB/s (+240)

Raises estimated decode speed by about 66%.98 tok/s decode

Raises estimated decode speed by about 66%.