Can Baichuan 7B run on Intel Arc A770 16GB?

YES — Tight Fit

B69Good

○Estimated from fit model

Baichuan 7B needs ~14.6 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) — 14.6 GB, 59.0 tok/s, Tight fit

14.6 GB required16.0 GB available

91% VRAM used

Fit status

Tight fit

Decode

59.0 tok/s

TTFT

3280 ms

Safe context

Memory

14.6 GB / 16.0 GB

Memory breakdown

Weights4.3 GB

KV Cache7.8 GB

Runtime0.9 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsBaichuan 7B on Intel Arc A770 16GB
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: 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	A	Runs well	59.0 tok/s	1789 ms	8K
Coding	B	Tight fit	59.0 tok/s	3280 ms	8K
Agentic Coding	F	Too heavy	21.8 tok/s	12912 ms	8K
Reasoning	B	Tight fit	59.0 tok/s	3877 ms	8K
RAG	F	Too heavy	21.8 tok/s	16140 ms	8K

Quantization options

How Baichuan 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	B63
Q3_K_S	3	3.4 GB	Low	B63
NVFP4	4	3.9 GB	Medium	B64
Q4_K_M	4	4.3 GB	Medium	B64
Q5_K_M	5	5.0 GB	High	B65
Q6_K	6	5.7 GB	High	B66
Q8_0Best for your GPU	8	7.5 GB	Very High	B67
F16	16	14.3 GB	Maximum	F0

Get started

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

Run

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

Upgrade options

Hardware that runs Baichuan 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%.