Would CUDA be a better path than Intel Arc Pro A40 6GB for Baichuan 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.

Can Baichuan 7B run on Intel Arc Pro A40 6GB?

Q: What should I upgrade first if Baichuan 7B feels slow on Intel Arc Pro A40 6GB?

Add more VRAM headroom. The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

NO — Won't Fit

F0Won't run

Estimated from fit model

Baichuan 7B needs ~13.6 GB but Intel Arc Pro A40 6GB only has 6.0 GB. Try a smaller quantization or lighter model.

Runtime: llama.cppCapacity: No fitBandwidth: Very lowStack: StandardBottleneck: Memory capacity

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) — 13.6 GB, exceeds 6.0 GB available

13.6 GB required6.0 GB available

227% VRAM needed

7.6 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

3.3 tok/s

TTFT

58579 ms

Safe context

Memory

13.6 GB / 6.0 GB

Offload

60%

Memory breakdown

Weights4.3 GB

KV Cache7.8 GB

Runtime0.9 GB

Headroom0.6 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsBaichuan 7B on Intel Arc Pro A40 6GB
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: 3.3 tok/s decode · 58.6s TTFT (warm) · 8 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 13.6 GB, but this setup only exposes 6.0 GB of usable VRAM.

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

Add more VRAM headroom

The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

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	F	Too heavy	6.0 tok/s	17476 ms	4K
Coding	F	Too heavy	3.3 tok/s	58579 ms	4K
Agentic Coding	F	Too heavy	3.3 tok/s	85206 ms	4K
Reasoning	F	Too heavy	3.3 tok/s	69230 ms	4K
RAG	F	Too heavy	3.3 tok/s	106508 ms	4K

Inference speed

Baichuan 7B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Baichuan 7B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~98 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
RTX 5090 32GB	32 GB	Q4_K_M	98.0	Fits
RTX 4090 24GB	24 GB	Q4_K_M	98.0	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	98.0

Quantization options

How Baichuan 7B (7B params) fits at each quantization level on Intel Arc Pro A40 6GB (6.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	A70
Q3_K_SBest for your GPU	3	3.4 GB	Low	B70

Upgrade options

Hardware that runs Baichuan 7B well

Intel Arc B580 12GBBest value

12 GB VRAM (+6)456 GB/s (+264)

Makes the model fit on the accelerator instead of staying completely out of reach.27.9 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Raises estimated decode speed by about 745%.

~$249 MSRP

Intel Arc A770 16GBBudget pick

16 GB VRAM (+10)560 GB/s (+368)

Makes the model fit on the accelerator instead of staying completely out of reach.59 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$349 MSRP

Intel Arc Pro B50 16GBIntel upgrade

16 GB VRAM (+10)224 GB/s (+32)

Makes the model fit on the accelerator instead of staying completely out of reach.28.3 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$399 MSRP

Frequently asked questions

Can Intel Arc Pro A40 6GB run Baichuan 7B?

No, Baichuan 7B requires more memory than Intel Arc Pro A40 6GB provides.

How much VRAM does Baichuan 7B need?

Baichuan 7B (7B parameters) requires approximately 13.6 GB of memory with Q4_K_M quantization.

What is the best quantization for Baichuan 7B?

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

What speed will Baichuan 7B run at on Intel Arc Pro A40 6GB?

On Intel Arc Pro A40 6GB, Baichuan 7B achieves approximately 3.3 tokens per second decode speed with a time-to-first-token of 58579ms using Q4_K_M quantization.

Can Intel Arc Pro A40 6GB run Baichuan 7B for coding?

For coding workloads, Baichuan 7B on Intel Arc Pro A40 6GB receives a F grade with 3.3 tok/s and 4K context.

What context window can Baichuan 7B use on Intel Arc Pro A40 6GB?

On Intel Arc Pro A40 6GB, Baichuan 7B can safely use up to 4K tokens of context. The model's official context limit is 8K, but available memory constrains the safe maximum.

What should I upgrade first if Baichuan 7B feels slow on Intel Arc Pro A40 6GB?

See all results for Intel Arc Pro A40 6GB See all hardware for Baichuan 7B

Embed this result▼

Paste this snippet into any page to show a live fit card.

<iframe src="https://willitrunai.com/embed/baichuan-7b-on-arc-pro-a40-6gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: