8 GB VRAM (+2)512 GB/s (+326)
B
Raises estimated decode speed by about 284%.74.5 tok/s decode
Raises estimated decode speed by about 284%.
Adds memory headroom for longer context windows and future model growth.
~$179 MSRP
Can Yi 1.5 6B run on Intel Arc A380 6GB?
YES — With Offload
C48Usable
○Estimated from fit model
Yi 1.5 6B needs ~6.1 GB VRAM. Intel Arc A380 6GB has 6.0 GB. With Q4_K_M quantization, expect ~18 tok/s.
Runtime: llama.cppCapacity: OffloadBandwidth: Very lowStack: StandardBottleneck: Host offload
Share:
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) — 6.1 GB, 19.4 tok/s, Runs with offload (needs ~0.1 GB host RAM)
6.1 GB required6.0 GB available
102% VRAM needed
100 MB over capacity — needs offload or smaller quantization
Fit status
Runs with offload (needs ~0.1 GB host RAM)
Decode
19.4 tok/s
TTFT
9994 ms
Safe context
4K
Memory
6.1 GB / 6.0 GB
Memory breakdown
Weights3.7 GB
KV Cache1.0 GB
Runtime0.9 GB
Headroom0.6 GB
See how fast it feels
See how fast it feelsYi 1.5 6B on Intel Arc A380 6GB
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: 19.4 tok/s decode · 10.0s TTFT (warm) · 48 tok/s prefill
What limits this setup
The raw memory story may look fine, but the software ecosystem is still a constraint here.
Very little memory headroom
You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.
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.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Tight fit | 24.9 tok/s | 4241 ms | 4K |
| Coding | C | Runs with offload | 17.8 tok/s | 10869 ms | 4K |
| Agentic Coding | D | Very compromised | 13.1 tok/s | 21574 ms | 4K |
| Reasoning | C | Runs with offload | 17.8 tok/s | 12845 ms | 4K |
| RAG | D | Very compromised | 13.1 tok/s | 26967 ms | 4K |
Quantization options
How Yi 1.5 6B (6B params) fits at each quantization level on Intel Arc A380 6GB (6.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.3 GB | Low | C54 |
Q3_K_S | 3 | 2.9 GB | Low | C54 |
NVFP4Best for your GPU |
Get started
Copy-paste commands to run Yi 1.5 6B on your machine.
Run
lms load Yi-1.5-6B-Chat && lms server startUpgrade options
Hardware that runs Yi 1.5 6B well
10 GB VRAM (+4)380 GB/s (+194)
C
Raises estimated decode speed by about 214%.61 tok/s decode
Raises estimated decode speed by about 214%.
Adds memory headroom for longer context windows and future model growth.
~$219 MSRP
12 GB VRAM (+6)456 GB/s (+270)
C
Raises estimated decode speed by about 235%.65 tok/s decode
Raises estimated decode speed by about 235%.
Adds memory headroom for longer context windows and future model growth.
~$249 MSRP