Intel Arc B570 10GBBudget pick10 GB VRAM (+2)
C
Adds memory headroom for longer context windows and future model growth.42 tok/s decode
Adds memory headroom for longer context windows and future model growth.
~$219 MSRP
Dolphin3.0 Llama3.1 8B needs ~7.5 GB VRAM. Intel Arc A750 8GB has 8.0 GB. With Q4_K_M quantization, expect ~45 tok/s.
Operating mode
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
Fit status
Tight fit
Decode
45.1 tok/s
TTFT
4291 ms
Safe context
24K
Memory
7.5 GB / 8.0 GB
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.
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.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Tight fit | 45.1 tok/s | 2341 ms | 24K |
| Coding | C | Tight fit | 45.1 tok/s | 4291 ms | 24K |
| Agentic Coding | C | Runs with offload (needs ~0.3 GB host RAM) | 30.1 tok/s | 9350 ms | 24K |
| Reasoning | C | Tight fit | 45.1 tok/s | 5072 ms | 24K |
| RAG | C | Runs with offload (needs ~0.3 GB host RAM) | 30.1 tok/s | 11688 ms |
How Dolphin3.0 Llama3.1 8B (8B params) fits at each quantization level on Intel Arc A750 8GB (8.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.1 GB | Low | C54 |
Q3_K_S | 3 | 3.9 GB | Low | C53 |
NVFP4 | 4 |
Copy-paste commands to run Dolphin3.0 Llama3.1 8B on your machine.
Run
lms load hf-bartowski--dolphin3-0-llama3-1-8b-gguf && lms server startUpgrade options
Intel Arc B570 10GBBudget pickAdds memory headroom for longer context windows and future model growth.
~$219 MSRP
Intel Arc B580 12GBBest valueAdds memory headroom for longer context windows and future model growth.
~$249 MSRP
Intel Arc Pro A60 12GBIntel upgradeAdds memory headroom for longer context windows and future model growth.
~$499 MSRP
Yes, Intel Arc A750 8GB can run Dolphin3.0 Llama3.1 8B with a C grade (Tight fit). Expected decode speed: 45.1 tok/s.
Dolphin3.0 Llama3.1 8B (8B parameters) requires approximately 7.5 GB of memory with Q4_K_M quantization.
The recommended quantization for Dolphin3.0 Llama3.1 8B is Q4_K_M, which balances quality and memory efficiency.
On Intel Arc A750 8GB, Dolphin3.0 Llama3.1 8B achieves approximately 45.1 tokens per second decode speed with a time-to-first-token of 4291ms using Q4_K_M quantization.
For coding workloads, Dolphin3.0 Llama3.1 8B on Intel Arc A750 8GB receives a C grade with 45.1 tok/s and 24K context.
On Intel Arc A750 8GB, Dolphin3.0 Llama3.1 8B can safely use up to 24K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/hf-bartowski--dolphin3-0-llama3-1-8b-gguf-on-arc-a750-8gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| 24K |
4.5 GB |
| Medium |
| C53 |
Q4_K_MBest for your GPU | 4 | 4.9 GB | Medium | C53 |
Q5_K_M | 5 | 5.8 GB | High | F0 |
Q6_K | 6 | 6.6 GB | High | F0 |
Q8_0 | 8 | 8.6 GB | Very High | F0 |
F16 | 16 | 16.4 GB | Maximum | F0 |
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.
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.