Llama 3.2 1B Instruct needs ~2.0 GB VRAM. Intel Arc A370M 4GB has 4.0 GB. With Q4_K_M quantization, expect ~14 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
Runs well
Decode
14.0 tok/s
TTFT
13829 ms
Safe context
285K
Memory
2.0 GB / 4.0 GB
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.
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.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 14.0 tok/s | 7543 ms | 167K |
| Coding | C | Runs well | 14.0 tok/s | 13829 ms | 285K |
| Agentic Coding | C | Runs well | 14.0 tok/s | 20114 ms | 285K |
| Reasoning | C | Runs well | 14.0 tok/s | 16343 ms | 285K |
| RAG | C | Runs well | 14.0 tok/s | 25143 ms | 285K |
How Llama 3.2 1B Instruct (1B params) fits at each quantization level on Intel Arc A370M 4GB (4.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 0.4 GB | Low | B55 |
Q3_K_S | 3 | 0.5 GB | Low | B56 |
NVFP4 | 4 |
Copy-paste commands to run Llama 3.2 1B Instruct on your machine.
Run
lms load hf-maziyarpanahi--llama-3-2-1b-instruct-gguf && lms server startYes, Intel Arc A370M 4GB can run Llama 3.2 1B Instruct with a C grade (Runs well). Expected decode speed: 14.0 tok/s.
Llama 3.2 1B Instruct (1B parameters) requires approximately 2.0 GB of memory with Q4_K_M quantization.
The recommended quantization for Llama 3.2 1B Instruct is Q4_K_M, which balances quality and memory efficiency.
On Intel Arc A370M 4GB, Llama 3.2 1B Instruct achieves approximately 14.0 tokens per second decode speed with a time-to-first-token of 13829ms using Q4_K_M quantization.
For coding workloads, Llama 3.2 1B Instruct on Intel Arc A370M 4GB receives a C grade with 14.0 tok/s and 285K context.
On Intel Arc A370M 4GB, Llama 3.2 1B Instruct can safely use up to 285K 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-maziyarpanahi--llama-3-2-1b-instruct-gguf-on-arc-a370m-4gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| Medium |
| B56 |
Q4_K_M | 4 | 0.6 GB | Medium | B56 |
Q5_K_M | 5 | 0.7 GB | High | B56 |
Q6_K | 6 | 0.8 GB | High | B56 |
Q8_0Best for your GPU | 8 | 1.1 GB | Very High | B56 |
F16 | 16 | 2.1 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.