8 GB VRAM (+2)512 GB/s (+326)
B
Raises estimated decode speed by about 39%.56 tok/s decode
Raises estimated decode speed by about 39%.
Adds memory headroom for longer context windows and future model growth.
~$179 MSRP
Can Nemotron Mini 4B run on Intel Arc A380 6GB?
YES — With Offload
C52Usable
○Estimated from fit model
Nemotron Mini 4B needs ~5.9 GB VRAM. Intel Arc A380 6GB has 6.0 GB. With Q4_K_M quantization, expect ~40 tok/s.
Runtime: llama.cppCapacity: OffloadBandwidth: Very lowStack: StandardBottleneck: Memory bandwidth
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) — 5.9 GB, 40.2 tok/s, Runs with offload
5.9 GB required6.0 GB available
98% VRAM used
Fit status
Runs with offload
Decode
40.2 tok/s
TTFT
4821 ms
Safe context
4K
Memory
5.9 GB / 6.0 GB
Memory breakdown
Weights2.4 GB
KV Cache2.0 GB
Runtime0.9 GB
Headroom0.6 GB
See how fast it feels
See how fast it feelsNemotron Mini 4B 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: 40.2 tok/s decode · 4.8s TTFT (warm) · 100 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 | Runs well | 40.2 tok/s | 2630 ms | 4K |
| Coding | C | Runs with offload | 40.2 tok/s | 4821 ms | 4K |
| Agentic Coding | F | Too heavy | 17.1 tok/s | 16447 ms | 4K |
| Reasoning | C | Runs with offload | 40.2 tok/s | 5698 ms | 4K |
| RAG | F | Too heavy | 17.1 tok/s | 20559 ms | 4K |
Quantization options
How Nemotron Mini 4B (4B params) fits at each quantization level on Intel Arc A380 6GB (6.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 1.6 GB | Low | C55 |
Q3_K_S | 3 | 2.0 GB | Low | B55 |
NVFP4 | 4 |
Get started
Copy-paste commands to run Nemotron Mini 4B on your machine.
Run
docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
--hf-repo "nvidia/Nemotron-Mini-4B-Instruct" \
--hf-file "Nemotron-Mini-4B-Instruct-Q4_K_M.gguf" \
-c 4096 -ngl 99Upgrade options
Hardware that runs Nemotron Mini 4B well
10 GB VRAM (+4)380 GB/s (+194)
C
Raises estimated decode speed by about 39%.56 tok/s decode
Raises estimated decode speed by about 39%.
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 39%.56 tok/s decode
Raises estimated decode speed by about 39%.
Adds memory headroom for longer context windows and future model growth.
~$249 MSRP