Nemotron Mini 4B needs ~6.5 GB VRAM. Intel Arc A730M 12GB has 12.0 GB. With Q4_K_M quantization, expect ~56 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
56.0 tok/s
TTFT
3457 ms
Safe context
4K
Memory
6.5 GB / 12.0 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 | 56.0 tok/s | 1886 ms | 4K |
| Coding | C | Runs well | 56.0 tok/s | 3457 ms | 4K |
| Agentic Coding | B | Runs well | 56.0 tok/s | 5029 ms | 4K |
| Reasoning | C | Runs well | 56.0 tok/s | 4086 ms | 4K |
| RAG | B | Runs well | 56.0 tok/s | 6286 ms | 4K |
Inference speed
Estimated decode speed (tokens/sec) for Nemotron Mini 4B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~76 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 | 76.0 | Fits |
| RTX 4090 24GB | 24 GB | Q4_K_M | 64.0 | Fits |
| RTX 4080 Super 16GB | 16 GB | Q4_K_M | 64.0 | Fits |
| RTX 4060 8GB | 8 GB | Q4_K_M | 64.0 | Fits |
| RTX 3090 24GB | 24 GB | Q4_K_M | 56.0 | Fits |
| RTX 4070 12GB | 12 GB | Q4_K_M | 56.0 | Fits |
| RTX 3060 12GB | 12 GB | Q4_K_M | 56.0 | Fits |
RX 7900 XTX 24GB | 24 GB | Q4_K_M | 56.0 | Fits |
MacBook Pro M4 Max 128GB | 128 GB | Q4_K_M | 56.0 | Fits |
Mac Studio M3 Ultra 256GB | 256 GB | Q4_K_M | 56.0 | Fits |
Mac Studio M2 Ultra 128GB | 128 GB | Q4_K_M | 56.0 | Fits |
Mac Studio M1 Ultra 128GB | 128 GB | Q4_K_M | 56.0 | Fits |
MacBook Pro M4 Max 64GB | 64 GB | Q4_K_M | 56.0 | Fits |
MacBook Pro M3 Max 64GB | 64 GB | Q4_K_M | 56.0 | Fits |
MacBook Pro M1 Max 64GB | 64 GB | Q4_K_M | 56.0 | Fits |
MacBook Pro M4 Pro 48GB | 48 GB | Q4_K_M | 56.0 | Fits |
Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.
How Nemotron Mini 4B (4B params) fits at each quantization level on Intel Arc A730M 12GB (12.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 1.6 GB | Low | C48 |
Q3_K_S | 3 | 2.0 GB | Low | C49 |
NVFP4 | 4 | 2.2 GB | Medium | C49 |
Q4_K_M | 4 | 2.4 GB | Medium | C49 |
Q5_K_M | 5 | 2.9 GB | High | C50 |
Q6_K | 6 | 3.3 GB | High | C50 |
Q8_0 | 8 | 4.3 GB | Very High | C52 |
F16Best for your GPU | 16 | 8.2 GB | Maximum | C52 |
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 99Yes, Intel Arc A730M 12GB can run Nemotron Mini 4B with a C grade (Runs well). Expected decode speed: 56.0 tok/s.
Nemotron Mini 4B (4B parameters) requires approximately 6.5 GB of memory with Q4_K_M quantization.
The recommended quantization for Nemotron Mini 4B is Q4_K_M, which balances quality and memory efficiency.
On Intel Arc A730M 12GB, Nemotron Mini 4B achieves approximately 56.0 tokens per second decode speed with a time-to-first-token of 3457ms using Q4_K_M quantization.
For coding workloads, Nemotron Mini 4B on Intel Arc A730M 12GB receives a C grade with 56.0 tok/s and 4K context.
On Intel Arc A730M 12GB, Nemotron Mini 4B can safely use up to 4K tokens of context. The model's official context limit is 4K, but available memory constrains the safe maximum.
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.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/nemotron-mini-4b-on-arc-a730m-12gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
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