Best PC to Run LLMs Locally in Singapore: 7B to 550B Ladder

The best PC for running a large language model locally is the one whose memory holds the model you actually want, with enough bandwidth to generate tokens at a speed you can live with; most "AI PC" marketing leads with NPU TOPS, which is the wrong number. This guide is a ladder: for each model size, from a 7B assistant to a 550B frontier-class checkpoint, it names the machine SourceIT sells in Singapore that fits it, from the ASUS NUC 16 Pro and Ascent QN10, through the ROG NUC with an RTX 5080 and the Ascent GX10, to the ExpertCenter Pro ET900N G3, with the Mac Studio as the Apple alternative, and explains the memory and bandwidth arithmetic that lets you check any machine yourself.

Memory sets the ceiling, not TOPS

A language model is a set of weights that must sit in memory the processor can reach quickly: VRAM on a discrete GPU, or unified memory on a chip like NVIDIA's GB10 or Apple's M-series. How much memory it needs depends on parameter count and the precision (quantisation) you run at: about 2 GB per billion parameters at 16-bit, 1 GB at 8-bit, and 0.5 to 0.6 GB at 4-bit. Add the KV cache (which grows with context length and simultaneous users) plus headroom for the OS and runtime; a sensible planning rule is weights plus 20 to 30 percent.

The figures are arithmetic from parameter count and precision, not benchmarks; the SourceIT AI hardware calculator runs the same sums for any parameter count, quantisation and context length. Quantising to 4-bit costs a small amount of quality and is how a 70B model fits in 128 GB.

Bandwidth sets tokens per second

Once the model fits, memory bandwidth decides how fast it answers. Every generated token requires reading the active weights once, so the single-stream ceiling is bandwidth divided by model size: a 40 GB model on a 273 GB/s system cannot exceed roughly seven tokens per second, and real results sit below that. Batching several users recovers throughput. Compute (TOPS or FLOPS) matters for prompt processing and multi-user serving, which is where the ET900N G3 pulls away.

7B to 14B on Windows: NUC 16 Pro X9 or Ascent QN10

For a coding assistant, document summariser or retrieval chatbot on a normal Windows desktop, a 7B to 14B model at 4-bit is the sweet spot and two ASUS mini PCs run it without a discrete GPU. The NUC 16 Pro on the Core Ultra X7 358H or X9 388H uses the Arc B390 integrated GPU with twelve Xe3 cores and soldered LPDDR5X-9600; the 32 GB X9 at S$3,390 covers 7B to 14B, and the 64 GB X9 at S$4,420 adds headroom for a 30B-class model at 4-bit. The Ascent QN10 at S$2,375 takes the NPU route: Qualcomm's 80 TOPS Hexagon NPU runs models compiled through Qualcomm AI Hub (175-plus available) at very low power, the better choice for an always-on assistant or a fleet of edge devices, but its 32 GB is soldered and it is not a CUDA machine. See QN10 vs GX10 for where the NPU approach stops.

Up to about 13B on CUDA: ROG NUC with RTX 5080

Developers who need the real CUDA stack on Windows (PyTorch, TensorRT, ComfyUI, Ollama with GPU offload) and whose models fit in 16 GB should look at the ROG NUC. The laptop-class GeForce RTX 5080 carries 16 GB of GDDR7 at 896 GB/s, more than three times the GX10's bandwidth, so a 7B to 13B model at FP8 or 4-bit generates tokens faster on it than on anything else in this guide short of the ET900N G3. If the model fits in VRAM, a discrete GeForce is quicker; if it does not, it does not run at all. The ROG NUC 15 RTX 5080 32 GB / 1 TB at S$4,835 is the lowest-cost RTX 5080 configuration; the ROG NUC 16 RTX 5080 at S$5,745.80 adds the newer Core Ultra 9 290HX Plus. Skip the 12 GB and 8 GB tiers for LLM work; our ROG NUC 16 vs 15 guide covers the configurations.

30B to 200B: Ascent GX10, the 70B daily driver

The step from 14B to 70B is the one no Windows mini PC and no 16 GB GPU can take, and it is the step most teams want, because 70B-class models are where local quality catches up with cloud services for everyday work. The ASUS Ascent GX10 is the entry point: one NVIDIA GB10 Grace Blackwell Superchip, 128 GB of unified memory the GPU addresses directly, 1 PFLOP of FP4 compute, DGX OS with CUDA, vLLM and NVIDIA NIM preinstalled, and a 240 W USB-C adapter. It runs 7B to 32B models at chat-grade speed, Llama 3.3 70B and DeepSeek-R1 70B quantised as a daily driver, and GPT-OSS 120B at FP4, with a single-unit ceiling of about 200B parameters; it also fine-tunes (LoRA, QLoRA for 70B-class) and runs sandboxed agents through NVIDIA NemoClaw. Pricing: 1 TB at S$7,200, 2 TB at S$8,050 and 4 TB at S$9,850; compute is identical across the three. Our GB10 buyer's guide compares it with the DGX Spark and other GB10 systems.

405B: two GX10s. 400B to 550B in one box: ET900N G3

For Llama 3.1 405B-class or Qwen3-235B-class models, two GX10s linked over ConnectX-7 pool 256 GB, with NVIDIA Sync handling the cluster and four units (512 GB) possible through a switch. Two 1 TB units plus the ASUS QSFP cable (S$120) is the cheapest path on the store to a 405B model, with the trade-off that clustered inference is slower than one coherent memory pool. When the model is 400B to 550B parameters, or a whole team will hit the endpoint at once, the ExpertCenter Pro ET900N G3 is the single-box answer: NVIDIA's GB300 Grace Blackwell Ultra with 252 GB of HBM3e at 7.1 TB/s plus 496 GB of CPU memory over NVLink-C2C, 748 GB coherent in total. In StorageReview's testing it served a 433 GB GLM-5.2 checkpoint at 139 tokens per second where four RTX PRO 6000 cards managed about 40. It is a 27 kg liquid-cooled tower with a 1,600 W supply that needs its own circuit, priced on request.

The Apple alternative: Mac Studio and MLX

If your team lives on macOS, the Mac Studio is the credible non-NVIDIA option, because Apple's unified memory plays the same role as the GX10's. SourceIT lists the Mac Studio M3 Ultra with 96 GB and 1 TB at S$5,745, which fits 70B models at 4-bit and runs them through MLX, llama.cpp or Ollama at 819 GB/s, three times the GX10's bandwidth; the M4 Max 36 GB at S$2,870 is a 14B to 30B machine. The trade-offs are capacity (96 GB versus 128 GB, so no 120B-class models at 4-bit), no CUDA, and no clustering; Apple's current range has moved to M5-series chips, so confirm availability of the M3 Ultra unit when you request a quote.

Buying a local LLM PC in Singapore

SourceIT is an authorised ASUS reseller and stocks every ASUS machine in this ladder (the tiers are explained in our ASUS Agent Computer guide) with official Singapore warranty, and also lists the NVIDIA DGX Spark, the Mac Studio and the other GB10 systems. We size hardware to the model you name and commission GX10 and ET900N G3 units so they arrive updated, networked and serving your chosen models, with prompts kept on-premises for PDPA-sensitive data. Education and NVIDIA Inception pricing may apply on the GX10; ask us. Use Request for Quotation on any product page, call (65) 6978 3502, or email Sales@sourceit.com.sg; government and institutional buyers can procure through GeBIZ and Vendors@Gov.

Frequently asked questions

How much memory do I need to run a 70B model locally? About 38 to 42 GB for the weights at 4-bit plus KV cache and headroom, so a 64 GB machine is tight and 96 to 128 GB is comfortable. On the store that means the Ascent GX10 or the Mac Studio M3 Ultra.

Is NPU TOPS the right number to compare? No. Memory capacity decides whether a model runs at all and bandwidth decides how fast; TOPS matter for prompt processing and multi-user serving.

Can an ASUS NUC run Llama or DeepSeek? Yes, the NUC 16 Pro X7 358H and X9 388H run 7B to 14B models at usable speed through Ollama or LM Studio. Larger models need the Ascent GX10.

What is the cheapest machine for DeepSeek-R1 70B? The Ascent GX10 128 GB / 1 TB at S$7,200 is the lowest-cost NVIDIA system on the store that loads a 70B model at a quality quantisation; the Mac Studio M3 Ultra 96 GB at S$5,745 is the macOS alternative.

Does the ROG NUC run LLMs? Models up to about 13B parameters at FP8 or 4-bit fit in the RTX 5080's 16 GB and run quickly with CUDA. It cannot load a 70B model.

What does quantisation do? It stores weights at lower precision (8-bit or 4-bit instead of 16-bit), cutting memory to a half or a quarter with a small quality cost. It is how 70B models fit on a 128 GB desktop.

Can I run a 405B model at home or in the office? Yes, on two Ascent GX10s linked over ConnectX-7 (256 GB pooled) or on one ET900N G3.