Local Inference for Very Large Models: Hardware Options

Overview: Why local inference for very large models?

Running very large language models locally is increasingly attractive for teams and developers who need low latency, strong data privacy, or predictable cost. This guide explains current hardware options for local inference of very large models (including MoE model hardware) and walks through trade-offs between consumer GPUs, enterprise cards, Apple Silicon, and cloud hybrid designs.

Key challenges for local inference

VRAM limits: Large models need lots of memory — often tens to hundreds of gigabytes depending on precision and architecture.
Memory bandwidth: Throughput matters. High bandwidth reduces bottlenecks when moving tensors on and off the GPU.
Power & cost: Enterprise hardware is expensive and power hungry; consumer GPUs are cheaper but limited in capacity.
Software ecosystem: Most LLM inference tooling is optimized for NVIDIA CUDA; alternatives exist but have narrower support.

GPU options

Enterprise-grade GPUs

Enterprise GPUs are designed for heavy ML workloads and are the go-to for large-scale local inference when budget permits.

NVIDIA A100

Variants: 40GB or 80GB HBM2 VRAM.
Pros: Massive memory bandwidth, strong compute, Multi-Instance GPU (MIG) on some SKUs for partitioning.
Best for: 30B+ parameter models, multi-GPU clusters, training and inference of dense and MoE models.

NVIDIA RTX 6000 / RTX PRO 6000 Blackwell

Example: 48GB or 96GB VRAM options.
Pros: Very large VRAM for single-node inference, professional drivers and ECC memory on some models.
Best for: Teams needing large single-GPU capacity for inference without a multi-node setup.

Consumer-grade GPUs

Consumer cards offer the best price/performance for many local inference tasks when combined with quantization or model parallelism.

NVIDIA RTX 4090 and RTX 3090

VRAM: Typically 24GB for 3090/4090 (varies by model and memory configuration).
Pros: Excellent FP32/FP16 throughput for the price; great for development and smaller production tasks.
Limitations: Generally suitable for models up to ~13B parameters in float16 without heavy model slicing or quantization; larger models require quantization, offloading, or multi-GPU strategies.

Memory and performance insights

Picking the right GPU requires balancing memory, bandwidth, and software compatibility. Consider both raw VRAM and memory architecture when sizing for your model.

Minimal baseline: 8GB per GPU is the low end — useful only for tiny models or serving heavily quantized weights.
Practical range: 12–32GB is common on high-end consumer cards and allows inference for medium-sized LLMs when combined with 4/8-bit quantization.
Large-scale single-GPU: 48–96GB (enterprise cards) may be necessary for unsharded inference of very large dense models or some MoE deployments.
Bandwidth matters: HBM2/3 on data-center GPUs provides higher throughput than GDDR6(X), which can significantly affect latency for attention-heavy workloads.

Scalability: multi-GPU and distributed inference

The practical path to support very large models locally is often horizontal scaling across GPUs or nodes.

Tensor/model parallelism: Splits model weights across devices. Works well but adds communication overhead.
Pipeline parallelism: Stages model layers across GPUs, useful for very deep models.
Sharding and offloading: Techniques like CPU offload, NVMe offload, or host memory paging let you run larger models at the cost of latency.
Distributed inference clusters: Multiple A100/MI100 GPUs connected via NVLink/NVSwitch or RDMA provide the best performance for enterprise local inference.

Alternative hardware options

Cloud solutions (hybrid approach)

APIs and cloud VMs remain the most flexible option for very large models. Use cloud for occasional heavy runs and local infra for routine private inference to control costs.

Pros: Instant access to the latest GPUs, pay-per-use cost model, no capital expenditure.
Cons: Ongoing cost, data transfer considerations, potential latency and privacy trade-offs.

Apple Silicon (M-series)

Apple M1/M2/M3 family and Ultra chips use a unified memory architecture that can be attractive for some inference tasks.

Pros: Competitive energy efficiency, good for medium-sized models and on-device privacy-focused inference.
Cons: Software support is improving but not as mature as CUDA-based tooling. Memory is shared (e.g., 48–150GB in high-end configurations), but raw GPU ML throughput and ecosystem compatibility differ from NVIDIA offerings.

Software compatibility and ecosystem

Most production LLM frameworks are optimized for NVIDIA CUDA and related libraries (cuDNN, TensorRT). That means NVIDIA-first toolchains are the fastest path to optimized inference for many models.

NVIDIA-first: Best framework support and fastest path to optimized inference for most models.
Quantization tools: Libraries like bitsandbytes, ONNX Runtime, and Hugging Face Accelerate help reduce VRAM pressure for consumer GPUs.
Apple toolchain: Core ML and LLModel-style ports exist but may require model conversion and have limitations for MoE and custom ops.

Practical recommendations by user profile

Individual / student: Single RTX 4090 is an excellent choice for development and local inference of quantized models up to ~13B parameters. Combine with 4-bit quantization and tools like bitsandbytes for the best ROI.
Startup / research group: Use a mix of local consumer GPUs for experimentation and cloud for large-scale runs. Consider a small multi-GPU workstation (2–4x RTX 4090) with NVMe offload for cost-effective scaling.
Enterprise: Invest in A100 or Blackwell-class GPUs in an on-prem cluster if privacy, latency, or long-running workloads justify capital expense. Use NVLink/InfiniBand for fast inter-GPU communication when supporting MoE or huge dense models.

Cost and power trade-offs

Compare capital cost, per-hour cloud costs, and power consumption before committing to a platform. Workload shape heavily influences the optimal choice.

Consumer GPUs: Lower upfront cost, lower power per card, but may need multiple cards and workarounds for big models.
Enterprise GPUs: Higher upfront and running costs, better density and performance, simplified large-model support.
Cloud: No capital expense but higher long-term operational spending for heavy usage.

Decision checklist: choose hardware for local inference

Estimate model size in memory (consider parameter count and precision).
Decide acceptable latency and throughput targets.
Check software stack compatibility (CUDA vs. Apple tools).
Evaluate cost: upfront vs ongoing (cloud) and power implications.
Plan for scaling: multi-GPU, offload strategies, or cloud hybrid.

Quick comparative table

Option	Typical VRAM	Best for	Pros	Cons
RTX 4090 (consumer)	24GB	Dev, quantized models ~13B	Great price/perf	Limited for very large models
NVIDIA A100	40–80GB	Large models, clusters	High bandwidth, MIG	Expensive, power-hungry
RTX PRO / Blackwell 96GB	48–96GB	Single-node large-model inference	Large VRAM	Costly
Apple M-series	48–150GB unified	On-device inference, privacy	Energy efficient	Software compatibility limits

Example: running a quantized model locally

Many readers will use quantization and bitsandbytes to run bigger models on consumer GPUs. Example command skeleton:

python -m my_inference_tool \
  --model "path/to/quantized-model" \
  --device cuda:0 \
  --dtype int8 \
  --batch_size 1

This demonstrates a typical invocation pattern when combining quantized weights and a CUDA-backed runtime.

Future outlook

Expect three converging trends: wider use of quantization and MoE to reduce inference cost, continued dominance of NVIDIA in mainstream ML tooling, and growing niche use of energy-efficient unified-memory chips (like Apple M-series) for private on-device inference.

Hybrid cloud+edge architectures will also become more common as teams mix local inference for privacy-sensitive tasks with cloud bursts for occasional heavy throughput.

Final recommendations

Start with a clear model memory and latency target before choosing hardware.
For most individuals and small teams, a single high-end consumer GPU (RTX 4090) plus quantization is the best value for local inference of medium-sized LLMs.
For large-scale or very large models (30B+), prefer enterprise GPUs (A100, 80GB-class cards) or a cloud-first approach.
Keep software compatibility and long-term scaling in mind — investing in an NVIDIA-centric stack yields the broadest support today.

Community call and next steps

If you're planning a build, sketch out your model sizes, latency targets, and monthly inference volume. Share that in community forums or issue trackers for specific tuning tips.

Need a tailored recommendation? Provide model size, expected concurrency, and budget and the community or your infra lead can help map a specific hardware plan.