Best Hardware to Run Local LLMs (2026 Buying Hub)

One interactive stream where token speed is set by memory bandwidth, not raw compute. Capacity is rarely the wall at small or mid model sizes since only one request's KV cache is live.

Recommendation: Pick the highest-bandwidth card that actually holds your chosen quant in VRAM. A used RTX 3090 (24GB) runs 7B to 32B snappily; for a full-residency 70B Q4 you need ~48GB+ or a unified-memory box (Strix Halo, Mac Studio, DGX Spark). A Q4 70B does NOT fit a 32GB 5090.

Latency-sensitive autocomplete plus chained agentic edits over long source-file context. MoE sparsity is the enabling trick: 30B quality at near-3B decode cost.

Recommendation: Qwen3-Coder 30B-A3B (~18.6GB Q4) fits a 24GB to 32GB card with room for code context. An RTX 5090 (32GB) is ideal; a 24GB card or 64GB unified box also works. Reserve KV-cache headroom beyond the weights for repo-scale context.

Memory is driven by KV cache, not weights. A 70B at 8K context is ~20GB of cache; at 128K it is ~40GB with the model's GQA. Long-context prefill is also a compute spike.

Recommendation: Size for KV-cache capacity, not parameter count. A large-VRAM card (RTX PRO 6000 96GB) or large unified memory (DGX Spark 128GB) holds weights plus long-context cache. Use PagedAttention and quantized KV cache to stretch it.

Each concurrent agent holds its own KV cache, so aggregate cache can exceed the weights several times over. This is fundamentally a batching workload.

Recommendation: A single large card (RTX PRO 6000 96GB) handles a small swarm; H100/H200 with tensor parallelism handles large fan-out. A continuous-batching server (vLLM PagedAttention) is mandatory to keep the GPU saturated across uneven agent lifetimes.

The most memory-intensive regime per parameter: optimizer states, gradients, and activations must stay resident. QLoRA's 4-bit base roughly halves VRAM versus LoRA.

Recommendation: QLoRA 7B (~12GB) and 13B (~20GB) fit one 24GB to 32GB card at home. A 70B QLoRA adapter (~88GB) or any full fine-tune needs multi-GPU or datacenter memory. The DGX Spark is a CUDA-native home option for LoRA work.

Hold weights plus a large shared KV-cache pool sized for tens to hundreds of concurrent sequences. Optimize tokens/sec across the whole batch, accepting higher per-request latency.

Recommendation: H100/H200, often multi-GPU tensor-parallel. An RTX PRO 6000 96GB only covers modest deployments. FP8 weights (half the FP16 memory) plus PagedAttention buy more concurrent slots before adding GPUs.

Language weights plus a visual encoder and image or video token expansion. Resolution and image count drive memory more than base parameter count.

Recommendation: Pick the smallest VL variant that meets accuracy: a 2B for OCR or caption on a 4GB to 8GB card, Qwen2.5-VL 7B (~6 to 8GB) for general use on 12GB to 16GB, 32B+ only when reasoning over visuals demands it. Reserve cache headroom for high-res image batches.

MoE models like Qwen3 235B-A22B and DeepSeek-R1 671B fit in memory by total params but decode fast on their small active-param count. Local here means big unified memory or multi-GPU.

Recommendation: A 192GB+ Mac Studio (used high-RAM units), multi-GPU rigs, or RAM-offload with ktransformers/llama.cpp run the 235B class. The 671B class realistically wants 8x H100/H200, or low-bit dynamic quant (~150 to 220GB) on a serious offload rig.

Diffusion models are often the single biggest VRAM consumer. FLUX.1-dev is ~24GB FP16 / ~12GB FP8; video is worse, with original HunyuanVideo 13B needing 60GB minimum, 80GB recommended.

Recommendation: A 24GB to 32GB card (RTX 4090/5090) handles FP8 image and quantized video work. FP16 video at full quality wants 60GB to 80GB (datacenter). FP8/GGUF quantization is the only path to current video models on consumer cards, at a quality and speed cost.