The best GPU for local LLMs in 2026 depends almost entirely on how much model you need to hold in VRAM: an 8-12GB card around $250-$300 runs 7B-8B models at 4-bit quantization, a 16-24GB card in the $400-$800 range handles 13B-34B models comfortably, and 32GB or more (RTX 5090-class or used workstation cards) is what you need for 70B models and long contexts. Within a given VRAM tier, memory bandwidth matters more than raw compute for token generation, so prioritize cards with wide memory buses and fast GDDR7/HBM over higher-core-count alternatives. Buy the most VRAM you can afford, then let bandwidth break the tie.
Why VRAM and Memory Bandwidth Decide Local LLM Performance
Local LLM inference is memory-bound, not compute-bound. Every token the model generates requires reading the entire set of model weights from VRAM once per forward pass, plus the KV cache (key-value cache: the stored attention states for every token already in the context window, which grows linearly with context length and batch size). The math is blunt: a 7B model at 4-bit quantization occupies roughly 4-5GB, a 13B model about 7-8GB, a 34B model around 18-20GB, and a 70B model roughly 38-40GB, before you add any KV cache or context overhead. If the weights plus cache fit in VRAM, tokens stream out at a rate largely set by bandwidth divided by bytes-read-per-token; if they don't fit, the model spills to system RAM over PCIe and throughput collapses, often by an order of magnitude.
Memory bandwidth is the number to watch because token generation speed scales roughly as:
tokens/sec ≈ memory_bandwidth_GB/s / model_size_GB
A card with 1TB/s of bandwidth reading a 5GB quantized model can theoretically produce a few hundred tokens per second; the same card reading a 40GB model drops to roughly 25 tokens/sec. Compute (CUDA cores, tensor cores) matters far more for prompt processing — the prefill phase where the whole prompt is processed in parallel — and for training or fine-tuning, which is why bandwidth-first shopping is specific to inference.
VRAM capacity is the hard constraint, bandwidth is the soft one. A card that fits the model but has modest bandwidth will feel slow but usable; a card that doesn't fit the model at all is unusable regardless of how fast its memory is. This is why the used-market 24GB cards remain popular despite older architectures: capacity wins first, then bandwidth.
Entry-Level GPUs (Under $300): 8-12GB VRAM for 7B Models
This tier is where most people start, and in 2026 the realistic options are 8GB cards (RTX 5060-class, roughly $250-$300 new) and 12GB cards (RTX 5060 Ti 12GB or a used RTX 3060 12GB, roughly $250-$350). For 7B-8B models at 4-bit quantization with modest context (4K-8K tokens), 8GB is workable and 12GB is comfortable. The 12GB cards are the smarter buy: the extra headroom covers the KV cache at longer contexts, leaves room for the desktop compositor and browser, and lets you run 13B-14B models at 4-bit if you accept slower speeds and shorter contexts.
Expect token generation in the tens of tokens per second for 7B models on these cards — fast enough to feel interactive for chat, marginal for agentic loops that make many sequential calls. Prompt processing on long inputs will be noticeably slower than on mid-range cards because compute and cache bandwidth are both limited.
Two practical caveats. First, 8GB cards increasingly force aggressive quantization (4-bit or lower) and tight context windows; if you plan to use long documents or coding assistants with big repositories in context, stretch to 12GB. Second, at this price point a used 12GB card from the previous generation often beats a new 8GB card for LLM work — VRAM capacity and bandwidth age better than feature sets for this workload. Skip anything with 6GB or less; it cannot hold a useful 7B model plus context at reasonable quantization.
Across this tier, the decision rule is simple: buy the cheapest card with at least 12GB of VRAM and the highest bandwidth you can find within budget, and don't pay extra for compute you won't use during inference.
Mid-Range GPUs ($300-$800): 16-24GB VRAM for 13B-34B Models
This is the sweet spot for most local LLM users. At this tier you get 16-24GB of VRAM, enough to run quantized 13B models entirely on the GPU at comfortable context lengths and to run 30B-class models when quantized aggressively (4-bit or lower). New cards in this range typically offer 16GB; the used market is where 24GB cards (older flagship and workstation models) become reachable, but check power connectors, PSU headroom, and driver support before buying used.
Two specs matter here. Memory bandwidth (how fast the GPU moves data from VRAM to compute units) sets your tokens-per-second ceiling during inference, because every generated token requires streaming the model's weights. Compute matters less for single-stream chat but more for prompt processing and batch work. VRAM capacity decides whether the model fits at all; a card that fits a model slowly beats a faster card that spills to system RAM, since system RAM bandwidth is far lower.
Worked example (estimates, stated assumptions): Assume 4-bit quantization at roughly 0.55 bytes per parameter including overhead, and a KV cache of about 0.5GB per 1,000 tokens of context for a mid-size model (KV cache grows with layers, hidden size, and context; treat this as a planning figure). A 13B model needs about 13 * 0.55 ≈ 7.2GB for weights. At 8,000 tokens of context: 8 * 0.5 = 4GB of cache. Total ≈ 11.2GB, plus 1-2GB for the runtime and fragmentation — comfortably inside 16GB. A 34B model: 34 * 0.55 ≈ 18.7GB for weights alone, so it fits in 24GB only at short context; add 4GB of cache and you are at ~22.7GB before overhead, which is tight. Plan on 24GB for 30B-class models, 16GB for 13B-class.
Checklist before buying mid-range:
- Confirm the model you actually want fits at your target quantization and context length.
- Compare memory bandwidth, not just TFLOPS, across candidates.
- Verify PSU wattage and connectors; older 24GB cards can be power-hungry.
- Check that current driver and CUDA (or ROCm) versions support the card.
High-End and Workstation GPUs ($800+): 32-48GB VRAM for 70B Models and Long Contexts
To run a 70B model fully on GPU you need serious capacity. At 4-bit, weights alone are roughly 70 * 0.55 ≈ 38.5GB, before any KV cache or overhead — so 48GB is the realistic floor for comfortable 70B use, and 32GB cards generally require heavier quantization or partial offloading to system RAM (which slows generation substantially). At this tier you are also paying for bandwidth that keeps long-context inference tolerable, since KV cache reads grow with context length and batch size.
Long contexts are the hidden cost. Using the same rough planning figure of 0.5GB per 1,000 tokens (larger models scale this up — verify per model), 32,000 tokens could add ~16GB, pushing a 70B model past 48GB. Techniques like KV cache quantization and paged attention reduce this, but budget headroom.
Multi-GPU is the pragmatic alternative: two 24GB consumer cards can host a 70B model across ~48GB combined. The tradeoffs are inter-GPU communication overhead (tensor parallelism splits layers across cards each token), motherboard and PSU requirements, and software complexity. It often beats one expensive workstation card on price per gigabyte, but expect more tuning.
Rule of thumb:
VRAM_needed ≈ params * bytes_per_param + (context_tokens / 1000) * cache_GB_per_1k + overhead
## Frequently Asked Questions
### How much VRAM do I need to run a 7B or 70B LLM locally?
VRAM is the GPU's dedicated memory, and for inference it must hold the model weights plus the KV-cache (the stored attention keys and values that let the model keep context) plus some working overhead. A rough estimate for weights alone is parameter count times bytes per parameter: 7B at 4-bit quantization is about 4GB, at 8-bit about 7GB, and at 16-bit about 14GB; 70B at 4-bit is roughly 35-40GB, at 8-bit around 70GB. Add 2-4GB of overhead and a KV-cache that grows with context length, and practical targets land near 8-12GB for 7B, 40-48GB for 70B at 4-bit, and 80GB or multi-GPU setups for 70B at higher precision.
### Is memory bandwidth more important than compute for LLM inference?
For single-user inference, yes: token generation is memory-bound, meaning each token requires streaming the entire weight set (or the active experts) through the GPU, so tokens per second scale roughly with memory bandwidth (in GB/s) rather than raw compute. Raw compute (FLOPS) matters more for prompt processing, where many tokens are handled in parallel, and for batched serving. That is why older high-bandwidth workstation cards can beat newer gaming cards with higher FLOPS but narrower memory buses on token-generation speed.
### Can I run large models on multiple consumer GPUs instead of one expensive card?
Yes, and it is often the cheapest route to 70B-class models: two 24GB cards give 48GB of combined VRAM, enough for a 70B model at 4-bit. The tradeoffs are that the model must be split across cards layer by layer, inter-GPU traffic adds latency, and you need enough PCIe lanes plus a large power supply and case space. Multi-GPU also complicates software setup, and not every inference stack scales gracefully, so verify support in your chosen runtime before buying.
### How does KV-cache size affect the GPU I should buy for long-context inference?
The KV-cache grows linearly with context length and batch size, and it competes with model weights for the same VRAM. As a worked estimate, a 7B model with 32 layers and grouped-query attention can consume on the order of a few GB at 32K context, while larger models and full multi-head attention can push tens of GB at 128K. If you plan long contexts, budget 25-50% extra VRAM beyond the weights and prefer cards with headroom; otherwise you will be forced to shrink context or offload to system RAM.
### When is renting cloud GPUs cheaper than buying a local GPU for LLM work?
Renting wins when your usage is intermittent or experimental: occasional fine-tuning runs, bursty prototyping, or trying models that need more VRAM than you own. Buying wins when you run inference daily for many months, need data to stay on-premises, or want no per-hour billing risk. A simple break-even estimate: divide the card's purchase price by the hourly rental rate for an equivalent GPU; if you expect fewer hours of use than that quotient, rent, and remember to add electricity and depreciation on the buy side.