Best GPU for Running Local LLMs in 2026: VRAM Is the Whole Answer
For running language models locally, buy the most VRAM you can afford, then the most memory bandwidth — in that order, and ignore almost everything else. If the model does not fit in the frame buffer you cannot run it properly at any speed; once it fits, generation speed is set by bandwidth rather than by shader count or gaming frame rate. The cheapest card in our data that reaches 16 GB is NVIDIA GeForce RTX 5060 Ti 16 GB at $279, with 448 GB/s behind it.
Short version. 8 GB runs 7–8B models. 12 GB runs them comfortably or a 14B at 4-bit. 16 GB reaches 24B. 24 GB reaches 32B, which is where local output starts feeling close to a hosted service. 70B does not fit on any consumer card.
The trap: two cards can both say 16 GB and differ by half in token rate. RX 7600 XT costs $50 more than RTX 5060 Ti 16 GB and moves 448 GB/s against 288 — 56% less memory bandwidth for more money.
Choosing a GPU for gaming balances several specifications against each other. Choosing one for inference mostly does not. This is the rare buying question with an arithmetic answer rather than a benchmark one, so here is the arithmetic.
How much memory a model needs
Weights dominate, and quantization sets the bytes per parameter:
- 4-bit (Q4_K_M and relatives) — roughly 0.5 GB per billion parameters. The standard choice for local use: quality loss is small and usually not noticeable in conversation.
- 8-bit — roughly 1 GB per billion. Marginal quality gain over 4-bit for double the memory, which in practice means dropping a model class to pay for it.
- fp16 — roughly 2 GB per billion. Rarely worth it for inference on consumer hardware.
Then add the overhead people consistently forget. The KV cache grows with context length and can add several gigabytes on a long conversation, and the runtime plus compute buffers take roughly another 1–2 GB. The figures below include a working allowance for both; leave more headroom if you plan to use very long contexts.
| Model size | 4-bit VRAM | 8-bit VRAM | Cheapest card that fits it at 4-bit | Its bandwidth |
|---|---|---|---|---|
| 7–8B (Llama 3.1 8B, Mistral 7B) | ~5 GB | ~9 GB | Intel Arc A380 — $149 MSRP | 186 GB/s |
| 12–14B (Phi-4, Mistral Nemo) | ~9 GB | ~15 GB | Intel Arc B570 — $219 MSRP | 380 GB/s |
| 24B (Mistral Small) | ~15 GB | ~26 GB | NVIDIA GeForce RTX 5060 Ti 16 GB — $279 | 448 GB/s |
| 32B (Qwen 2.5 32B) | ~20 GB | ~35 GB | AMD Radeon RX 7900 XT — $680 MSRP | 800 GB/s |
| 70B (Llama 3.3 70B) | ~42 GB | ~73 GB | no single consumer card | — |
Performance figures: Tom's Hardware GPU hierarchy — geometric mean of 15 games at 1440p ultra, no upscaling. We do not test hardware ourselves; our methodology documents where every number on this site comes from.
The cliffs that matter
Model sizes are not evenly distributed, so VRAM tiers land on real capability boundaries rather than a smooth curve. Each step up is a step to a different class of model, not a percentage improvement:
- 8 GB — 7–8B models at 4-bit with a short context. Workable for summarising, autocomplete and simple chat. You will meet the ceiling quickly.
- 12 GB — 7–8B comfortably with real context length, or a 12–14B model at 4-bit. The first tier that feels unconstrained day to day.
- 16 GB — 14B comfortably, 24B at 4-bit. Noticeably better at code and multi-step reasoning than the 8B class, and the tier where a general-purpose gaming card stops being a compromise.
- 24 GB — 32B at 4-bit with room for context. The sweet spot: 32B-class models are where local output starts feeling close to a hosted service for most everyday tasks.
- 32 GB and up — 32B at higher precision, or 70B partially offloaded. A single-card 70B at 4-bit needs around 42 GB, which no consumer card provides.
Once it fits, bandwidth sets the speed
Generating a token requires reading the entire weight set from memory. That makes token generation a memory-bandwidth problem, not a compute problem — which is why the ranking below looks nothing like a gaming chart, and why the fastest gaming card in a price bracket is often not the fastest inference card in it.
You can turn that into a rough speed estimate without any benchmark at all. The theoretical ceiling for token generation is bandwidth divided by the size of the model in memory. A 4-bit 8B model occupies about 5 GB, so RTX 5060 Ti 16 GB at 448 GB/s has a ceiling near 90 tokens per second, while RTX 5090 at 1792 GB/s sits near 358. Real runtimes land somewhere around half to two-thirds of that ceiling depending on the backend, the quantization format and how long your context is. It is arithmetic rather than a measurement, and we present it as arithmetic — but it predicts the ordering of cards well, and it explains why the ordering is not the gaming one.
Prompt processing works the other way round. Ingesting a long document is compute-bound, so a card can feel quick at reading a 20,000-token file and only moderate at writing the reply. If your workload is mostly long-context summarisation rather than long-form generation, weight compute higher than this section suggests.
Two cards that both say 16 GB
NVIDIA GeForce RTX 5060 Ti 16 GB and AMD Radeon RX 7600 XT carry the same 16 GB. They will load the same models. On a spec comparison site that filters by capacity they are interchangeable.
They are not. RTX 5060 Ti 16 GB moves 448 GB/s; RX 7600 XT moves 288. On the ceiling arithmetic above that is roughly 90 tokens per second against 58 on the same 8B model — and RX 7600 XT costs $50 more. This is the single most common way to overpay for a local-AI build: capacity is the headline number, so it is the number vendors compete on, and bandwidth is where the cost is quietly removed.
Check bus width, memory type, bandwidth before capacity convinces you. A 128-bit GDDR6 configuration and a 128-bit GDDR7 one can carry identical VRAM and deliver very different token rates.
Bandwidth per dollar, among cards that clear the 24B barrier
Capacity is a gate rather than a scale: below the model size nothing works, above it the extra gigabytes do nothing. So the sensible way to shop is to pick the capacity your target model needs, then optimise bandwidth per dollar within that set. Here is that set for 16 GB and up — the cards that hold a 24B model at 4-bit.
Intel Arc A770 sets the line at 1.70 GB/s per dollar. The cluster far below it is previous-generation flagship stock: cards that were excellent inference hardware in their year and are now asking flagship money for mid-range bandwidth. For gaming, an old flagship at a discount is often a reasonable buy. For inference it is usually not, because the thing that ages worst — bandwidth per dollar — is the exact thing you are buying it for.
Partial offload, and why it disappoints
Runtimes such as llama.cpp and Ollama will split a model between GPU and system RAM when it does not fit. It works, and it is slow in a way worth understanding before you plan around it.
System RAM bandwidth sits roughly an order of magnitude below GPU memory bandwidth, and throughput is set by the slowest portion rather than averaged across the split. Moving 20% of a model to system RAM does not cost 20% of your speed — it can cost most of it, because every generated token has to wait for that 20% to arrive over a far narrower pipe. The practical rule: a smaller model held entirely on the GPU will almost always give a better experience than a larger one spilling to RAM. Choose the model class your card can actually hold, and spend the savings on a card that can hold the next one.
Software maturity is a real specification
CUDA remains the best-supported path: new runtimes, quantization formats and optimisations land there first, and the amount of undocumented troubleshooting you will do is lowest. AMD's ROCm has improved substantially and works well on supported cards, with the caveat that "supported" is a shorter list than "AMD cards". Intel's stack is the least mature of the three, which is worth weighing against its very strong bandwidth-per-dollar numbers above.
This is not a small factor and it does not appear on any chart on this page. If your priority is that a new model works on the first attempt rather than the third, that ordering is worth paying something for — perhaps a tier of capacity, which is a real cost, so decide deliberately rather than by default.
Beyond consumer cards
If 70B at 4-bit is the requirement, no consumer card reaches it and the question becomes a different one: workstation and datacenter accelerators, where 42 GB and above exists but the pricing model does too. Our local LLM GPU rankings include a reference table of those parts — capacity, bandwidth, power and what each one is actually for — alongside the consumer ranking. They are listed rather than ranked, because we have no measured benchmark for them and will not invent one.
Which card should you actually buy
- Cheapest sensible entry point: NVIDIA GeForce RTX 5060 Ti 16 GB at $279. 16 GB at 448 GB/s, best capacity per dollar in our data, and it runs 24B-class models at 4-bit.
- Best bandwidth per dollar at 16 GB or more: Intel Arc A770 at $329 MSRP. 560 GB/s — the fastest tokens per dollar among cards that clear the 24B barrier.
- You want 32B-class models: AMD Radeon RX 7900 XTX at $959 MSRP. The cheapest 24 GB card we track. This is the step that changes what the models can do, not just how fast they do it.
- Maximum single-card capacity and speed: NVIDIA GeForce RTX 5090 at $1999 MSRP. 32 GB at 1792 GB/s. Expensive per gigabyte, and the only consumer part that makes 32B at 8-bit comfortable.
- Dual-purpose gaming and AI build: pick on VRAM here first, then check the gaming ranking second — the reverse order produces a card that games well and cannot hold your model. Our VRAM guide covers the gaming side of the same decision.
Common questions
How much VRAM do I need to run a local LLM?
Multiply the parameter count in billions by 0.5 for a 4-bit model and add 2–4 GB for context and runtime overhead. An 8B model needs about 5 GB, a 32B needs about 20 GB. Everything else on the spec sheet is secondary to clearing that number.
Is more VRAM or more bandwidth better?
Capacity first, always — it is a gate, and below it nothing runs. Once two cards both fit your target model, bandwidth decides which is faster and capacity beyond the requirement does nothing. Buy the cheapest card that clears the gate, then the most bandwidth within that group.
Can I run a 70B model at home?
Not on one consumer card. 70B at 4-bit needs about 42 GB and the largest consumer card here has 32 GB. The realistic routes are multiple GPUs, a workstation card, or accepting partial offload to system RAM and the large slowdown that comes with it.
Does the gaming benchmark tell me anything about LLM speed?
Very little. Gaming performance is compute-weighted; token generation is bandwidth-bound. The clearest example in our data is RTX 5060 Ti 16 GB and RX 7600 XT — same capacity, 56% different bandwidth. Sort by bandwidth, not by frame rate.
Is a local model as good as a hosted one?
On raw quality per dollar, no — a hosted frontier model is still ahead, and it is worth being honest about that before spending on capacity. Local models are chosen for privacy, offline capability, predictable cost and freedom to experiment. Those are good reasons; "cheaper than a subscription" usually is not, once you price the card.