Best GPU for running LLMs locally (2026)

Running an LLM locally is the best privacy/price deal in AI – if you pick the right GPU. Get this wrong and you either buy too much card (wasted money) or too little (your model won't fit). Here's the honest 2026 pick, from budget to enthusiast.

Quick answer

• Best overall: RTX 4090 (24 GB) – still the sweet spot in 2026.
• Best value mid-range: RTX 5070 Ti (16 GB) or RX 9070 XT (16 GB) – fast and fit 14B models with room to spare.
• Best budget entry: RTX 4060 Ti 16 GB – the 16 GB version specifically. It can run 14B Q4 and even small 30B with offload.
• Best Radeon: RX 7900 XTX (24 GB) – most VRAM per rupee on the AMD side.
• Best used bargain: RX 6800M (12 GB) or a used 3090 (24 GB) if the price is right.

What actually matters for local LLMs

Three things, in this order:

1. VRAM is everything. The model plus its KV cache must fit in VRAM or you're offloading to RAM and crawling. This is the hard limit that decides which models you can run at all.
2. Memory bandwidth sets your speed. Generating text (decode) is memory-bound – the GPU reads the whole model on every token. More bandwidth = more tokens per second.
3. Compute matters less. Raw TFLOPS only shines during prompt processing (prefill). For chat, a card with lots of bandwidth beats one with lots of compute.

So the buying rule is simple: buy the most VRAM you can afford, then the most memory bandwidth within that VRAM class.

The 2026 lineup, compared

Estimated decode speeds for a 14B model at Q4_K_M (via llama.cpp). Your results vary with context, engine, and cooling.

GPU             VRAM   Bandwidth   Qwen 7B Q4   Qwen 14B Q4   Qwen 27B Q4   ~Price (INR)
────────────────────────────────────────────────────────────────────────────────────────
RTX 4060       8 GB    272 GB/s    ~40 tok/s    offload only   ✗             ₹30,000
RTX 4060 Ti    16 GB   288 GB/s    ~42 tok/s    ~40 tok/s     offload only  ₹48,000
RX 7600 XT     16 GB   288 GB/s    ~42 tok/s    ~40 tok/s     offload only  ₹38,000
RX 7700 XT     12 GB   432 GB/s    ~55 tok/s    offload only  ✗             ₹40,000
RTX 5070 Ti    16 GB   896 GB/s    ~95 tok/s    ~90 tok/s     offload only  ₹80,000
RX 9070 XT     16 GB   644 GB/s    ~72 tok/s    ~68 tok/s     offload only  ₹65,000
RTX 4090       24 GB  1008 GB/s   ~160 tok/s   ~120 tok/s    ~60 tok/s     ₹1,50,000+
RX 7900 XTX    24 GB   960 GB/s   ~150 tok/s   ~115 tok/s    ~58 tok/s     ₹1,20,000

"Offload only" = won't fit fully in VRAM; parts run on system RAM, which is much slower. Numbers are estimates from the GPU AI Performance Calculator.

Best overall: RTX 4090 (24 GB)

The 4090 is still the benchmark in 2026. 24 GB of VRAM runs 14B Q4 with a huge context, 27B-class models fully on GPU, and it's the fastest consumer card for prompt processing. The RTX 5090 (32 GB) is faster and fits bigger models, but it costs a fortune – for most people the 4090 (or a used one) is the smarter buy.

Check current price on Amazon →

Best value mid-range: RTX 5070 Ti / RX 9070 XT (16 GB)

If you can't justify a 4090, this is the class I'd buy. 16 GB fits 14B Q4_K_M with 16–32K context comfortably and even 27B Q4 with light offload. The RTX 5070 Ti has almost double the bandwidth of a 4060 Ti – that shows up as roughly 2× the tokens per second. On the AMD side the RX 9070 XT is a solid alternative with strong ROCm support.

RTX 5070 Ti on Amazon → · RX 9070 XT on Amazon →

Best budget: RTX 4060 Ti 16 GB

The 16 GB version is the one to buy for AI. Yes, it's slower than the 8 GB model costs to produce – but the 8 GB version can't run 14B Q4 fully on GPU, while the 16 GB version can, at a still-respectable ~40 tok/s. That's the difference between a toy and a usable local assistant. The RX 7600 XT 16 GB is the Radeon equivalent and usually cheaper.

RTX 4060 Ti 16 GB on Amazon → · RX 7600 XT 16 GB on Amazon →

AMD vs NVIDIA for local AI

The old "AMD is hopeless for AI" advice is outdated. ROCm works well with llama.cpp, Ollama, and LM Studio for RDNA2/RDNA3 cards – see our ROCm installation guide. AMD still wins on VRAM per rupee (the RX 7900 XTX is the cheapest 24 GB card around), while NVIDIA wins on software polish, CUDA libraries, and prefill speed. For a pure local-chat setup, both are fine.

What about the used market?

• RX 6800M (12 GB) – a laptop chip that appears on cheap used boards; excellent price/VRAM for 7B–14B work. We've covered it before – it's a great first local-AI card.
• Used RTX 3090 (24 GB) – if you find one near ₹60–70k, it's effectively a budget 4090 for AI.
• Avoid 8 GB cards for anything above 7B – you'll fight offload constantly.

Search RX 6800M on Amazon →

Don't forget the rest of the build

VRAM is the star, but a slow CPU or too-little system RAM becomes the bottleneck when you offload. Aim for 16–32 GB system RAM and a decent NVMe for loading model files. Check your whole build with the PSU calculator before you buy that 4090.

FAQ

Can I run a 70B model on consumer hardware?

Not really on GPU. 70B at Q4 needs ~40–45 GB – that's cloud GPU territory. A 4090 can run it with heavy offload, but it's slow enough to be unusable for chat.

Is 8 GB of VRAM enough?

For 7B Q4 models, yes – barely. For anything bigger, no. That's why every budget pick on this page is 16 GB.

Does NVIDIA CUDA matter that much?

For prefill speed and exotic libraries, yes. For everyday llama.cpp/Ollama chat, the gap has narrowed a lot – Radeon is fine.

What about Apple Silicon?

Unified memory Macs (M-series with 32–64 GB) are genuinely great for local LLMs – that's a separate article. On the desktop side, this page is about NVIDIA/AMD GPUs.

Bottom line

Buy the most VRAM you can afford, in this order: 24 GB (4090/7900 XTX) → 16 GB (5070 Ti / 9070 XT / 4060 Ti) → 12 GB used (6800M) → anything else is a compromise. Then plug your exact model and context into the calculators below and buy with confidence.