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Best GPUs for Local LLMs in 2026 (By Budget)

Every speed figure below is an AI Hashrate estimate, not a benchmark · prices are MSRP

Two specs decide whether a GPU is good for local LLMs: VRAM capacity (what fits) and memory bandwidth (how fast it generates). Raw compute matters far less for single-user decoding than either. These picks are organized by budget; every tok/s figure is computed with the AI Hashrate formula at 8K context and labeled as an estimate.

Prices shown are manufacturer MSRP at launch. Street prices move constantly — actual Amazon prices apply after you click through. Some older cards below are only realistically available used.

Under $400: the entry ticket

RTX 3060 12GB — $329 MSRP

Still the cheapest sensible way into local LLMs. 12 GB fits 8B-class models at Q4 (7.0 GB needed) with room for long context, and even 13B Q4 (10.8 GB) just barely. Estimated ~29 tok/s on Llama-3.1-8B Q4 (est.) — comfortable chat speed. Its 360 GB/s bandwidth is the ceiling: FP16 on 8B (18.6 GB) does not fit, so plan to live at Q4.

$600–$1,000: the mainstream sweet spot

RTX 5070 Ti 16GB — $749 MSRP

16 GB and 896 GB/s. Fits every 8B-class model at Q4 easily and FP16 8B is close to the line (18.6 GB vs 15.2 GB usable — it misses; keep to Q4 or short context). Estimated ~71 tok/s on 8B Q4 (est.).

RX 7900 XTX 24GB — $999 MSRP

The capacity play: 24 GB at this price fits 8B at FP16 (18.6 GB) and gets closest to 13B FP16. Estimated ~76 tok/s on 8B Q4, ~21 tok/s on 8B FP16 (est.). Software setup on AMD takes more patience than NVIDIA, but the VRAM-per-dollar is excellent.

RTX 5080 16GB — $999 MSRP

Same 16 GB class as the 5070 Ti with faster 960 GB/s memory. Worth it over the Ti only if the price gap is small — for LLMs the identical VRAM is usually the binding constraint.

$1,500–$2,000: the enthusiast tier

RTX 4090 24GB — $1,599 MSRP

The long-reigning local-LLM card: 24 GB, 1,008 GB/s. Runs 8B at FP16 (~22 tok/s est.) and everything up to 13B FP16. Its one famous limit: dense 32B Q4 needs 25.6 GB at 8K context — just over the 22.8 GB usable line — so 30B-class dense models offload unless you shorten context. Widely available used.

RTX 5090 32GB — $1,999 MSRP

The current consumer king. 32 GB finally fits dense 32B Q4 (25.6 GB needed) with headroom, and 1,792 GB/s bandwidth makes it the fastest single consumer card we track: estimated ~143 tok/s on 8B Q4, ~35 tok/s on Qwen3-32B Q4, and ~380 tok/s on the MoE Qwen3-30B-A3B at Q4 (all est.). If one card must do everything short of 70B, this is it.

Workstation and Apple: the 70B club

RTX 6000 Ada 48GB — $6,800 MSRP

48 GB fits 13B FP16 and gets close to 70B Q4 — but not close enough at 8K (53.8 GB needed vs 45.6 GB usable). Buy it for multi-user or professional work, not as a 70B machine.

MacBook Pro M4 Max 128GB — $4,399 MSRP

Unified memory changes the game: 128 GB fits Llama-3.1-70B at Q4 and even gpt-oss-120b at Q4, in a laptop. The trade-off is bandwidth (546 GB/s): estimated ~5 tok/s on 70B Q4 (est.) — usable for batch jobs and patient chat, not interactive speed. For MoE models with small active params it shines: ~43 tok/s on gpt-oss-120b Q4 (est.).

Datacenter tier: when only 80GB+ will do

A100 80GB — $15,000 MSRP · MI300X 192GB — $15,000 MSRP

80 GB is the 70B-Q4 threshold (~19 tok/s est. on A100). The MI300X's 192 GB and 5,300 GB/s are in another league: 70B at FP16 fits (~13 tok/s est.), and MoE giants like Qwen3-235B run at Q4 (~153 tok/s est.). These are server parts — buy only if you know why you need one.

What about buying used?

The used market is where VRAM-per-dollar gets interesting, with two caveats: no warranty, and prices that swing weekly — so we quote MSRP for reference only and you should treat any live listing (Amazon or elsewhere) as the real price.

RTX 3090 24GB — $1,499 launch MSRP, mostly used now

The classic budget-24GB play. Same capacity class as the 4090 with 936 GB/s bandwidth: estimated ~74 tok/s on 8B Q4 (est.), fits 8B FP16, and hits the same 32B-Q4 wall as the 4090. Two of them in one box is a well-trodden path to 48 GB for 70B Q4 offload experiments — though at that point a single M4 Max 128GB is simpler, if slower.

Used datacenter cards (A100 80GB)

Used 80 GB A100s appear well under MSRP, but remember they are SXM/PCIe server parts: power, cooling and chassis requirements make them impractical for most home setups. For almost everyone, consumer cards or Apple silicon are the saner route to big-model capacity.

How should you choose?