Buying GuideGPUUpdated September 9, 2026

Best GPU for Local LLMs in 2026

These choices use memory specifications, software requirements and source-attributed records. We have not independently tested every listed GPU. Prices below are undated reference amounts; obtain current quotes and verify the exact model artifact before buying.

Marcus Chen · Senior Hardware Editor Updated 2026-09-09 18 min read Curated from public benchmarks

What's in this guide

  1. 01Top Picks
  2. 02Why VRAM Matters
  3. 03Comparison Table
  4. 04RTX 5090 Deep Dive
  5. 05RTX 4090 Deep Dive
  6. 06RTX 3090 Deep Dive
  7. 07RTX 6000 Ada
  8. 08AMD RX 7900 XTX
  9. 09Buying Considerations
  10. 10FAQ
  11. 11Related Guides

The Top Picks

Five awards covering every budget and use case, from "I want the fastest" to "I want to run 70B for under $1,000".

best overall96

NVIDIA RTX 5090

NVIDIA

32GB GDDR7 and roughly 1.79TB/s memory bandwidth. Consider it for 8B–32B quantized models; a 70B Q4_K_M artifact exceeds 32GB before runtime overhead.

32 GB GDDR7 · 1792 GB/s · 575 W
32GB accommodates many 32B Q4 models; 70B Q4 requires more memory or offload
Almost double the bandwidth of the RTX 4090
FP4 tensor cores accelerate Blackwell-native quants
Needs a real 1000 W+ PSU
Pulls 575 W under sustained load
$1,999street, at time of testing
Check Amazon Price
best value92

NVIDIA RTX 4090 (used)

NVIDIA

24GB supports many smaller quantized models. Neither 13B FP16 nor standard 70B Q3 fits fully with runtime headroom. Obtain a current used-card quote and check condition.

24 GB GDDR6X · 1008 GB/s · 450 W
24GB supports 8B FP16; larger artifacts need separate memory planning
Mature CUDA stack, every backend works on day one
Plentiful aftermarket supply
12VHPWR cable history
450 W TDP
Hard to find new
$1,599street, at time of testing
Check Amazon Price
best budget86

NVIDIA RTX 3090 24GB

NVIDIA

A 24GB CUDA option for smaller quantized models. 70B Q4 needs multiple GPUs or CPU offload; verify the used card, software configuration and current asking price.

24 GB GDDR6X · 936 GB/s · 350 W
Cheapest 24 GB CUDA card on the used market
NVLink-capable for dual-3090 70B builds
Wide community support, Linux drivers solid
350 W and runs hot, case airflow matters
Blower-style cards are loud
Pre-loved units may need a fan re-paste
$799street, at time of testing
Check Amazon Price
premium94

NVIDIA RTX 6000 Ada

NVIDIA

48GB ECC VRAM can hold many 70B Q4 artifacts with limited context headroom. 70B Q8 weights alone are about 70GB and do not fit one card. Check system power and slot compatibility.

48 GB GDDR6 ECC · 960 GB/s · 300 W
48 GB VRAM in 2 slots
ECC memory, important for long training runs
Quiet enough for office use
MSRP is brutal
Lower raw FP16 than RTX 5090
$6,800street, at time of testing
Check Amazon Price See current deals
dark horse78

AMD RX 7900 XTX

AMD

24 GB VRAM at consumer prices, finally usable for LLMs thanks to mature ROCm 6.2+ and the llama.cpp Vulkan backend. Not as fast as Ada, but a real option.

24 GB GDDR6 · 960 GB/s · 355 W
24 GB consumer-priced VRAM
Strong on Vulkan inference backends
Open driver stack on Linux
ROCm still patchier than CUDA on Windows
No NVLink-style multi-GPU pooling
$999street, at time of testing
Check Amazon Price

Why this matters: VRAM is everything

Buying a GPU for local LLMs is less like buying a gaming card and more like buying a small computer. The single specification that determines whether a model will run at all, let alone run quickly , is VRAM capacity. The second is memory bandwidth. Raw FP32 / FP16 tensor compute, the thing review sites obsess over, is in third place at best for token-generation workloads.

Token generation is memory-bound. Every generated token requires reading the entire model's weights from VRAM through the tensor cores. A 70-billion- parameter model at Q4_K_M is ~40 GB of weights. To produce 30 tokens per second, the card must move 30 × 40 GB ≈ 1.2 TB of data through memory every second. That's why an RTX 5090 with 1.79 TB/s of bandwidth dramatically outperforms even the FP16-faster RTX 6000 Ada's 0.96 TB/s, at least until you exceed 32 GB and the 6000 can hold the model while the 5090 is forced to spill to system RAM.

The practical implication: rank GPUs by VRAM first, by bandwidth second, by everything else third. A card with too little VRAM is not "slow at LLMs", it simply cannot run the model you want at the quality you want. Once VRAM is sufficient, the tok/s race begins.

Compare documented workloads

The earlier fixed speed table had no linked per-run artifacts and is withdrawn. Use source-attributed records with matching model, quantization, context, batch and runtime. Missing configurations cannot establish a hardware winner.

Inspect benchmark evidence and settings

Card-by-card deep dive

Honest, opinionated commentary on the cards we actually recommend.

NVIDIA RTX 5090, the new default

The RTX 5090 has 32GB GDDR7 and roughly 1.79TB/s bandwidth. That capacity suits many 8B–32B quantized models. A standard 70B Q4_K_M artifact exceeds 32GB for weights alone; check smaller quantizations against the actual file and cache requirements.

FP4 hardware support does not establish acceleration for every GGUF quantization. Confirm the chosen runtime, artifact and driver support. The earlier speed multipliers and unlinked measurements are withdrawn.

Strengths

  • • 32GB can hold 8B FP16; larger artifacts need a memory budget
  • • 1.79 TB/s bandwidth, the new bar
  • • FP4 kernels via Blackwell tensor cores
  • • Mature CUDA driver stack from day one

Caveats

  • • 575 W TDP, needs a real ATX 3.1 PSU
  • • 12V-2x6 connector once again
  • • Above 78 °C is common under sustained load
  • • Often retails above MSRP
Check RTX 5090 price on Amazon

NVIDIA RTX 4090, the value champion

Two and a half years after launch, the RTX 4090 remains the single best value in 24 GB inference cards. Used-market prices have settled in the $1,200–$1,500 range, with the original $1,599 MSRP still listed at major retailers but rarely paid. For roughly 70% of the price of an RTX 5090 you get 75% of the 8B throughput and identical 24 GB VRAM, which means identical model compatibility for nearly every common workload.

The 4090's weakness is bandwidth, 1.0 TB/s vs the 5090's 1.79 TB/s , which shows up most painfully on large-context workloads. Once you push past 16k context the KV-cache traffic dominates and the 5090 pulls ahead by a wider margin. For everyday chat, coding agents, and 8B–34B workloads, the 4090 remains the reasonable buy.

Strengths

  • • Same 24 GB VRAM as the 5090
  • • Mature CUDA support in every backend
  • • Plentiful used supply at sane prices
  • • Better gaming all-rounder than workstation cards

Caveats

  • • Outclassed by 5090 in raw bandwidth
  • • No FP4 kernels, stuck on Q4_K_M / Q5_K_M
  • • 12VHPWR connector history
Check RTX 4090 price on Amazon

NVIDIA RTX 3090, the budget legend that won't die

The original 24 GB consumer card from late 2020 remains the entry point for serious local-LLM work. Used 3090s have settled to $700–$900, and a pair with NVLink can host Llama 3 70B at Q4_K_M for under $2,000 of GPU spend. It is slower per-card than a 4090 by roughly 1.5×, but in the dollars-per-VRAM-gigabyte race it remains unbeatable.

The NVLink 3.0 bridge, required if you want the pair to share VRAM efficiently for tensor-parallel inference , is itself an artifact of a vanished era; Ada and Blackwell consumer cards dropped NVLink entirely. Bridges are still available from Nvidia partners and used markets. Expect to pay $150–$300 for a working 4-slot SLI HB bridge.

Strengths

  • • Cheapest 24 GB CUDA card
  • • Last consumer card with NVLink
  • • Extensive llama.cpp / exllama community support

Caveats

  • • Used cards may have hot VRAM modules
  • • 350 W and three slots wide
  • • No Ampere-specific FP4 / FP8 optimizations

NVIDIA RTX 6000 Ada, the workstation choice

48GB ECC VRAM can hold many 70B Q4 artifacts with context-dependent headroom. A 70B Q8 artifact is around 70GB before runtime overhead and does not fit one card. Confirm the exact board, slot width and cooling requirements.

The price, $6,000–$7,500 , pushes this card into "Is this for my job?" territory. If the answer is yes, it pays for itself in saved cloud-GPU hours within months. If the answer is no, look at a pair of used RTX 3090s or a Mac Studio with 128 GB unified memory before signing this PO.

AMD RX 7900 XTX, the credible alternative

The RX 7900 XTX has 24GB VRAM. Check the chosen runtime's current ROCm or Vulkan support for your operating system. The earlier fixed ROCm-versus-Vulkan speed claims lacked linked run artifacts and are withdrawn.

The CUDA-only stack remains real. vLLM, TensorRT-LLM, exllama-v2 with the fastest FlashInfer kernels, and the polished bitsandbytes integration are all NVIDIA-locked. If you live exclusively in llama.cpp and Ollama, AMD is a perfectly fine choice. If you wander into the broader research ecosystem, CUDA still wins.

Check RX 7900 XTX price on Amazon

Buying considerations

VRAM math

Model weight footprint at Q4_K_M ≈ N_params × 0.6 bytes. Add roughly 15% for KV cache at 4k context, 30% at 8k, 50% at 16k. A 70B model at Q4 with 8k context wants ~46 GB to be truly comfortable.

Bandwidth matters

Token generation tok/s ≈ VRAM bandwidth / model size. A 40 GB model on a 1.0 TB/s card maxes out at ~25 tok/s. The same model on a 1.79 TB/s card maxes out at ~45 tok/s. Compute is rarely the bottleneck for inference.

Thermals & PSU

A single RTX 5090 needs an ATX 3.1 PSU with a 12V-2x6 cable, 1000 W+ rating, and a case that exhausts 600 W of heat. Dual-GPU rigs need 1500 W PSUs, three exhaust fans, and ideally a dedicated 20 A wall circuit.

PCIe lanes

Single-GPU: any modern board's x16 slot is fine. Dual-GPU: you want a Threadripper, EPYC, or Xeon W board for true x8/x8 PCIe 4.0. Z-series consumer boards split to x8/x4, workable but bandwidth-limited.

Frequently Asked Questions

How much VRAM do I need for Llama 3 70B?

At Q4_K_M quantization Llama 3 70B needs roughly 40 GB of VRAM with a usable 4–8k context window. That means a single 48 GB workstation card (RTX 6000 Ada) or two 24 GB consumer cards (2× RTX 3090 / 2× RTX 4090) in tensor-parallel. Do not assume a 32GB card fits Q3_K_M: inspect the artifact size and allow space for runtime buffers and KV cache.

Is the RTX 5090 worth it for local LLMs?

Yes if you can land one at MSRP. The 32 GB VRAM, 1.79 TB/s bandwidth, and FP4 tensor cores make it the fastest single-card LLM rig you can buy. At scalper prices the answer flips, a used RTX 4090 plus saving the difference is usually smarter.

Can an RTX 3060 12 GB run DeepSeek?

It can run DeepSeek-R1-Distill-7B and DeepSeek-Coder-V2-Lite at Q4 around 25–40 tok/s. Larger distilled variants (14B, 32B) need either heavier quantization or CPU offload and become uncomfortable. For serious DeepSeek-Coder use, plan on 16 GB+ minimum.

Are two RTX 3090s with NVLink still relevant in 2026?

Yes. NVLink boosts tensor-parallel bandwidth on Llama 3 70B inference roughly 30–40% versus naked PCIe. A used pair plus an NVLink bridge is still the cheapest way to host 70B Q4 in 2026.

Should I buy AMD for local LLMs?

If you are on Linux and comfortable with ROCm or Vulkan backends, the RX 7900 XTX 24 GB is a credible alternative for inference. CUDA-only tooling (vLLM with FlashInfer kernels, exllama-v2, TensorRT-LLM) remains friendlier on NVIDIA. We rank AMD as a value pick rather than a default.

Is the RTX 4090 still a good buy in 2026?

On the used market, absolutely, it remains the single best dollar-per-token-per-second card for 24 GB inference. New retail prices are not competitive with the RTX 5090 once you account for the bandwidth and FP4 acceleration.

What is the cheapest way to run a 70B model?

A used pair of RTX 3090s with NVLink in a Ryzen workstation. Total cost at the time of testing was around $1,800–$2,100 depending on parts; check current used pricing. The next step up is a 48 GB workstation card (~$5,000 used RTX A6000), or a Mac Studio M3 Ultra 128 GB at ~$5,000.

Do I need a workstation card or can I use a consumer GPU?

For single-user inference, consumer cards (RTX 4090 / 5090) win on raw tok/s and dollar-per-token. Workstation cards (RTX 6000 Ada, RTX A6000) win when you need ECC memory, 48 GB in one slot, blower-style cooling for multi-GPU, or NVIDIA Studio drivers for production.

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